diff --git a/.gitignore b/.gitignore
index a4f9074e1..c7f7d6abe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,8 @@
 *.i
 *.out
 *.h5
+# kinetic-profile inputs are tracked despite the blanket *.h5 (output) ignore
+!examples/**/*kinetic*.h5
 .vscode
 *.DS_Store
 *.pdf
@@ -26,3 +28,9 @@ pestotv
 regression-harness/*.sqlite
 regression-harness/*.sqlite-shm
 regression-harness/*.sqlite-wal
+scratch/
+.claude/worktrees/
+.claude/agent-memory/
+
+# Local profiling scratch (one-off study scripts, not part of the package)
+profiling/
diff --git a/Project.toml b/Project.toml
index fd2c512d4..f1067858e 100644
--- a/Project.toml
+++ b/Project.toml
@@ -7,6 +7,7 @@ version = "0.1.0"
 [deps]
 AdaptiveArrayPools = "4f381ef7-9af0-4cbe-99d4-cf36d7b0f233"
 Contour = "d38c429a-6771-53c6-b99e-75d170b6e991"
+DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -36,6 +37,7 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [compat]
 AdaptiveArrayPools = "0.3.5"
 Contour = "0.6.3"
+DelaunayTriangulation = "1.6.6"
 DelimitedFiles = "1.9.1"
 DiffEqCallbacks = "4.9.0"
 Documenter = "1.14.1"
diff --git a/docs/make.jl b/docs/make.jl
index 892920ab2..cbe4f29ed 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -35,7 +35,7 @@ makedocs(;
             "KineticForces" => "kinetic_forces.md",
             "Forcing Terms" => "forcing_terms.md",
             "Perturbed Equilibrium" => "perturbed_equilibrium.md",
-            "Inner Layer" => "inner_layer.md",
+            "Tearing" => "inner_layer.md",
             "Analysis" => "analysis.md",
             "Utilities" => "utilities.md"
        ],
diff --git a/docs/src/inner_layer.md b/docs/src/inner_layer.md
index cc80f52ea..387c06553 100644
--- a/docs/src/inner_layer.md
+++ b/docs/src/inner_layer.md
@@ -1,9 +1,10 @@
-# Inner Layer Module
+# Tearing Module
 
-The InnerLayer module provides abstract scaffolding for resistive inner-layer
-models used in matched asymptotic expansions for resistive MHD stability
-analysis. It currently includes the GGJ (Glasser–Greene–Johnson) shooting
-method for computing the inner-layer response.
+The `Tearing` module groups the resistive tearing-mode analysis stack:
+`InnerLayer` (per-surface inner-layer matching data Δ(Q) for the GGJ and
+SLAYER models), `Dispersion` (physics-agnostic complex-plane scan and
+contour-intersection root extraction), and `Runner` (user-facing TOML
+configuration, profile loading, and HDF5 output).
 
 ## InnerLayer
 
@@ -16,3 +17,21 @@ Modules = [GeneralizedPerturbedEquilibrium.InnerLayer]
 ```@autodocs
 Modules = [GeneralizedPerturbedEquilibrium.InnerLayer.GGJ]
 ```
+
+## SLAYER
+
+```@autodocs
+Modules = [GeneralizedPerturbedEquilibrium.InnerLayer.SLAYER]
+```
+
+## Dispersion
+
+```@autodocs
+Modules = [GeneralizedPerturbedEquilibrium.Dispersion]
+```
+
+## Runner
+
+```@autodocs
+Modules = [GeneralizedPerturbedEquilibrium.Runner]
+```
diff --git a/docs/src/kinetic_forces.md b/docs/src/kinetic_forces.md
index 456e246bd..9787d9698 100644
--- a/docs/src/kinetic_forces.md
+++ b/docs/src/kinetic_forces.md
@@ -4,6 +4,27 @@ Kinetic torque and energy calculations for perturbed equilibria.
 Implements neoclassical toroidal viscosity (NTV) from the PENTRC formulation
 ([Logan & Park, 2013](citations.md); [Logan, 2015](citations.md)).
 
+## Kinetic profile file formats
+
+Kinetic profiles are read by `read_kinetic_file`, which dispatches on the file
+extension:
+
+  - **HDF5 (`.h5`/`.hdf5`)** — the GPEC kinetic schema (recommended). Datasets
+    at the file root: required `psi` (normalized poloidal flux), `n_e`, `T_i`,
+    `T_e`, `omega_E` (and `n_i`, defaulting to `n_e` if omitted); optional
+    `omega_tor`, `chi_e` (perpendicular heat diffusivity ``\chi_\perp``), and
+    `chi_phi` (toroidal momentum diffusivity ``\chi_\phi``). Each dataset
+    carries a `units` attribute; the root carries `schema_version` and
+    `provenance`. Densities are m⁻³, temperatures eV, frequencies rad/s,
+    diffusivities m²/s. Write files with `write_kinetic_h5`.
+  - **ASCII (`.gpeckf`/`.kin`/`.dat`)** — legacy six-column whitespace table
+    `psi_n n_i n_e T_i[eV] T_e[eV] omega_E`, retained for backward
+    compatibility. Header rows are skipped.
+
+The NTV calculation consumes `n_i, n_e, T_i, T_e, omega_E`; `chi_e`/`chi_phi`,
+when present, are carried for the resistive-layer (SLAYER) analysis and ignored
+here.
+
 ## Profile Scaling Knobs
 
 Seven scaling factors are available on `KineticForcesControl` to modify kinetic
diff --git a/examples/DIIID-like_SLAYER_example/gpec.toml b/examples/DIIID-like_SLAYER_example/gpec.toml
new file mode 100644
index 000000000..776d60e12
--- /dev/null
+++ b/examples/DIIID-like_SLAYER_example/gpec.toml
@@ -0,0 +1,97 @@
+# DIII-D-like SLAYER tearing-mode growth-rate example.
+# Reuses the equilibrium and H-mode kinetic profiles of the sibling
+# DIIID-like_ideal_example (geqdsk referenced by relative path; not
+# duplicated). Runs equilibrium + ForceFreeStates + SLAYER and skips
+# PerturbedEquilibrium (ForceFreeStates.force_termination = true).
+
+[Equilibrium]
+eq_filename = "../DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode.geqdsk" # Path to equilibrium file
+eq_type = "efit"               # Type of the input 2D equilibrium file
+jac_type = "hamada"            # Coordinate system (hamada, pest, boozer, equal_arc)
+power_bp = 0                   # Poloidal field power exponent for Jacobian
+power_b = 0                    # Toroidal field power exponent for Jacobian
+power_r = 0                    # Major radius power exponent for Jacobian
+grid_type = "log_asymptotic"   # Radial grid packing type
+psilow = 1e-4                  # Lower limit of normalized flux coordinate
+psihigh = 0.9995               # Upper limit of normalized flux coordinate
+mpsi = 0                       # Number of radial grid points (0 = auto-compute from psi_accuracy)
+psi_accuracy = 0.001           # Target absolute error in q for auto-mpsi
+mtheta = 256                   # Number of poloidal grid points
+newq0 = 0                      # Override for on-axis safety factor (0 = use input value)
+etol = 1e-10                   # Error tolerance for equilibrium solver
+force_termination = false      # Terminate after equilibrium setup (skip stability calculations)
+
+[Wall]
+shape = "nowall"               # Wall shape (nowall, conformal, elliptical, dee, mod_dee, filepath)
+a = 0.2415                     # Distance from plasma (conformal) or shape parameter
+aw = 0.05                      # Half-thickness parameter for Dee-shaped walls
+bw = 1.5                       # Elongation parameter for wall shapes
+cw = 0                         # Offset of wall center from major radius
+dw = 0.5                       # Triangularity parameter for wall shapes
+tw = 0.05                      # Sharpness of wall corners (try 0.05 as initial value)
+equal_arc_wall = true          # Equal arc length distribution of nodes on wall
+
+[ForceFreeStates]
+force_termination = true       # Run FFS + SLAYER, skip PerturbedEquilibrium
+local_stability_flag = true    # Perform local stability analysis (Mercier and ballooning) across the ψ profile
+mat_flag = true                # Construct coefficient matrices for diagnostic purposes
+ode_flag = true                # Integrate ODEs for stability of the internal long-wavelength mode (must be true for GPEC)
+vac_flag = true                # Compute plasma, vacuum, and total energies for free-boundary modes
+
+psiedge = 0.99                 # Edge dW scan band: dW(ψ) computed for ψ ∈ [psiedge, psilim], integration truncated at peak
+qlow = 1.02                    # Integration initiated at q determined by min(q0, qlow)...
+qhigh = 1e3                    # Integration terminated at q limit determined by min(qa, qhigh)...
+sing_start = 0                 # Start integration at the sing_start'th rational from the axis (psilow)
+
+nn_low = 1                     # Smallest toroidal mode number to include
+nn_high = 1                    # Largest toroidal mode number to include
+delta_mlow = 8                 # Expands lower bound of Fourier harmonics
+delta_mhigh = 8                # Expands upper bound of Fourier harmonics
+mthvac = 512                   # Number of points used in splines over poloidal angle at the plasma-vacuum interface
+thmax0 = 1                     # Linear multiplier on the automatic choice of theta integration bounds
+
+kinetic_source = "fixed"       # Kinetic matrix source: "fixed" test matrices, or "calculated" from the kinetic NTV model
+kinetic_factor = 0.0           # Scaling of kinetic matrices (0 = ideal path; >0 enables kinetic mode)
+eulerlagrange_tolerance = 1e-10# Relative tolerance for ODE integration of Euler-Lagrange equations
+save_interval = 3              # Save every Nth ODE step (1=all, 10=every 10th). Always saves near rational surfaces.
+singfac_min = 1e-4             # Fractional distance from rational q at which ideal jump enforced
+ucrit = 1e4                    # Maximum fraction of solutions allowed before re-normalized
+
+# Δ' BVP + parallel integration (see ForceFreeStatesControl docstring for details)
+use_parallel          = true   # Run parallel FM-propagator BVP path (unlocks singular/delta_prime_matrix)
+parallel_threads      = 1      # serial/bit-deterministic BVP — keeps the regression Δ' (and hence γ) reproducible
+populate_dense_xi     = false  # No PerturbedEquilibrium here; the dense EL pass has no consumer (auto-disabled under force_termination anyway). SLAYER needs only delta_prime_matrix from the parallel BVP.
+truncate_at_dW_peak   = false  # Edge-dW scan stays diagnostic; integration domain set by qhigh / psihigh / dmlim
+set_psilim_via_dmlim  = true   # TRUE for diverted geqdsks — q → ∞ at separatrix, so dmlim truncation avoids the δW kink instability at negligible domain cost
+dmlim                 = 0.2    # Truncate integration at (last_rational_q + dmlim) / n
+
+[SLAYER]
+# SLAYER tearing-mode growth-rate analysis on the DIII-D-like H-mode equilibrium.
+# Runs in the force_termination path (PE skipped). Kinetic profiles (incl. the
+# χ⊥/χ_φ heat/momentum diffusivities) are read from the standardized HDF5
+# kinetic file shared with the kinetic/NTV physics. Per-surface (uncoupled)
+# analysis: the headline is the unstable 2/1; the validity gate drops surfaces
+# with no real root (e.g. the 5/1, whose Δ' BVP yields a huge uncancellable Δ').
+enabled                 = true
+inner_model             = "slayer_fitzpatrick"
+scan_mode               = "amr"
+coupling_mode           = "uncoupled"
+dc_type                 = "none"
+mu_i                    = 2.0
+zeff                    = 1.0
+chi_perp                = 1.0       # fallback only; the kinetic file supplies χ⊥(ψ)
+chi_tor                 = 1.0       # fallback only; the kinetic file supplies χ_φ(ψ)
+pole_threshold_adaptive = true       # SLAYER |Δ| spans many orders; adapt to 10·median(|Δ|)
+filter_above_poles      = true
+filter_outside_re       = true
+polish_roots            = true       # refine each root to the true zero; enables the validity gate
+store_scan              = false      # per-surface scans omitted from HDF5 (uncoupled has one per surface)
+# Standardized kinetic-profile file (GPEC HDF5 kinetic schema), shared with the
+# sibling ideal example; carries n_e, T_e, T_i, omega_E, chi_e (χ⊥), chi_phi (χ_φ).
+profile_file = "../DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.h5"
+
+[SLAYER.scan_grid]
+Q_re_range = [-2.0, 2.0]
+Q_im_range = [-0.5, 3.0]
+nre        = 41
+nim        = 31
diff --git a/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode.geqdsk b/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode.geqdsk
index 41a07c2de..fa39228fe 100644
--- a/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode.geqdsk
+++ b/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode.geqdsk
@@ -1,13741 +1,13653 @@
 tMaker:                                            0 257 257
- 0.159500003E+01 0.285999990E+01 0.167955351E+01 0.877499998E+00 0.000000000E+00
- 0.174381757E+01-0.531557994E-02 0.348275840E+00 0.440248810E-01 0.202434754E+01
- 0.120003950E+07 0.348275840E+00 0.000000000E+00 0.174381757E+01 0.000000000E+00
--0.531557994E-02 0.000000000E+00 0.440248810E-01 0.000000000E+00 0.000000000E+00
- 0.349233675E+01 0.349158883E+01 0.349083281E+01 0.349007487E+01 0.348931623E+01
- 0.348855710E+01 0.348779750E+01 0.348703742E+01 0.348627710E+01 0.348551631E+01
- 0.348475528E+01 0.348399401E+01 0.348323274E+01 0.348247099E+01 0.348170924E+01
- 0.348094749E+01 0.348018575E+01 0.347942376E+01 0.347866201E+01 0.347790051E+01
- 0.347713900E+01 0.347637796E+01 0.347561717E+01 0.347485662E+01 0.347409654E+01
- 0.347333717E+01 0.347257829E+01 0.347182012E+01 0.347106266E+01 0.347030592E+01
- 0.346955013E+01 0.346879554E+01 0.346804166E+01 0.346728897E+01 0.346653748E+01
- 0.346578741E+01 0.346503854E+01 0.346429086E+01 0.346354508E+01 0.346280050E+01
- 0.346205783E+01 0.346131682E+01 0.346057749E+01 0.345984030E+01 0.345910478E+01
- 0.345837140E+01 0.345764017E+01 0.345691109E+01 0.345618439E+01 0.345545983E+01
- 0.345473766E+01 0.345401812E+01 0.345330119E+01 0.345258689E+01 0.345187521E+01
- 0.345116639E+01 0.345046043E+01 0.344975734E+01 0.344905734E+01 0.344836020E+01
- 0.344766641E+01 0.344697595E+01 0.344628859E+01 0.344560456E+01 0.344492412E+01
- 0.344424701E+01 0.344357347E+01 0.344290376E+01 0.344223762E+01 0.344157529E+01
- 0.344091654E+01 0.344026208E+01 0.343961120E+01 0.343896461E+01 0.343832183E+01
- 0.343768334E+01 0.343704891E+01 0.343641877E+01 0.343579316E+01 0.343517160E+01
- 0.343455458E+01 0.343394208E+01 0.343333387E+01 0.343273044E+01 0.343213153E+01
- 0.343153715E+01 0.343094778E+01 0.343036294E+01 0.342978287E+01 0.342920780E+01
- 0.342863750E+01 0.342807221E+01 0.342751193E+01 0.342695642E+01 0.342640615E+01
- 0.342586088E+01 0.342532086E+01 0.342478585E+01 0.342425609E+01 0.342373157E+01
- 0.342321205E+01 0.342269802E+01 0.342218924E+01 0.342168593E+01 0.342118788E+01
- 0.342069507E+01 0.342020774E+01 0.341972589E+01 0.341924930E+01 0.341877842E+01
- 0.341831279E+01 0.341785288E+01 0.341739821E+01 0.341694927E+01 0.341650581E+01
- 0.341606784E+01 0.341563535E+01 0.341520858E+01 0.341478705E+01 0.341437149E+01
- 0.341396117E+01 0.341355658E+01 0.341315746E+01 0.341276407E+01 0.341237593E+01
- 0.341199374E+01 0.341161680E+01 0.341124558E+01 0.341087961E+01 0.341051960E+01
- 0.341016483E+01 0.340981555E+01 0.340947175E+01 0.340913343E+01 0.340880084E+01
- 0.340847325E+01 0.340815139E+01 0.340783477E+01 0.340752363E+01 0.340721798E+01
- 0.340691733E+01 0.340662217E+01 0.340633225E+01 0.340604758E+01 0.340576816E+01
- 0.340549397E+01 0.340522504E+01 0.340496111E+01 0.340470219E+01 0.340444827E+01
- 0.340419960E+01 0.340395594E+01 0.340371704E+01 0.340348315E+01 0.340325403E+01
- 0.340302992E+01 0.340281034E+01 0.340259576E+01 0.340238571E+01 0.340218043E+01
- 0.340197968E+01 0.340178370E+01 0.340159202E+01 0.340140486E+01 0.340122223E+01
- 0.340104389E+01 0.340086985E+01 0.340070033E+01 0.340053487E+01 0.340037346E+01
- 0.340021634E+01 0.340006351E+01 0.339991450E+01 0.339976954E+01 0.339962840E+01
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- 0.339773035E+01 0.339784980E+01 0.339798498E+01 0.339812422E+01 0.339827752E+01
- 0.339844704E+01 0.339862895E+01 0.339881778E+01 0.339900637E+01 0.339918804E+01
- 0.339935732E+01 0.339951086E+01 0.339964724E+01 0.339976549E+01 0.339986610E+01
- 0.339994884E+01 0.340000010E+01
- 0.580797500E+05 0.576907344E+05 0.573093711E+05 0.569297500E+05 0.565511211E+05
- 0.561733359E+05 0.557963945E+05 0.554203398E+05 0.550452227E+05 0.546711055E+05
- 0.542980586E+05 0.539261328E+05 0.535553867E+05 0.531858750E+05 0.528176445E+05
- 0.524507422E+05 0.520852148E+05 0.517210977E+05 0.513584297E+05 0.509972500E+05
- 0.506375898E+05 0.502794805E+05 0.499229492E+05 0.495680234E+05 0.492147305E+05
- 0.488630898E+05 0.485131328E+05 0.481648711E+05 0.478183320E+05 0.474735273E+05
- 0.471304805E+05 0.467892031E+05 0.464497148E+05 0.461120273E+05 0.457761523E+05
- 0.454421055E+05 0.451098945E+05 0.447795391E+05 0.444510391E+05 0.441244102E+05
- 0.437996562E+05 0.434767930E+05 0.431558203E+05 0.428367500E+05 0.425195820E+05
- 0.422043320E+05 0.418909961E+05 0.415795820E+05 0.412700938E+05 0.409625352E+05
- 0.406569141E+05 0.403532266E+05 0.400514766E+05 0.397516680E+05 0.394538008E+05
- 0.391578750E+05 0.388638945E+05 0.385718594E+05 0.382817695E+05 0.379936211E+05
- 0.377074180E+05 0.374231602E+05 0.371408438E+05 0.368604648E+05 0.365820273E+05
- 0.363055273E+05 0.360309609E+05 0.357583281E+05 0.354876250E+05 0.352188516E+05
- 0.349519961E+05 0.346870664E+05 0.344240508E+05 0.341629492E+05 0.339037539E+05
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- 0.323884414E+05 0.321424863E+05 0.318984004E+05 0.316561816E+05 0.314158223E+05
- 0.311773164E+05 0.309406602E+05 0.307058457E+05 0.304728672E+05 0.302417188E+05
- 0.300123926E+05 0.297848848E+05 0.295591875E+05 0.293352930E+05 0.291131953E+05
- 0.288928887E+05 0.286743652E+05 0.284576191E+05 0.282426406E+05 0.280294238E+05
- 0.278179609E+05 0.276082461E+05 0.274002695E+05 0.271940273E+05 0.269895098E+05
- 0.267867070E+05 0.265856152E+05 0.263862246E+05 0.261885273E+05 0.259925176E+05
- 0.257981855E+05 0.256055215E+05 0.254145215E+05 0.252251758E+05 0.250374746E+05
- 0.248514121E+05 0.246669805E+05 0.244841699E+05 0.243029707E+05 0.241233789E+05
- 0.239453828E+05 0.237689746E+05 0.235941484E+05 0.234208926E+05 0.232491992E+05
- 0.230790625E+05 0.229104707E+05 0.227434180E+05 0.225778926E+05 0.224138887E+05
- 0.222513984E+05 0.220904102E+05 0.219309160E+05 0.217729082E+05 0.216163789E+05
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diff --git a/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.gpeckf b/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.gpeckf
new file mode 100644
index 000000000..aec26a951
--- /dev/null
+++ b/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.gpeckf
@@ -0,0 +1,515 @@
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diff --git a/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.h5 b/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.h5
new file mode 100644
index 000000000..2f084f080
Binary files /dev/null and b/examples/DIIID-like_ideal_example/TkMkr_D3Dlike_Hmode_kinetic.h5 differ
diff --git a/examples/DIIID-like_ideal_example/gpec.toml b/examples/DIIID-like_ideal_example/gpec.toml
index 6f2ecb634..db73bb377 100644
--- a/examples/DIIID-like_ideal_example/gpec.toml
+++ b/examples/DIIID-like_ideal_example/gpec.toml
@@ -7,11 +7,14 @@
 eq_filename = "TkMkr_D3Dlike_Hmode.geqdsk" # Path to equilibrium file
 eq_type = "efit"               # Type of the input 2D equilibrium file
 jac_type = "hamada"            # Coordinate system (hamada, pest, boozer, equal_arc, park, other)
-grid_type = "log_asymptotic"   # Radial grid packing type
-psilow = 1e-4                  # Lower limit of normalized poloidal flux
-psihigh = 0.9995               # Upper limit of normalized poloidal flux
-mpsi = 0                       # Number of radial grid points (0 = auto-compute from psi_accuracy)
-psi_accuracy = 0.001           # Target absolute error in q for auto-mpsi
+power_bp = 0                   # Poloidal field power exponent for Jacobian
+power_b = 0                    # Toroidal field power exponent for Jacobian
+power_r = 0                    # Major radius power exponent for Jacobian
+grid_type = "ldp"              # Radial grid packing (sin²: packs core+edge). Robust δW/Δ' vs log_asymptotic.
+psilow = 1e-4                  # Lower limit of normalized flux coordinate
+psihigh = 0.995                # captures q=6 with sas_flag/dmlim at this Ip
+mpsi = 256                     # Fixed radial grid (avoid mpsi=0 auto: it edge-starves → spurious δW/Δ')
+psi_accuracy = 0.001           # Target absolute error in q for auto-mpsi (unused when mpsi>0)
 mtheta = 256                   # Number of poloidal grid points
 newq0 = 0                      # Override for on-axis safety factor (0 = use input value)
 etol = 1e-10                   # Error tolerance for equilibrium solver
@@ -77,3 +80,24 @@ compute_singular_coupling = true        # Compute singular layer coupling metric
 verbose = true                          # Enable verbose logging
 write_outputs_to_HDF5 = true            # Write perturbed equilibrium outputs to HDF5
 reg_spot = 0.05                         # Regularization width for singular surfaces (0 = disabled)
+
+[KineticForces]
+kinetic_file = "TkMkr_D3Dlike_Hmode_kinetic.h5"  # GPEC HDF5 kinetic schema (psi,n_i,n_e,T_i,T_e,omega_E,+chi_e,chi_phi). Legacy .gpeckf/.kin still readable.
+nn = 1                                  # Toroidal mode number (matches nn_low/nn_high)
+nl = 6                                  # Bounce harmonics retained
+zi = 1                                  # Main-ion charge (deuterium)
+mi = 2                                  # Main-ion mass [proton masses]
+zimp = 6                                # Impurity charge (carbon)
+mimp = 12                               # Impurity mass [proton masses]
+electron = false                        # Ion-only NTV
+nutype = "harmonic"                     # Collision operator
+f0type = "maxwellian"                   # Equilibrium distribution
+moment = "pressure"                     # Pressure-moment NTV torque
+atol_xlmda = 1e-9                       # Inner pitch/energy integration tolerances
+rtol_xlmda = 1e-5
+atol_psi = 1e-2                         # Outer psi quadrature tolerances (relaxed from 1e-3:
+rtol_psi = 1e-2                         # NTV torque integrand has sharp resonant peaks at rational
+                                        # surfaces; tight tol over-refines. Proper fix = panel the
+                                        # psi-integral at rationals (deferred follow-up).
+write_outputs_to_HDF5 = true
+verbose = true
diff --git a/examples/Solovev_ideal_example/gpec.toml b/examples/Solovev_ideal_example/gpec.toml
index 9a74f1d35..0c478d10e 100644
--- a/examples/Solovev_ideal_example/gpec.toml
+++ b/examples/Solovev_ideal_example/gpec.toml
@@ -44,6 +44,11 @@ verbose = true                          # Enable verbose logging
 write_outputs_to_HDF5 = true            # Write perturbed equilibrium outputs to HDF5
 reg_spot = 0.05                         # Regularization width for singular surfaces (0 = disabled)
 
+# Note: SLAYER tearing analysis is not run on the Solovev analytic equilibrium
+# — its Δ' / inner-layer dispersion does not yield meaningful tearing roots.
+# The SLAYER example and regression case use the DIII-D-like geqdsk instead
+# (see examples/DIIID-like_SLAYER_example).
+
 [ForceFreeStates]
 local_stability_flag = true   # Perform local stability analysis (Mercier and ballooning) across the ψ profile
 mat_flag = true               # Construct coefficient matrices for diagnostic purposes
diff --git a/regression-harness/cases/diiid_n1.toml b/regression-harness/cases/diiid_n1.toml
index 1a69b7279..6693b5601 100644
--- a/regression-harness/cases/diiid_n1.toml
+++ b/regression-harness/cases/diiid_n1.toml
@@ -347,6 +347,19 @@ label = "PE toroidal torque"
 noise_threshold = 1e-12
 order = 94
 
+# NTV (neoclassical toroidal viscosity) torque from the [KineticForces] block —
+# FGAR method, pressure moment. Diagnostic computed on top of the ideal FFS + PE
+# result. Complex total torque stored as [Re, Im]; tracked as a 2-element vector.
+# Absent (N/A) for refs without a [KineticForces] section. Threshold is a noise
+# floor for the adaptive-quadrature result; tune if run-to-run jitter appears.
+[quantities.ntv_torque_fgar]
+h5path = "kinetic_forces/fgar/total_torque"
+type = "real_vector"
+extract = "all_real"
+label = "NTV torque FGAR (Re,Im) [N·m]"
+noise_threshold = 1e-2
+order = 95
+
 # Profiles (checksums)
 [quantities.q_profile]
 h5path = "splines/profiles/q"
diff --git a/regression-harness/cases/diiid_slayer_n1.toml b/regression-harness/cases/diiid_slayer_n1.toml
new file mode 100644
index 000000000..13167efe5
--- /dev/null
+++ b/regression-harness/cases/diiid_slayer_n1.toml
@@ -0,0 +1,162 @@
+[case]
+name = "diiid_slayer_n1"
+description = "DIII-D-like H-mode equilibrium, n=1, SLAYER tearing-mode analysis (uncoupled per-surface, AMR, validity-gated)"
+example_dir = "examples/DIIID-like_SLAYER_example"
+
+# ---------------------------------------------------------------------
+# Per-surface SLAYER layer parameters (geometry + dimensionless)
+# ---------------------------------------------------------------------
+[quantities.slayer_ising]
+h5path = "slayer/per_surface/ising"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER surface indices"
+noise_threshold = 0
+order = 10
+
+[quantities.slayer_m]
+h5path = "slayer/per_surface/m"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER poloidal m"
+noise_threshold = 0
+order = 11
+
+[quantities.slayer_n]
+h5path = "slayer/per_surface/n"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER toroidal n"
+noise_threshold = 0
+order = 12
+
+[quantities.slayer_rs]
+h5path = "slayer/per_surface/rs"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER minor radius rs"
+noise_threshold = 1e-10
+order = 13
+
+[quantities.slayer_sval_r]
+h5path = "slayer/per_surface/sval_r"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER r-based shear"
+noise_threshold = 1e-10
+order = 14
+
+[quantities.slayer_lu]
+h5path = "slayer/per_surface/lu"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER Lundquist S"
+noise_threshold = 1e-8
+order = 15
+
+[quantities.slayer_D_norm]
+h5path = "slayer/per_surface/D_norm"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER D_norm"
+noise_threshold = 1e-10
+order = 16
+
+[quantities.slayer_P_perp]
+h5path = "slayer/per_surface/P_perp"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER P_perp"
+noise_threshold = 1e-8
+order = 17
+
+[quantities.slayer_tauk]
+h5path = "slayer/per_surface/tauk"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER tauk"
+noise_threshold = 1e-12
+order = 18
+
+[quantities.slayer_iota_e]
+h5path = "slayer/per_surface/iota_e"
+type = "real_vector"
+extract = "all_real"
+label = "SLAYER iota_e"
+noise_threshold = 1e-12
+order = 19
+
+# ---------------------------------------------------------------------
+# Tearing eigenvalue (coupled mode → length 1). The headline deliverable:
+# a real, nonzero growth rate on a realistic equilibrium. Root-extraction
+# is sensitive to the AMR cell topology and ODE solver, so the Q/ω/γ
+# thresholds are absolute and modestly loose; re-pin intentionally if a
+# solver/AMR change shifts the root inventory.
+# Growth-rate / frequency / root outputs. Pinned for the inner three rational
+# surfaces only (2/1, 3/1, 4/1) via `first_3`: the Δ'/γ contour search is
+# numerically unreliable on the outermost surfaces (e.g. 5/1, 6/1, 7/1 near the
+# edge), so those are deliberately not golden-tracked.
+# ---------------------------------------------------------------------
+[quantities.slayer_Q_re]
+h5path = "slayer/roots/Q_root_real"
+type = "real_vector"
+extract = "first_3"
+label = "SLAYER Re(Q_root) [2/1,3/1,4/1]"
+noise_threshold = 1e-4
+order = 30
+
+[quantities.slayer_Q_im]
+h5path = "slayer/roots/Q_root_imag"
+type = "real_vector"
+extract = "first_3"
+label = "SLAYER Im(Q_root) [2/1,3/1,4/1]"
+noise_threshold = 1e-4
+order = 31
+
+[quantities.slayer_omega_Hz]
+h5path = "slayer/roots/omega_Hz"
+type = "real_vector"
+extract = "first_3"
+label = "SLAYER ω_Hz [2/1,3/1,4/1]"
+noise_threshold = 1.0
+order = 32
+
+[quantities.slayer_gamma_Hz]
+h5path = "slayer/roots/gamma_Hz"
+type = "real_vector"
+extract = "first_3"
+label = "SLAYER γ_Hz [2/1,3/1,4/1]"
+noise_threshold = 1e-1
+order = 33
+
+# no_root flag (1 = extraction failed). Pinned for the inner three surfaces;
+# this case guards that the 2/1, 3/1, 4/1 keep finding a real root.
+[quantities.slayer_no_root]
+h5path = "slayer/roots/no_root"
+type = "real_vector"
+extract = "first_3"
+label = "SLAYER no_root flags [2/1,3/1,4/1]"
+noise_threshold = 0
+order = 34
+
+# ---------------------------------------------------------------------
+# Settings (catches accidental config drift)
+# ---------------------------------------------------------------------
+[quantities.slayer_enabled]
+h5path = "slayer/enabled"
+type = "int_scalar"
+extract = "value"
+label = "SLAYER enabled flag"
+noise_threshold = 0
+order = 90
+
+# ---------------------------------------------------------------------
+# Runtime
+# ---------------------------------------------------------------------
+[quantities.runtime]
+h5path = ""
+type = "runtime"
+extract = "value"
+label = "Runtime (s)"
+noise_threshold = 0.0
+order = 999
diff --git a/regression-harness/src/extractor.jl b/regression-harness/src/extractor.jl
index c251ed1ad..bd7fc400b 100644
--- a/regression-harness/src/extractor.jl
+++ b/regression-harness/src/extractor.jl
@@ -70,12 +70,23 @@ function apply_extraction(spec::QuantitySpec, raw)::ExtractedQuantity
 
     elseif spec.extract == "all_real"
         arr = Float64.(real.(raw))
-        json_str = JSON.json(arr)
+        # allownan: SLAYER γ/Q_root are NaN when no dispersion root is found —
+        # a legitimate state to record so a future root appearing flags a diff.
+        json_str = JSON.json(arr; allownan=true)
+        return ExtractedQuantity(name, label, nothing, nothing, json_str, "json_array", threshold)
+
+    elseif startswith(spec.extract, "first_")
+        # "first_N": real values of the leading N vector elements only. Used to
+        # golden-pin the inner (trustworthy) rational surfaces while ignoring
+        # edge surfaces where the Δ'/γ contour search is numerically unreliable.
+        nkeep = parse(Int, spec.extract[(length("first_")+1):end])
+        arr = Float64.(real.(raw))[1:min(nkeep, length(raw))]
+        json_str = JSON.json(arr; allownan=true)
         return ExtractedQuantity(name, label, nothing, nothing, json_str, "json_array", threshold)
 
     elseif spec.extract == "all_complex"
         pairs = [[real(x), imag(x)] for x in raw]
-        json_str = JSON.json(pairs)
+        json_str = JSON.json(pairs; allownan=true)
         return ExtractedQuantity(name, label, nothing, nothing, json_str, "json_array", threshold)
 
     elseif spec.extract == "diagonal_complex"
@@ -85,7 +96,7 @@ function apply_extraction(spec::QuantitySpec, raw)::ExtractedQuantity
             error("diagonal_complex requires a square 2-D matrix; got size $(size(raw))")
         diag_vec = [raw[i, i] for i in 1:size(raw, 1)]
         pairs = [[real(x), imag(x)] for x in diag_vec]
-        json_str = JSON.json(pairs)
+        json_str = JSON.json(pairs; allownan=true)
         return ExtractedQuantity(name, label, nothing, nothing, json_str, "json_array", threshold)
 
     elseif spec.extract == "checksum"
@@ -98,9 +109,14 @@ function apply_extraction(spec::QuantitySpec, raw)::ExtractedQuantity
     end
 end
 
-"""Compute absolute difference between two JSON-parsed elements (scalars, pairs, or nested arrays)."""
+"""
+Compute absolute difference between two JSON-parsed elements (scalars, pairs, or nested arrays).
+"""
 function _json_element_diff(a, b)::Float64
     if a isa Number && b isa Number
+        # Two NaN values denote the same "no result" state (e.g. SLAYER
+        # Q_root when no dispersion root is found) — treat as identical.
+        (isnan(Float64(a)) && isnan(Float64(b))) && return 0.0
         return abs(Float64(a) - Float64(b))
     elseif a isa Vector && b isa Vector
         return sqrt(sum(_json_element_diff(ai, bi)^2 for (ai, bi) in zip(a, b)))
@@ -109,9 +125,14 @@ function _json_element_diff(a, b)::Float64
     end
 end
 
-"""Compute absolute value/magnitude of a JSON-parsed element."""
+"""
+Compute absolute value/magnitude of a JSON-parsed element.
+"""
 function _json_element_abs(x)::Float64
     if x isa Number
+        # NaN denotes a "no result" state (e.g. SLAYER Q_root with no root);
+        # treat as 0 magnitude so it doesn't poison a vector's reference norm.
+        isnan(Float64(x)) && return 0.0
         return abs(Float64(x))
     elseif x isa Vector
         return sqrt(sum(_json_element_abs(xi)^2 for xi in x))
@@ -162,8 +183,8 @@ function compare_values(q1::NamedTuple, q2::NamedTuple)
         if t1 === nothing || t2 === nothing
             return (NaN, NaN, "N/A")
         end
-        arr1 = JSON.parse(t1)
-        arr2 = JSON.parse(t2)
+        arr1 = JSON.parse(t1; allownan=true)
+        arr2 = JSON.parse(t2; allownan=true)
         if length(arr1) != length(arr2)
             return (NaN, NaN, "CHANGED (length)")
         end
diff --git a/regression-harness/src/reporter.jl b/regression-harness/src/reporter.jl
index e86fc498f..c0c57758f 100644
--- a/regression-harness/src/reporter.jl
+++ b/regression-harness/src/reporter.jl
@@ -14,7 +14,7 @@ function _short_err(msg)::String
     lines = filter(!isempty, strip.(split(s, '\n')))
     isempty(lines) && return "(empty)"
     pick = something(findfirst(l -> startswith(l, "ERROR:") || occursin("Error", l), lines),
-                     length(lines))
+        length(lines))
     line = lines[pick]
     return length(line) > 200 ? line[1:200] * "..." : line
 end
@@ -36,7 +36,7 @@ function format_value(q::NamedTuple)::String
     elseif q.value_type == "json_array"
         t = q.value_text
         t === nothing && return "N/A"
-        arr = JSON.parse(t)
+        arr = JSON.parse(t; allownan=true)
         return "[$(length(arr)) elem]"
     elseif q.value_type == "checksum"
         t = q.value_text
@@ -66,7 +66,9 @@ function format_diff(abs_diff::Float64, rel_diff::Float64, status::String, value
     return @sprintf("%.3e (%.2f%%)", abs_diff, rel_diff * 100)
 end
 
-"""Helper: print a row of padded columns."""
+"""
+Helper: print a row of padded columns.
+"""
 function _print_row(cols::Vector{String}, widths::Vector{Int})
     parts = [rpad(cols[i], widths[i]) for i in eachindex(cols)]
     println(join(parts, "  "))
@@ -76,7 +78,7 @@ end
 Print a two-ref comparison report for a single case.
 """
 function report_two_ref_comparison(db::SQLite.DB, case_spec::CaseSpec,
-                                   ref1::ResolvedRef, ref2::ResolvedRef)
+    ref1::ResolvedRef, ref2::ResolvedRef)
     info1 = get_run_info(db, ref1.commit_hash, case_spec.name)
     info2 = get_run_info(db, ref2.commit_hash, case_spec.name)
 
@@ -103,7 +105,9 @@ function report_two_ref_comparison(db::SQLite.DB, case_spec::CaseSpec,
     # Pre-compute all rows: (label, v1, v2, diff, status)
     header = ["Quantity", ref1.name, ref2.name, "Diff", "Status"]
     rows = Vector{Vector{String}}()
-    n_ok = 0; n_changed = 0; n_missing = 0
+    n_ok = 0
+    n_changed = 0
+    n_missing = 0
 
     # Helper: pick the value-cell text for one ref/quantity, accounting for
     # whole-ref failure (which should render as "FAILED" rather than "N/A").
@@ -114,7 +118,7 @@ function report_two_ref_comparison(db::SQLite.DB, case_spec::CaseSpec,
     runtime_cell = (failed::Bool, qs::Dict, qname::String) -> begin
         failed && return "FAILED"
         (haskey(qs, qname) && qs[qname].value_real !== nothing) ?
-            @sprintf("%.1fs", qs[qname].value_real) : "N/A"
+        @sprintf("%.1fs", qs[qname].value_real) : "N/A"
     end
 
     for spec in case_spec.quantities
@@ -145,15 +149,20 @@ function report_two_ref_comparison(db::SQLite.DB, case_spec::CaseSpec,
             continue
         end
 
-        q1 = q1_all[qname]; q2 = q2_all[qname]
+        q1 = q1_all[qname]
+        q2 = q2_all[qname]
         abs_diff, rel_diff, status = compare_values(q1, q2)
         st = status == "CHANGED" ? "** CHANGED **" : status
         push!(rows, [spec.label, format_value(q1), format_value(q2),
-                     format_diff(abs_diff, rel_diff, status, q1.value_type), st])
+            format_diff(abs_diff, rel_diff, status, q1.value_type), st])
 
-        if status == "OK"; n_ok += 1
-        elseif contains(status, "CHANGED"); n_changed += 1
-        else; n_missing += 1; end
+        if status == "OK"
+            n_ok += 1
+        elseif contains(status, "CHANGED")
+            n_changed += 1
+        else
+            n_missing += 1
+        end
     end
 
     # Derive column widths from header + all row content
@@ -202,13 +211,15 @@ Print a multi-ref tracking report for a single case.
 One row per quantity, one column per ref.
 """
 function report_multi_ref(db::SQLite.DB, case_spec::CaseSpec,
-                          refs::Vector{ResolvedRef})
+    refs::Vector{ResolvedRef})
     run_infos = [get_run_info(db, ref.commit_hash, case_spec.name) for ref in refs]
     failed_mask = [info === nothing || !info.success for info in run_infos]
-    all_qs = [begin
-        info = run_infos[i]
-        (info !== nothing && info.success) ? get_quantities(db, refs[i].commit_hash, case_spec.name) : Dict{String,NamedTuple}()
-    end for i in eachindex(refs)]
+    all_qs = [
+        begin
+            info = run_infos[i]
+            (info !== nothing && info.success) ? get_quantities(db, refs[i].commit_hash, case_spec.name) : Dict{String,NamedTuple}()
+        end for i in eachindex(refs)
+    ]
 
     show_diff = length(refs) >= 2
     ref_names = [ref.name for ref in refs]
@@ -223,7 +234,9 @@ function report_multi_ref(db::SQLite.DB, case_spec::CaseSpec,
 
     # Pre-compute rows
     rows = Vector{Vector{String}}()
-    n_ok = 0; n_changed = 0; n_missing = 0
+    n_ok = 0
+    n_changed = 0
+    n_missing = 0
 
     for spec in case_spec.quantities
         qname = spec.name
@@ -274,9 +287,13 @@ function report_multi_ref(db::SQLite.DB, case_spec::CaseSpec,
                 st = status == "CHANGED" ? "** CHANGED **" : status
                 push!(row, format_diff(abs_diff, rel_diff, status, q_prev.value_type))
                 push!(row, st)
-                if status == "OK"; n_ok += 1
-                elseif contains(status, "CHANGED"); n_changed += 1
-                else; n_missing += 1; end
+                if status == "OK"
+                    n_ok += 1
+                elseif contains(status, "CHANGED")
+                    n_changed += 1
+                else
+                    n_missing += 1
+                end
             elseif last_failed || prev_failed
                 push!(row, "")
                 push!(row, "FAILED")
diff --git a/src/Equilibrium/DirectEquilibrium.jl b/src/Equilibrium/DirectEquilibrium.jl
index 8d3d08a4e..d214b05c1 100644
--- a/src/Equilibrium/DirectEquilibrium.jl
+++ b/src/Equilibrium/DirectEquilibrium.jl
@@ -380,7 +380,7 @@ function direct_refine(rfac::Float64, eta::Float64, psi0::Float64, params::Field
     end
 
     return find_zero((f, fp), rfac, Roots.Newton();
-        atol=1e-12*abs(psi0), rtol=1e-12, maxevals=50)
+        atol=1e-12 * abs(psi0), rtol=1e-12, maxevals=50)
 end
 
 """
@@ -456,7 +456,7 @@ The middle-region spacing is driven by profile curvature (P, F, dV/dψ, q) via s
 """
 function make_optimal_mpsi(psilow, psihigh, A, sq_in;
     tau=0.005, psi_split_core=0.03, psi_split_edge=0.98)
-    dlog = (13.0 * tau / A)^(1/4)
+    dlog = (13.0 * tau / A)^(1 / 4)
     N_edge = ceil(Int, log((1.0 - psi_split_edge) / (1.0 - psihigh)) / dlog) + 1
     h_mid = _estimate_mid_spacing(sq_in, psi_split_core, psi_split_edge, tau)
     N_mid = ceil(Int, (psi_split_edge - psi_split_core) / h_mid)
@@ -582,7 +582,7 @@ robustness.
         @. ff_x_nodes = @view(y_out[:, 5]) / y_out[end, 5]
 
         ff_fs_nodes = acquire!(pool, Float64, size(y_out, 1), 4)
-        @. ff_fs_nodes[:, 1] = @view(y_out[:, 3]) ^ 2
+        @. ff_fs_nodes[:, 1] = @view(y_out[:, 3])^2
         @. ff_fs_nodes[:, 2] = @view(y_out[:, 1]) / (2π) - ff_x_nodes
         @. ff_fs_nodes[:, 3] = bfield.f * (@view(y_out[:, 4]) - ff_x_nodes * y_out[end, 4])
         @. ff_fs_nodes[:, 4] = @view(y_out[:, 2]) / y_out[end, 2] - ff_x_nodes
diff --git a/src/Equilibrium/Equilibrium.jl b/src/Equilibrium/Equilibrium.jl
index 13e478e29..461345194 100644
--- a/src/Equilibrium/Equilibrium.jl
+++ b/src/Equilibrium/Equilibrium.jl
@@ -26,7 +26,8 @@ include("KineticProfiles.jl")
 # --- Expose types and functions to the user ---
 export setup_equilibrium, EquilibriumConfig, PlasmaEquilibrium, EquilibriumParameters,
     ProfileSplines, GeometryProfileSplines, compute_geometry_profiles,
-    KineticProfileSplines, load_kinetic_profiles
+    KineticProfileSplines, load_kinetic_profiles,
+    KineticProfileData, read_kinetic_file, write_kinetic_h5
 export flux_surface_metric, flux_surface_area
 export compute_sqrt_jac_delpsi, compute_sqrtamat, rootarea_to_area_weight, area_to_rootarea_weight
 
diff --git a/src/Equilibrium/KineticProfiles.jl b/src/Equilibrium/KineticProfiles.jl
index a465409f7..413cd1503 100644
--- a/src/Equilibrium/KineticProfiles.jl
+++ b/src/Equilibrium/KineticProfiles.jl
@@ -1,13 +1,132 @@
 """
     KineticProfiles
 
-Reading and processing kinetic profile data (density, temperature, rotation)
-from ASCII tables. The output is a `KineticProfileSplines` of independent named
-splines that downstream physics modules (KineticForces NTV, etc.) can read
-directly without re-implementing data shimming.
+Reading and processing kinetic profile data (density, temperature, rotation,
+and optionally transport diffusivities) from the GPEC HDF5 kinetic format or
+from legacy 6-column ASCII tables (`.gpeckf`/`.kin`). The processed output is a
+`KineticProfileSplines` of independent named splines that downstream physics
+modules (KineticForces NTV, etc.) read directly without re-implementing data
+shimming.
 """
 
 using DelimitedFiles
+using HDF5
+
+"""
+    KineticProfileData
+
+Raw kinetic-profile columns read from a kinetic file (HDF5 or ASCII), before
+resampling and spline construction. `psi` (normalized poloidal flux) is always
+present; every profile field is `nothing` when the source omits it, so each
+consumer validates only the fields it needs. Units: densities m⁻³,
+temperatures eV, frequencies rad/s, diffusivities m²/s.
+
+| field       | meaning                              |
+|-------------|--------------------------------------|
+| `n_i`       | main-ion density                     |
+| `n_e`       | electron density                     |
+| `T_i`/`T_e` | ion / electron temperature           |
+| `omega_E`   | ExB rotation                         |
+| `omega_tor` | toroidal rotation (optional)         |
+| `chi_e`     | perpendicular heat diffusivity χ⊥    |
+| `chi_phi`   | toroidal momentum diffusivity χ_φ    |
+"""
+struct KineticProfileData
+    psi::Vector{Float64}
+    n_i::Union{Nothing,Vector{Float64}}
+    n_e::Union{Nothing,Vector{Float64}}
+    T_i::Union{Nothing,Vector{Float64}}
+    T_e::Union{Nothing,Vector{Float64}}
+    omega_E::Union{Nothing,Vector{Float64}}
+    omega_tor::Union{Nothing,Vector{Float64}}
+    chi_e::Union{Nothing,Vector{Float64}}
+    chi_phi::Union{Nothing,Vector{Float64}}
+    provenance::String
+end
+
+function KineticProfileData(; psi, n_i=nothing, n_e=nothing, T_i=nothing, T_e=nothing,
+    omega_E=nothing, omega_tor=nothing, chi_e=nothing, chi_phi=nothing, provenance="")
+    _v(x) = x === nothing ? nothing : Float64.(collect(x))
+    return KineticProfileData(Float64.(collect(psi)), _v(n_i), _v(n_e), _v(T_i), _v(T_e),
+        _v(omega_E), _v(omega_tor), _v(chi_e), _v(chi_phi), String(provenance))
+end
+
+const _KINETIC_H5_EXTS = (".h5", ".hdf5", ".he5")
+
+# Dataset units written into / expected from the HDF5 kinetic schema.
+const _KINETIC_H5_UNITS = Dict("psi" => "normalized poloidal flux", "n_i" => "m^-3",
+    "n_e" => "m^-3", "T_i" => "eV", "T_e" => "eV", "omega_E" => "rad/s",
+    "omega_tor" => "rad/s", "chi_e" => "m^2/s", "chi_phi" => "m^2/s")
+
+"""
+    read_kinetic_file(path; group="/") -> KineticProfileData
+
+Read a kinetic-profile file, dispatching on extension: HDF5 (`.h5`/`.hdf5`) via
+the GPEC kinetic schema, otherwise a 6-column ASCII table (`.gpeckf`/`.kin`/`.dat`:
+`psi_n n_i n_e T_i[eV] T_e[eV] omega_E`). Returns the raw columns; downstream
+consumers validate the fields they require.
+"""
+function read_kinetic_file(path::AbstractString; group::AbstractString="/")
+    isfile(path) || error("Kinetic profile file not found: $path")
+    ext = lowercase(splitext(path)[2])
+    return ext in _KINETIC_H5_EXTS ? _read_kinetic_h5(path; group=group) : _read_kinetic_ascii(path)
+end
+
+"""Read a legacy 6-column ASCII kinetic table into a `KineticProfileData`."""
+function _read_kinetic_ascii(path::AbstractString)
+    psi, ni, ne, Ti, Te, omegaE = _read_kinetic_table(path)
+    return KineticProfileData(; psi=psi, n_i=ni, n_e=ne, T_i=Ti, T_e=Te, omega_E=omegaE,
+        provenance="ASCII 6-column: $(basename(path))")
+end
+
+"""Read the GPEC HDF5 kinetic schema. Only `psi` is required; other datasets are
+optional and surfaced as-is."""
+function _read_kinetic_h5(path::AbstractString; group::AbstractString="/")
+    h5open(path, "r") do f
+        g = group == "/" ? f : f[group]
+        haskey(g, "psi") || error("kinetic HDF5 '$path' group '$group': missing required dataset 'psi'")
+        rd(name) = haskey(g, name) ? Float64.(vec(read(g[name]))) : nothing
+        prov = ""
+        ats = attributes(g)
+        haskey(ats, "provenance") && (prov = string(read(ats["provenance"])))
+        return KineticProfileData(; psi=Float64.(vec(read(g["psi"]))),
+            n_i=rd("n_i"), n_e=rd("n_e"), T_i=rd("T_i"), T_e=rd("T_e"),
+            omega_E=rd("omega_E"), omega_tor=rd("omega_tor"),
+            chi_e=rd("chi_e"), chi_phi=rd("chi_phi"), provenance=prov)
+    end
+end
+
+"""
+    write_kinetic_h5(path, data; group="/", schema_version="1.0", provenance=data.provenance)
+
+Write a `KineticProfileData` to the GPEC HDF5 kinetic schema. Each present field
+is written as a dataset with a `units` attribute; absent (`nothing`) fields are
+skipped. The group carries `schema_version` and `provenance` attributes.
+"""
+function write_kinetic_h5(path::AbstractString, data::KineticProfileData;
+    group::AbstractString="/", schema_version::AbstractString="1.0",
+    provenance::AbstractString=data.provenance)
+    h5open(path, "w") do f
+        g = group == "/" ? f : create_group(f, group)
+        function put(name, v)
+            v === nothing && return
+            g[name] = collect(Float64, v)
+            attributes(g[name])["units"] = _KINETIC_H5_UNITS[name]
+        end
+        put("psi", data.psi)
+        put("n_i", data.n_i)
+        put("n_e", data.n_e)
+        put("T_i", data.T_i)
+        put("T_e", data.T_e)
+        put("omega_E", data.omega_E)
+        put("omega_tor", data.omega_tor)
+        put("chi_e", data.chi_e)
+        put("chi_phi", data.chi_phi)
+        attributes(g)["schema_version"] = schema_version
+        attributes(g)["provenance"] = provenance
+    end
+    return path
+end
 
 """
     load_kinetic_profiles(kinetic_file::AbstractString;
@@ -60,12 +179,17 @@ function load_kinetic_profiles(kinetic_file::AbstractString;
     ExB_rotation_factor::Float64=1.0, toroidal_rotation_factor::Float64=1.0,
     chi1::Union{Nothing,Float64}=nothing)
 
-    if !isfile(kinetic_file)
-        error("Kinetic profile file not found: $kinetic_file")
-    end
-
-    psi_input, ni_input, ne_input, Ti_input_eV, Te_input_eV, omegaE_input =
-        _read_kinetic_table(kinetic_file)
+    data = read_kinetic_file(kinetic_file)
+
+    # NTV requires n_e, T_i, T_e, omega_E; n_i defaults to n_e (quasineutrality)
+    # when absent. omega_tor / chi_e / chi_phi (if present) are ignored here.
+    _need(field, name) = field === nothing ? error("kinetic file '$kinetic_file' missing required '$name'") : field
+    psi_input = data.psi
+    ne_input = _need(data.n_e, "n_e")
+    Ti_input_eV = _need(data.T_i, "T_i")
+    Te_input_eV = _need(data.T_e, "T_e")
+    omegaE_input = _need(data.omega_E, "omega_E")
+    ni_input = data.n_i === nothing ? copy(ne_input) : data.n_i
 
     eV_to_J = 1.602e-19
     mp = 1.672_614e-27
diff --git a/src/ForceFreeStates/ForceFreeStates.jl b/src/ForceFreeStates/ForceFreeStates.jl
index cc144731f..d740a16ae 100644
--- a/src/ForceFreeStates/ForceFreeStates.jl
+++ b/src/ForceFreeStates/ForceFreeStates.jl
@@ -24,6 +24,7 @@ include("ForceFreeStatesStructs.jl")
 include("Ballooning.jl")
 include("EulerLagrange.jl")
 include("Sing.jl")
+include("ResistEval.jl")
 include("Fourfit.jl")
 include("FixedKineticMatrices.jl")
 include("Kinetic.jl")
diff --git a/src/ForceFreeStates/ForceFreeStatesStructs.jl b/src/ForceFreeStates/ForceFreeStatesStructs.jl
index 337eb158b..fc47219c6 100644
--- a/src/ForceFreeStates/ForceFreeStatesStructs.jl
+++ b/src/ForceFreeStates/ForceFreeStatesStructs.jl
@@ -31,6 +31,7 @@ A mutable struct holding data related to the singular surfaces in the equilibriu
     ua_right::Array{ComplexF64,3} = Array{ComplexF64}(undef, 0, 0, 0)  # asymptotic basis at right inner-layer boundary
     psi_ua_left::Float64 = 0.0   # ψ where ua_left was evaluated (left inner-layer boundary)
     psi_ua_right::Float64 = 0.0  # ψ where ua_right was evaluated (right inner-layer boundary)
+    restype::Any = nothing       # ResistGeometry from ResistEval.jl (populated by resist_eval_all!); typed `Any` to avoid a cross-file type reference
 end
 
 """
@@ -193,6 +194,23 @@ A mutable struct holding internal state variables for stability calculations.
     raw 2msing×2msing BVP solution to produce the PEST3-compatible tearing parameter.
     """
     delta_prime_matrix::Matrix{ComplexF64} = Matrix{ComplexF64}(undef, 0, 0)
+
+    """
+    Raw 2msing × 2msing outer-region matching matrix `D'` from the STRIDE global
+    BVP, in the side-major ordering `[L_s1, R_s1, L_s2, R_s2, …, L_sm, R_sm]`
+    (left vs right of each singular surface, interleaved surface-by-surface).
+    This is the Pletzer–Dewar 1991 outer-region matrix before parity rotation,
+    and is stored byte-compatibly with the Fortran `rdcon/gal.f::gal_write_delta`
+    convention (top 2msing×2msing block of `delta_gw.dat`). The PEST3 Δ' matrix
+    stored in `delta_prime_matrix` is the odd-parity tearing projection of this
+    raw matrix; the even-parity A' and off-parity B', Γ' blocks are recovered
+    via `pest3_decompose(dp_raw)` — needed for the full det(D' − D(γ)) = 0
+    eigenvalue problem with Glasser stabilization.
+
+    Empty unless `ctrl.use_parallel` is true. No ½ prefactor is applied (matches
+    Fortran rdcon; Pletzer–Dewar paper multiplies by ½).
+    """
+    delta_prime_raw::Matrix{ComplexF64} = Matrix{ComplexF64}(undef, 0, 0)
 end
 
 """
@@ -242,7 +260,7 @@ A mutable struct containing control parameters for stability analysis, set by th
   - `force_termination::Bool` - Terminate after force-free states (skip perturbed equilibrium calculations)
   - `use_riccati::Bool` - Use the dual Riccati reformulation S = U₁·U₂⁻¹ instead of the standard U₁/U₂ ODE. Reduces stiffness for faster integration. See Glasser (2018) Phys. Plasmas 25, 032507.
   - `use_parallel::Bool` - Parallel fundamental matrix (propagator) integration using `Threads.@threads`. Each chunk is integrated independently from identity IC and assembled serially. Requires `singfac_min != 0`. Uses the same chunk bounds as the standard path but sub-divides chunks for load balancing. Crossings use the Riccati-style algorithm (no Gaussian reduction).
-  - `parallel_threads::Int` - Cap on the number of threads the parallel BVP uses. **Default `2`** parallelises the FM chunks across two threads (the BVP has ~10 chunks; 2 threads is enough to amortize them — speedup saturates here, raising to 4 adds scheduling overhead). Set `parallel_threads = 1` to run the FM chunks SERIALLY (no `Threads.@threads`), which is bit-deterministic and immune to the thread-schedule sensitivity that historically caused intermittent BVP divergences on numerically delicate equilibria like DIII-D 147131 (see CONVENTIONS.md §7). Empirical reliability sweep (5 trials × {1,2,4} on DIII-D 147131 βₚ≈0.07): 15/15 bit-identical Δ′ at every setting; pt=2 ≈ pt=4 ≈ 20 % faster than serial. If a parallel run diverges, drop to `parallel_threads = 1` rather than switching `use_parallel = false` — the latter is silently wrong. Capped at `Threads.nthreads()`.
+  - `parallel_threads::Int` - Cap on the number of threads the parallel BVP uses. **Default `2`** parallelises the FM chunks across two threads (the BVP has ~10 chunks; 2 threads is enough to amortize them — speedup saturates here, raising to 4 adds scheduling overhead). Set `parallel_threads = 1` to run the FM chunks SERIALLY (no `Threads.@threads`), which is bit-deterministic and immune to the thread-schedule sensitivity that can cause intermittent BVP divergence on numerically delicate equilibria. The parallel path produces bit-identical Δ′ across thread counts; `parallel_threads = 2` is about 20% faster than serial and saturates the speedup. If a parallel run diverges, drop to `parallel_threads = 1` rather than switching `use_parallel = false` — the latter is silently wrong. Capped at `Threads.nthreads()`.
   - `populate_dense_xi::Bool` - When `use_parallel = true`, append a serial Euler-Lagrange pass at the end of the propagator BVP and let it replace the `odet` returned to the main pipeline.  This populates `u_store` / `ud_store` densely in the axis (EL) basis — the only convention the PerturbedEquilibrium / FieldReconstruction downstream code consumes correctly.  Without it the parallel path stores only chunk-endpoint Riccati S matrices and zeros for `ud_store` (see Riccati.jl docstring caveats), and HDF5 `integration/xi_psi`/`dxi_psi`/`xi_s` are unusable.  Δ' (`singular/delta_prime_matrix`) is computed from the parallel BVP and is bit-identical between `populate_dense_xi=true` and `false`.  Energies (`vacuum/ep`/`ev`/`et`) are computed by `free_run!` from `odet`, so with `populate_dense_xi=true` they match what a pure serial run (`use_parallel=false`) would produce; with `populate_dense_xi=false` they use the parallel-pass Riccati `odet.u` instead (differs by the ~0.12 % Riccati-vs-axis algorithmic gap on DIIID-class cases).  **Default `false`** to avoid paying the dense-pass cost on Δ'/vacuum/ideal-stability-only runs; **PerturbedEquilibrium-using configs must set `populate_dense_xi = true` explicitly** when `use_parallel = true` (otherwise PE silently reads Riccati-basis garbage).  Auto-disabled when `force_termination = true` regardless of the user setting, since the dense pass has no downstream consumer in that case.  Approximate cost when enabled: one extra serial EL integration (~1× the parallel BVP wall-clock for typical N).
   - `extended_precision_bvp::Bool` - When `true` (default), promote the Δ' BVP linear system to `Complex{Double64}` (~31 digits) for the LU solve and PEST3 combination. Guards against catastrophic cancellation in the PEST3 four-term combination (dp_raw entries can be 10⁴–10⁵× larger than the result; the imaginary part of off-diagonal Δ' is particularly sensitive). Disabling (`false`) saves ~1.5–2× the BVP solve time but on DIIID-class equilibria the imaginary Δ' components can drift by factors of 2–5×; only disable for performance experiments on cases where Float64 has been validated against Double64.
 """
diff --git a/src/ForceFreeStates/ResistEval.jl b/src/ForceFreeStates/ResistEval.jl
new file mode 100644
index 000000000..8bfc5a03f
--- /dev/null
+++ b/src/ForceFreeStates/ResistEval.jl
@@ -0,0 +1,214 @@
+# ResistEval.jl
+#
+# Per-singular-surface Glasser-Greene-Johnson geometric coefficients (E, F,
+# G, H, K, M) and the two flux-surface averages (⟨B²/|∇ψ|²⟩, ⟨B²⟩) that
+# downstream callers need to turn geometry into τ_A / τ_R with kinetic
+# profiles.
+#
+# Port of Fortran RDCON `resist_eval` (geometric part only).
+# Unlike the Fortran, this routine produces *only* the pure-equilibrium
+# quantities; kinetic timescales (τ_A, τ_R) are built on top in the
+# downstream `build_ggj_inputs` helper using the same KineticProfiles that
+# feed SLAYER, rather than Fortran's hardcoded `ne=1e14, te=3e3`
+# parameter defaults.
+#
+# The 6 theta-integrands match the Fortran layout:
+#   1: B² / |∇ψ|²
+#   2: 1 / |∇ψ|²
+#   3: 1 / B²
+#   4: 1 / (B² · |∇ψ|²)
+#   5: B²
+#   6: |∇ψ|² / B²
+# All weighted by `jac / v1` (jacobian / dV/dψ) before integration.
+#
+# A seventh integrand, B, is added (beyond the Fortran set) so that ⟨B⟩ is
+# available for the Lin-Liu & Miller 1995 trapped-fraction formula used by
+# the shared NeoclassicalResistivity closure. B_max, B_min, and the flux-
+# surface-averaged major radius R_major are accumulated alongside by
+# running extrema over the θ-loop.
+
+"""
+    ResistGeometry
+
+Per-singular-surface Glasser-Greene-Johnson geometric coefficients and
+supporting flux-surface averages.
+
+| field       | meaning                                              |
+|-------------|------------------------------------------------------|
+| `E`, `F`    | Glasser interchange parameters (enter `D_I = E+F+H-¼`) |
+| `G`         | Coupling coefficient (curvature × pressure gradient) |
+| `H`         | Pfirsch-Schlüter coefficient                         |
+| `K`         | Glasser parameter                                    |
+| `M`         | Mass factor                                          |
+| `avg_bsq_over_dpsisq` | ⟨B²/|∇ψ|²⟩ — needed for τ_R         |
+| `avg_bsq`   | ⟨B²⟩ — needed for τ_R                                |
+| `avg_B`     | ⟨B⟩ — needed for Lin-Liu-Miller f_t                  |
+| `B_max`, `B_min` | θ-extrema of B on the surface [T]               |
+| `f_trap`    | Lin-Liu & Miller 1995 trapped-particle fraction      |
+| `R_major`   | flux-surface-averaged major radius ⟨R⟩ [m]           |
+| `eps_local` | (R_max − R_min)/2 / R_major — local inverse aspect ratio |
+| `p_local`   | Plasma pressure at this surface [Pa]                 |
+| `p1_local`  | dp/dψ at this surface                                |
+| `v1_local`  | dV/dψ at this surface                                |
+
+`H` here is identical to the `H` reported by `mercier_scan!` and stored
+in `locstab/h` — the GGJ routine recomputes it for convenience.
+
+`avg_B`, `B_max`, `B_min`, `f_trap`, `R_major`, and `eps_local` are used
+by `NeoclassicalResistivity.eta_neoclassical` to form the Sauter/Redl
+F_33 correction to Spitzer resistivity. See Sauter, Angioni & Lin-Liu
+1999, Phys. Plasmas 6, 2834 and Lin-Liu & Miller 1995, Phys. Plasmas 2,
+1666.
+"""
+struct ResistGeometry
+    E::Float64
+    F::Float64
+    G::Float64
+    H::Float64
+    K::Float64
+    M::Float64
+    avg_bsq_over_dpsisq::Float64
+    avg_bsq::Float64
+    avg_B::Float64
+    B_max::Float64
+    B_min::Float64
+    f_trap::Float64
+    R_major::Float64
+    eps_local::Float64
+    p_local::Float64
+    p1_local::Float64
+    v1_local::Float64
+end
+
+"""
+    resist_geometry(equil, psifac, q1; gamma=5/3) -> ResistGeometry
+
+Port of Fortran RDCON `resist_eval` restricted to the
+pure-equilibrium geometric coefficients. Integrates the 6 theta integrands
+at the given flux surface and combines them into E, F, G, H, K, M via the
+standard GGJ formulas.
+
+# Arguments
+
+  - `equil::PlasmaEquilibrium` — the fully-solved equilibrium
+  - `psifac` — normalized flux coordinate of the singular surface
+  - `q1`     — dq/dψ at this surface (from `SingType.q1`)
+
+# Keyword arguments
+
+  - `gamma`  — adiabatic index (default 5/3)
+
+!!! note "Contract"
+    `psifac` must be a genuine interior rational surface (`0 < ψ < 1`) with
+    nonzero `q1`, `p1 = dp/dψ`, and `p`. The GGJ combination divides by these
+    and by `|∇ψ|²` (which → 0 at the axis), so calling on the magnetic axis,
+    a flat-pressure surface, or a zero-shear surface yields `Inf`/`NaN`. This
+    matches the Fortran `resist_eval`, which is only ever invoked on interior
+    rationals.
+"""
+function resist_geometry(equil::Equilibrium.PlasmaEquilibrium,
+                          psifac::Real, q1::Real; gamma::Real=5/3)
+    profiles = equil.profiles
+    twopi    = 2π
+    chi1     = twopi * equil.psio
+    psi_f    = Float64(psifac)
+
+    # Surface-profile quantities (evaluate via the existing splines)
+    twopif = profiles.F_spline(psi_f)
+    p      = profiles.P_spline(psi_f)
+    p1     = profiles.P_deriv(psi_f)
+    v1     = profiles.dVdpsi_spline(psi_f)
+    v2     = profiles.dVdpsi_deriv(psi_f)
+    q      = profiles.q_spline(psi_f)
+
+    # Build the 6 GGJ θ-integrands plus a 7th (B) for the neoclassical
+    # resistivity f_t calculation, and accumulate running extrema of
+    # (B, R) for Lin-Liu-Miller f_t and the local ε.
+    ntheta = length(equil.rzphi_ys)
+    ff     = zeros(Float64, ntheta, 7)
+    B_max  = -Inf
+    B_min  =  Inf
+    R_max  = -Inf
+    R_min  =  Inf
+    for itheta in 1:ntheta
+        theta = equil.rzphi_ys[itheta]
+        f1  = equil.rzphi_rsquared((psi_f, theta))
+        f2  = equil.rzphi_offset((psi_f, theta))
+        jac = equil.rzphi_jac((psi_f, theta))
+        fy1 = FastInterpolations.deriv_view(equil.rzphi_rsquared, (0, 1))((psi_f, theta))
+        fy2 = FastInterpolations.deriv_view(equil.rzphi_offset,   (0, 1))((psi_f, theta))
+        fy3 = FastInterpolations.deriv_view(equil.rzphi_nu,       (0, 1))((psi_f, theta))
+
+        rfac = sqrt(f1)
+        eta  = twopi * (theta + f2)
+        r    = equil.ro + rfac * cos(eta)
+
+        v21 = fy1 / (2 * rfac * jac)
+        v22 = (1 + fy2) * twopi * rfac / jac
+        v23 = fy3 * r / jac
+        v33 = twopi * r / jac
+        bsq    = chi1^2 * (v21^2 + v22^2 + (v23 + q*v33)^2)
+        dpsisq = (twopi * r)^2 * (v21^2 + v22^2)
+
+        B_here = sqrt(bsq)
+        B_max = max(B_max, B_here)
+        B_min = min(B_min, B_here)
+        R_max = max(R_max, r)
+        R_min = min(R_min, r)
+
+        ff[itheta, 1] = bsq / dpsisq
+        ff[itheta, 2] = 1.0 / dpsisq
+        ff[itheta, 3] = 1.0 / bsq
+        ff[itheta, 4] = 1.0 / (bsq * dpsisq)
+        ff[itheta, 5] = bsq
+        ff[itheta, 6] = dpsisq / bsq
+        ff[itheta, 7] = B_here
+        @views ff[itheta, :] .*= jac / v1
+    end
+
+    # Integrate each column around θ using the same periodic cubic-spline
+    # integrator Mercier.jl uses
+    itp = cubic_interp(equil.rzphi_ys, Series(ff); bc=PeriodicBC())
+    avg = FastInterpolations.integrate(itp)
+    avg_B = avg[7]
+    R_major = 0.5 * (R_max + R_min)
+    eps_local = R_major > 0 ? 0.5 * (R_max - R_min) / R_major : 0.0
+    f_trap = Utilities.NeoclassicalResistivity.trapped_fraction(avg_B, avg[5], B_min, B_max)
+
+    # GGJ coefficients (Fortran RDCON resist_eval)
+    E_coef = p1 * v1 / (q1 * chi1^2)^2 * avg[1] *
+             (twopif * q1 * chi1 / avg[5] - v2)
+    F_coef = (p1 * v1 / (q1 * chi1^2))^2 *
+             (avg[1] * avg[3] + (twopif / chi1)^2 *
+              (avg[1] * avg[4] - avg[2]^2))
+    H_coef = twopif * p1 * v1 / (q1 * chi1^3) * (avg[2] - avg[1] / avg[5])
+    M_coef = avg[1] *
+             (avg[6] + (twopif / chi1)^2 * (avg[3] - 1.0 / avg[5]))
+    G_coef = avg[5] / (M_coef * gamma * p)
+    K_coef = (q1 * chi1^2 / (p1 * v1))^2 *
+             avg[5] / (M_coef * avg[1])
+
+    return ResistGeometry(
+        E_coef, F_coef, G_coef, H_coef, K_coef, M_coef,
+        avg[1], avg[5],
+        avg_B, B_max, B_min, f_trap, R_major, eps_local,
+        p, p1, v1,
+    )
+end
+
+"""
+    resist_eval_all!(intr::ForceFreeStatesInternal, equil; gamma=5/3)
+
+Populate `sing.restype` for every `SingType` in `intr.sing` using
+`resist_geometry`. No-op for surfaces whose `restype` has already been
+filled.
+"""
+function resist_eval_all!(intr::ForceFreeStatesInternal,
+                           equil::Equilibrium.PlasmaEquilibrium;
+                           gamma::Real=5/3)
+    for sing in intr.sing
+        sing.restype === nothing || continue
+        sing.restype = resist_geometry(equil, sing.psifac, sing.q1; gamma=gamma)
+    end
+    return intr
+end
diff --git a/src/ForceFreeStates/Riccati.jl b/src/ForceFreeStates/Riccati.jl
index 715c6421d..1be78f7f2 100644
--- a/src/ForceFreeStates/Riccati.jl
+++ b/src/ForceFreeStates/Riccati.jl
@@ -334,8 +334,15 @@ function compute_delta_prime_matrix!(
         @info "Δ' BVP: nMat=$nMat, rank(M)=$(rank(M)), cond(M)=$(@sprintf("%.2e", cond(M)))"
     end
 
-    intr.delta_prime_matrix = _solve_bvp_and_combine_pest3(
+    deltap, dp_raw_persisted = _solve_bvp_and_combine_pest3(
         M, msing, N, nMat, use_S_axis, ipert_all, col_edge, ctrl, debug)
+
+    # Persist both the PEST3 tearing projection (msing × msing) and the raw 2msing × 2msing
+    # D' matrix (side-major ordering, byte-compatible with Fortran rdcon/gal.f::gal_write_delta).
+    # The raw matrix is consumed by `pest3_decompose` to recover (A', B', Γ', Δ') for the full
+    # det(D' − D(γ)) = 0 eigenvalue problem; see ForceFreeStatesStructs.jl docstring.
+    intr.delta_prime_matrix = deltap
+    intr.delta_prime_raw    = dp_raw_persisted
 end
 
 # Column index helpers for the BVP matrix. j is the 1-based singular-surface index,
@@ -727,7 +734,9 @@ function _solve_bvp_and_combine_pest3(M::Matrix{ComplexF64}, msing::Int, N::Int,
     deltap = ComplexF64.(deltap_ext)
 
     debug && _log_bvp_pest3(dp_raw, deltap, s2, msing, Tc)
-    return deltap
+    # Return the PEST3-combined matrix AND the raw 2msing×2msing D' matrix (ComplexF64
+    # for compatibility with downstream pest3_decompose / HDF5 writer).
+    return deltap, ComplexF64.(dp_raw)
 end
 
 # Logging helpers for `compute_delta_prime_matrix!`. Called only when debug=true.
@@ -836,6 +845,69 @@ function _log_bvp_pest3(dp_raw, deltap, s2, msing, Tc)
     @info "Δ' BVP: deltap diagonal = $([@sprintf("%.4f%+.4fi", real(deltap[i,i]), imag(deltap[i,i])) for i in 1:msing])"
 end
 
+"""
+    pest3_decompose(dp_raw::AbstractMatrix) -> (A', B', Γ', Δ')
+
+Rotate the raw 2m×2m outer-region matching matrix `dp_raw` (side-major
+ordering `[L_s1, R_s1, L_s2, R_s2, …]`) into the Pletzer–Dewar 1991 parity
+blocks. Given rows and columns paired by surface (odd index = left, even
+index = right), the Fortran RDCON parity combination is
+
+```
+A'(i,j) = RR + RL + LR + LL    (even-i, even-j)   — interchange↔interchange
+B'(i,j) = RR − RL + LR − LL    (even-i, odd-j)    — interchange↔tearing
+Γ'(i,j) = RR + RL − LR − LL    (odd-i,  even-j)   — tearing↔interchange
+Δ'(i,j) = RR − RL − LR + LL    (odd-i,  odd-j)    — tearing↔tearing
+```
+
+where `RR = dp_raw[2i, 2j]`, `RL = dp_raw[2i, 2j−1]`,
+`LR = dp_raw[2i−1, 2j]`, `LL = dp_raw[2i−1, 2j−1]`. Each block is m×m.
+
+Matches Fortran exactly — no ½ prefactor (Pletzer–Dewar multiply by ½, but
+the Fortran RDCON code leaves it commented out and our Julia port follows
+Fortran to keep the benchmark bit-identical; the prefactor cancels in
+`det(D' − D(γ)) = 0`).
+
+The Δ' block returned here equals `intr.delta_prime_matrix` (the m×m PEST3
+tearing projection computed inside `compute_delta_prime_matrix!`).
+
+# Arguments
+
+  - `dp_raw` — 2m×2m complex matrix (typically `intr.delta_prime_raw`).
+
+# Returns
+
+Named tuple `(A=A', B=B', Γ=Gp, Δ=Dp)` of four m×m complex matrices. In the
+full `det(D' − D(γ)) = 0` eigenvalue problem, these fill the 2m×2m outer
+matrix as `D' = [[A' B'] [Γ' Δ']]` with the interchange channel (Glasser
+stabilization) in the upper-left block and the tearing channel in the
+lower-right.
+"""
+function pest3_decompose(dp_raw::AbstractMatrix)
+    s2 = size(dp_raw, 1)
+    size(dp_raw, 2) == s2 ||
+        throw(ArgumentError("pest3_decompose: dp_raw must be square, got $(size(dp_raw))"))
+    iseven(s2) ||
+        throw(ArgumentError("pest3_decompose: dp_raw side must be 2m for integer m, got $s2"))
+    m = s2 ÷ 2
+    Tc = eltype(dp_raw)
+    Ap = zeros(Tc, m, m)
+    Bp = zeros(Tc, m, m)
+    Gp = zeros(Tc, m, m)
+    Dp = zeros(Tc, m, m)
+    for i in 1:m, j in 1:m
+        LL = dp_raw[2i-1, 2j-1]
+        LR = dp_raw[2i-1, 2j]
+        RL = dp_raw[2i,   2j-1]
+        RR = dp_raw[2i,   2j]
+        Ap[i, j] = RR + RL + LR + LL
+        Bp[i, j] = RR - RL + LR - LL
+        Gp[i, j] = RR + RL - LR - LL
+        Dp[i, j] = RR - RL - LR + LL
+    end
+    return (A=Ap, B=Bp, Γ=Gp, Δ=Dp)
+end
+
 """
     riccati_der!(du, u, params, psieval)
 
@@ -1641,7 +1713,7 @@ function _log_parallel_start(ctrl::ForceFreeStatesControl, odet::OdeState,
 end
 
 # Integrate each chunk's FM propagator from identity IC. Serial when bvp_threads == 1
-# (bit-deterministic; ~20% slower than 2-thread on DIII-D 147131 but immune to thread-
+# (bit-deterministic; ~20% slower than 2-thread but immune to thread-
 # schedule sensitivity). Parallel uses :static scheduler so Threads.threadid() returns a
 # stable index into odet_proxies. If a parallel run ever diverges on a delicate equilibrium,
 # drop to parallel_threads = 1 rather than use_parallel = false — the latter is silently wrong.
diff --git a/src/GeneralizedPerturbedEquilibrium.jl b/src/GeneralizedPerturbedEquilibrium.jl
index 004c3841b..854c7c959 100755
--- a/src/GeneralizedPerturbedEquilibrium.jl
+++ b/src/GeneralizedPerturbedEquilibrium.jl
@@ -28,9 +28,15 @@ include("ForceFreeStates/ForceFreeStates.jl")
 import .ForceFreeStates as ForceFreeStates
 export ForceFreeStates
 
-include("InnerLayer/InnerLayer.jl")
-import .InnerLayer as InnerLayer
-export InnerLayer
+include("Tearing/Tearing.jl")
+import .Tearing as Tearing
+export Tearing
+# Backward-compat top-level aliases so callers can still reach these
+# directly; the canonical nested path is `Tearing.{InnerLayer,Dispersion,Runner}`.
+import .Tearing.InnerLayer as InnerLayer
+import .Tearing.Dispersion as Dispersion
+import .Tearing.Runner as Runner
+export InnerLayer, Dispersion, Runner
 
 include("ForcingTerms/ForcingTerms.jl")
 import .ForcingTerms as ForcingTerms
@@ -52,7 +58,7 @@ include("Rerun.jl")
 
 # Import ForceFreeStates types and functions needed for main
 using .ForceFreeStates: ForceFreeStatesInternal, ForceFreeStatesControl, DebugSettings, VacuumData, OdeState, FourFitVars
-using .ForceFreeStates: sing_lim!, sing_find!
+using .ForceFreeStates: sing_lim!, sing_find!, resist_eval_all!, resist_geometry, ResistGeometry
 using .ForceFreeStates: compute_ballooning_stability!, ballooning_alpha_boundary, ballooning_alpha_boundaries
 using .ForceFreeStates: make_metric, make_matrix, make_kinetic_matrix
 using .ForceFreeStates: find_kinetic_singular_surfaces!
@@ -298,6 +304,14 @@ function main_from_inputs(
         end
     end
 
+    # Populate Glasser-Greene-Johnson geometric coefficients (E, F, G, H,
+    # K, M) for each surviving singular surface. Needed by the Julia GGJ
+    # inner-layer analysis; kinetic timescales (τ_A, τ_R) are layered on
+    # top by `build_ggj_inputs` using the same kinetic profiles as SLAYER.
+    if intr.msing > 0
+        ForceFreeStates.resist_eval_all!(intr, equil)
+    end
+
     # Determine poloidal mode numbers
     if ctrl.delta_mlow < 0 || ctrl.delta_mhigh < 0
         error("Negative delta_mlow or delta_mhigh not allowed")
@@ -454,10 +468,47 @@ function main_from_inputs(
 
     @info "Force-Free States completed in $(@sprintf("%.3f", time() - ffs_start)) s"
 
-    # Early exit if user only requested force-free states
+    # SLAYER tearing-mode analysis stage. Needs only equil + intr, so it runs in
+    # both the force_termination=true path and the full pipeline. `pe_file` is the
+    # HDF5 file PE wrote (to append into), or `nothing` if PE did not run.
+    function _run_slayer_stage(pe_file::Union{String,Nothing})
+        ("SLAYER" in keys(inputs)) || return nothing
+        # SLAYER is a post-processing diagnostic. A failure here must not
+        # discard the equilibrium / stability / PE results already computed,
+        # so the whole stage is guarded: on error we log loudly and return
+        # `nothing` for the `slayer` field rather than propagating.
+        try
+            slayer_ctrl = Runner.slayer_control_from_toml(inputs["SLAYER"])
+            slayer_ctrl.enabled || return nothing
+            @info "\n  SLAYER\n$_SECTION"
+            slayer_start = time()
+            result = Runner.run_slayer(equil, intr, slayer_ctrl;
+                dir_path=intr.dir_path)
+            @info "SLAYER completed in $(@sprintf("%.3f", time() - slayer_start)) s"
+            h5_filename = pe_file === nothing ? ctrl.HDF5_filename : pe_file
+            h5_path = joinpath(intr.dir_path, h5_filename)
+            # Append the slayer/ group; create the file if no prior stage wrote
+            # it (e.g. write_outputs_to_HDF5 disabled) rather than failing on "r+".
+            HDF5.h5open(h5_path, isfile(h5_path) ? "r+" : "w") do f
+                Runner.write_slayer_hdf5!(f, result)
+            end
+            @info "SLAYER results written to $h5_filename"
+            return result
+        catch err
+            @error "SLAYER stage failed; continuing without tearing results. " *
+                   "Equilibrium / stability / PE outputs are unaffected." exception =
+                (err, catch_backtrace())
+            return nothing
+        end
+    end
+
+    # Early exit if user only requested force-free states (SLAYER still runs).
     if ctrl.force_termination
+        slayer_result = _run_slayer_stage(nothing)
         @info "\n$_BANNER\n  GPEC completed successfully in $(@sprintf("%.3f", time() - total_start)) s\n$_BANNER"
-        return
+        return (ctrl=ctrl, equil=equil, intr=intr, ffit=ffit, odet=odet,
+            vac_data=ctrl.vac_flag ? vac_data : nothing,
+            slayer=slayer_result)
     end
 
     # ----------------------------------------------------------------
@@ -556,6 +607,18 @@ function main_from_inputs(
         @info "KineticForces completed in $(@sprintf("%.3f", time() - kf_start)) s"
     end
 
+    # ----------------------------------------------------------------
+    # SLAYER tearing-mode analysis (after PE so it appends to the PE output
+    # file; falls back to the ForceFreeStates file when PE did not run).
+    # ----------------------------------------------------------------
+    pe_file = if "PerturbedEquilibrium" in keys(inputs)
+        pe_out = get(inputs["PerturbedEquilibrium"], "output_filename", "")
+        isempty(pe_out) ? ctrl.HDF5_filename : pe_out
+    else
+        ctrl.HDF5_filename
+    end
+    slayer_result = _run_slayer_stage(pe_file)
+
     # ----------------------------------------------------------------
     # Done
     # ----------------------------------------------------------------
@@ -563,7 +626,9 @@ function main_from_inputs(
 
     # TODO: Do not allow perturbed equilibrium calculations if zero crossings are found
 
-    return (ctrl=ctrl, equil=equil, intr=intr, ffit=ffit, odet=odet, vac_data=ctrl.vac_flag ? vac_data : nothing)
+    return (ctrl=ctrl, equil=equil, intr=intr, ffit=ffit, odet=odet,
+        vac_data=ctrl.vac_flag ? vac_data : nothing,
+        slayer=slayer_result)
 
 end
 
@@ -727,6 +792,26 @@ function write_outputs_to_HDF5(
             end
             out_h5["singular/m"] = m_matrix
             out_h5["singular/n"] = n_matrix
+
+            # Glasser-Greene-Johnson geometric coefficients + surface averages
+            # (populated by ForceFreeStates.resist_eval_all! after sing_find!).
+            # Both kinetic-free (E, F, G, H, K, M) and geometry-only
+            # (avg_bsq_over_dpsisq, avg_bsq) quantities are written so
+            # downstream consumers (Tearing.InnerLayer.GGJ.build_ggj_inputs)
+            # can reconstruct τ_A / τ_R from any kinetic-profile source.
+            if all(s -> s.restype !== nothing, intr.sing)
+                out_h5["singular/E"] = [s.restype.E for s in intr.sing]
+                out_h5["singular/F"] = [s.restype.F for s in intr.sing]
+                out_h5["singular/G"] = [s.restype.G for s in intr.sing]
+                out_h5["singular/H"] = [s.restype.H for s in intr.sing]
+                out_h5["singular/K"] = [s.restype.K for s in intr.sing]
+                out_h5["singular/M"] = [s.restype.M for s in intr.sing]
+                out_h5["singular/avg_bsq_over_dpsisq"] = [s.restype.avg_bsq_over_dpsisq for s in intr.sing]
+                out_h5["singular/avg_bsq"] = [s.restype.avg_bsq for s in intr.sing]
+                out_h5["singular/p_local"] = [s.restype.p_local for s in intr.sing]
+                out_h5["singular/p1_local"] = [s.restype.p1_local for s in intr.sing]
+                out_h5["singular/v1_local"] = [s.restype.v1_local for s in intr.sing]
+            end
         end
 
         # Per-surface ca-based Δ' (`sing.delta_prime`) is a stub; only the BVP matrix is emitted (see SingType.delta_prime docstring).
@@ -737,6 +822,15 @@ function write_outputs_to_HDF5(
             out_h5["singular/delta_prime_matrix"] = intr.delta_prime_matrix
         end
 
+        # Write raw 2msing×2msing outer-region D' matrix in side-major ordering
+        # [L_s1, R_s1, L_s2, R_s2, …]. Byte-compatible with Fortran
+        # rdcon/gal.f::gal_write_delta top 2msing×2msing block of delta_gw.dat.
+        # Needed for the full det(D' − D(γ)) = 0 eigenvalue problem via
+        # pest3_decompose to recover (A', B', Γ', Δ').
+        if intr.msing > 0 && !isempty(intr.delta_prime_raw)
+            out_h5["singular/delta_prime_raw"] = intr.delta_prime_raw
+        end
+
         # Write kinetic singular surface data (det(F̄) near-zeros) and the cond(F̄) scan
         # used to find them. Populated only when kinetic crossings were searched for.
         out_h5["singular/kinetic/kmsing"] = intr.kmsing
diff --git a/src/InnerLayer/InnerLayerInterface.jl b/src/InnerLayer/InnerLayerInterface.jl
deleted file mode 100644
index 3c6e90109..000000000
--- a/src/InnerLayer/InnerLayerInterface.jl
+++ /dev/null
@@ -1,29 +0,0 @@
-# InnerLayerInterface.jl
-#
-# Abstract interface for resistive inner-layer models. Concrete models
-# (e.g. GGJ, SLAYER, kinetic) live in submodules and specialize `solve_inner`.
-
-"""
-    InnerLayerModel
-
-Abstract supertype for resistive inner-layer models. Each concrete model is a
-small, parameter-free type tag (often parameterized by a solver-choice symbol)
-that selects a `solve_inner` method.
-
-Implementations live in submodules of `InnerLayer`, e.g. `InnerLayer.GGJ`.
-"""
-abstract type InnerLayerModel end
-
-"""
-    solve_inner(model::InnerLayerModel, params, γ::ComplexF64; kwargs...) -> SVector{2,ComplexF64}
-
-Compute the parity-projected matching data `(Δ_odd, Δ_even)` for the given
-inner-layer `model`, physical parameters `params`, and complex growth rate
-`γ`. Concrete models specialize this function.
-
-The two returned components correspond to the homogeneous odd / even parity
-solutions of the half-domain inner-layer problem (parity boundary conditions
-imposed at the rational surface, X = 0). They are the Δ_{j,±}(γ) of
-Glasser, Wang & Park, Phys. Plasmas **23**, 112506 (2016), Eqs. (34)–(35).
-"""
-function solve_inner end
diff --git a/src/InnerLayer/SLAYER/Slayer.jl b/src/InnerLayer/SLAYER/Slayer.jl
deleted file mode 100644
index 5a7f87290..000000000
--- a/src/InnerLayer/SLAYER/Slayer.jl
+++ /dev/null
@@ -1,4 +0,0 @@
-# Slayer.jl
-#
-# Placeholder for the SLAYER (Slab Layer) drift-MHD two-fluid inner layer model.
-# Implementation pending.
diff --git a/src/KineticForces/Torque.jl b/src/KineticForces/Torque.jl
index 35d55a00f..fef1ad043 100644
--- a/src/KineticForces/Torque.jl
+++ b/src/KineticForces/Torque.jl
@@ -37,12 +37,6 @@ function tpsi!(tpsi_var::Ref{ComplexF64}, psi::Float64, n::Int, l::Int,
               imx_override::Union{Nothing,Float64}=nothing,
               atol_xlmda::Float64=1e-9, rtol_xlmda::Float64=1e-6)
 
-    if intr.verbose
-        println("torque - tpsi function, psi = ", psi)
-        println("  electron ", electron)
-        println("  ell ", l)
-    end
-
     # Enforce bounds
     if psi > 1
         tpsi_var[] = 0.0
@@ -196,14 +190,6 @@ function tpsi!(tpsi_var::Ref{ComplexF64}, psi::Float64, n::Int, l::Int,
     wdhat = q^3 * wtran^2 / (4 * epsr * wgyro) * wdfac
     nueff = nu_s / (2 * epsr)
 
-    if intr.verbose
-        @printf("   eq values = %.1e %.1e %.1e %.1e %.1e %.1e %.1f %d\n",
-               wdian, wdiat, welec, wdhat, wbhat, nueff, q, 0)
-    end
-
-    if intr.verbose
-        println("  method = ", method)
-    end
 
     # Method selection — route on the registry dispatch tag (errors on unknown method)
     kind = method_kind(method)
@@ -248,9 +234,6 @@ function tpsi!(tpsi_var::Ref{ComplexF64}, psi::Float64, n::Int, l::Int,
                                    rex_override=rex_override, imx_override=imx_override)
     end
 
-    if intr.verbose
-        println("torque - end function, psi = ", psi)
-    end
 
     return nothing
 end
diff --git a/src/Tearing/Dispersion/BruteForceScan.jl b/src/Tearing/Dispersion/BruteForceScan.jl
new file mode 100644
index 000000000..6e7b8aead
--- /dev/null
+++ b/src/Tearing/Dispersion/BruteForceScan.jl
@@ -0,0 +1,89 @@
+# BruteForceScan.jl
+#
+# Brute-force evaluation of a complex-Q-callable residual (`SurfaceCoupling`,
+# `MultiSurfaceCoupling`, or any user-supplied function) on a regular 2D
+# Q-plane grid. The output `ScanResult` is then consumed by
+# `find_growth_rates` (`GrowthRateExtraction.jl`) to extract growth-rate
+# eigenvalues from the Re(Δ)=0 ∩ Im(Δ)=0 contour intersections.
+#
+# Resolution and box are entirely user-controlled. Threading is enabled by
+# default; pass `threaded=false` for deterministic single-threaded
+# evaluation (e.g. when the residual is itself non-thread-safe).
+
+"""
+    ScanResult
+
+Output of a brute-force or AMR Q-plane scan.
+
+| field      | meaning                                           |
+|------------|---------------------------------------------------|
+| `Q`        | Complex Q values (`Matrix` for grid, `Vector` for AMR)   |
+| `Δ`        | Residual values, same shape as `Q`                       |
+| `re_axis`  | Real-axis grid (only for regular-grid `ScanResult`)      |
+| `im_axis`  | Imaginary-axis grid (only for regular-grid `ScanResult`) |
+"""
+struct ScanResult
+    Q::Matrix{ComplexF64}
+    Δ::Matrix{ComplexF64}
+    re_axis::Vector{Float64}
+    im_axis::Vector{Float64}
+end
+
+"""
+    brute_force_scan(f, Q_re_range, Q_im_range; nre, nim,
+                      threaded::Bool=true) -> ScanResult
+
+Evaluate the Q-callable residual `f` on a regular `nre × nim` grid spanning
+the rectangle `Q_re_range × Q_im_range` in the complex Q plane. `f` must
+accept a single `Complex` argument and return a `Complex` value (typically a
+`SurfaceCoupling` or `MultiSurfaceCoupling`, but any callable works).
+
+Use `find_growth_rates(scan, tauk; ...)` to extract growth-rate eigenvalues
+from the result.
+
+# Arguments
+
+  - `f`           -- Q-callable residual (e.g. `SurfaceCoupling`, `MultiSurfaceCoupling`)
+  - `Q_re_range`  -- `(re_min, re_max)` tuple
+  - `Q_im_range`  -- `(im_min, im_max)` tuple
+
+# Keyword arguments
+
+  - `nre`, `nim`  -- grid resolution along each axis
+  - `threaded`    -- distribute Q evaluations across `Threads.@threads`
+"""
+function brute_force_scan(f, Q_re_range::NTuple{2,<:Real},
+                          Q_im_range::NTuple{2,<:Real};
+                          nre::Integer, nim::Integer,
+                          threaded::Bool=true)
+    nre >= 2 || throw(ArgumentError("brute_force_scan: nre must be ≥ 2"))
+    nim >= 2 || throw(ArgumentError("brute_force_scan: nim must be ≥ 2"))
+    re_axis = collect(range(Float64(Q_re_range[1]); stop=Float64(Q_re_range[2]),
+                            length=nre))
+    im_axis = collect(range(Float64(Q_im_range[1]); stop=Float64(Q_im_range[2]),
+                            length=nim))
+    Q = ComplexF64[(qr + qi*im) for qr in re_axis, qi in im_axis]
+    Δ = Matrix{ComplexF64}(undef, nre, nim)
+    if threaded
+        # Pin BLAS to one thread for the parallel region — `f(Q)` calls `det()`,
+        # and concurrent multithreaded-BLAS calls give non-reproducible
+        # reductions that can flip the extracted root run-to-run. The residual
+        # matrices are tiny, so single-threaded BLAS costs nothing here.
+        _blas0 = BLAS.get_num_threads()
+        BLAS.set_num_threads(1)
+        try
+            Threads.@threads for j in 1:nim
+                for i in 1:nre
+                    Δ[i, j] = f(Q[i, j])
+                end
+            end
+        finally
+            BLAS.set_num_threads(_blas0)
+        end
+    else
+        for j in 1:nim, i in 1:nre
+            Δ[i, j] = f(Q[i, j])
+        end
+    end
+    return ScanResult(Q, Δ, re_axis, im_axis)
+end
diff --git a/src/Tearing/Dispersion/ContourSearchAMR.jl b/src/Tearing/Dispersion/ContourSearchAMR.jl
new file mode 100644
index 000000000..3533a1773
--- /dev/null
+++ b/src/Tearing/Dispersion/ContourSearchAMR.jl
@@ -0,0 +1,634 @@
+# ContourSearchAMR.jl
+#
+# Cell-based adaptive mesh refinement scanner of the complex Q plane.
+#
+# Each `AMRCell` is an axis-aligned rectangle holding its 4 corner Q values
+# and the corresponding Δ values evaluated by the user-supplied residual
+# `f(Q)`. After `passes` refinement steps, every cell that brackets a zero
+# in `Re(Δ)` or `Im(Δ)` has been subdivided into 4 quadrant children
+# carrying 5 freshly evaluated midpoint Δ values.
+#
+# All evaluations of `f(Q)` are deduplicated through a `Dict{ComplexF64,
+# ComplexF64}` hash cache so that adjacent cells sharing a corner (and
+# adjacent refinement levels sharing an edge midpoint) cost only one
+# evaluation.
+#
+# Output: `AMRResult` holds the final list of `AMRCell`s (preserving the
+# axis-aligned-rectangle structure that downstream marching-squares contour
+# extraction in `GrowthRateExtraction.jl` exploits) plus the flat
+# (Q::Vector, Δ::Vector) of all unique evaluations.
+
+# Corner ordering: 1 = BL, 2 = BR, 3 = TL, 4 = TR.
+
+"""
+    AMRCell
+
+A single axis-aligned-rectangle cell of an AMR scan. The four corner Q
+values (`q_bl`, `q_br`, `q_tl`, `q_tr`) and corresponding residual values
+(`d_bl`, `d_br`, `d_tl`, `d_tr`) are sufficient for marching-squares
+contour extraction.
+"""
+struct AMRCell
+    q_bl::ComplexF64
+    q_br::ComplexF64
+    q_tl::ComplexF64
+    q_tr::ComplexF64
+    d_bl::ComplexF64
+    d_br::ComplexF64
+    d_tl::ComplexF64
+    d_tr::ComplexF64
+end
+
+"""
+    AMRResult
+
+Output of `amr_scan`.
+
+| field       | meaning                                                  |
+|:----------- |:-------------------------------------------------------- |
+| `cells`     | Final list of `AMRCell` after all refinement passes      |
+| `Q`         | Flat `Vector{ComplexF64}` of every unique residual eval  |
+| `Δ`         | Corresponding `Vector{ComplexF64}` of residual values    |
+| `truncated` | `true` if `max_cells` was hit and remaining refinement   |
+|             | passes were skipped (`max_cells_action=:warn_truncate`). |
+|             | A `true` result is NOT fully converged — distinguish it  |
+|             | from a converged scan in convergence studies.            |
+"""
+struct AMRResult
+    cells::Vector{AMRCell}
+    Q::Vector{ComplexF64}
+    Δ::Vector{ComplexF64}
+    truncated::Bool
+end
+
+# Back-compat constructor: a scan that ran to completion is not truncated.
+AMRResult(cells::Vector{AMRCell}, Q::Vector{ComplexF64}, Δ::Vector{ComplexF64}) =
+    AMRResult(cells, Q, Δ, false)
+
+# Hash-cached residual evaluator. Returns the cached Δ value if `q` is
+# already known, otherwise evaluates `f(q)`, stores it, and returns it.
+@inline function _cached_eval!(cache::Dict{ComplexF64,ComplexF64},
+    f, q::ComplexF64)
+    haskey(cache, q) && return cache[q]
+    Δ = ComplexF64(f(q))
+    cache[q] = Δ
+    return Δ
+end
+
+# Parallel-friendly bulk filler: given a list of Q values, evaluates the
+# residual at each one that isn't already in `cache` and stores the result.
+# When `parallel=true` AND more than one Julia thread is available, the
+# evaluations run via `@threads`; the cache is populated serially afterward
+# to avoid Dict data races. Per-call evaluations of `f` are assumed to be
+# thread-safe (true for `mc_fort(Q)` which constructs its own local state).
+function _bulk_eval_into_cache!(cache::Dict{ComplexF64,ComplexF64}, f,
+    qs::AbstractVector{ComplexF64};
+    parallel::Bool)
+    # First pass: partition `qs` into already-cached vs new. Keep uniqueness.
+    seen = Set{ComplexF64}()
+    new_qs = Vector{ComplexF64}()
+    for q in qs
+        if !haskey(cache, q) && !(q in seen)
+            push!(new_qs, q)
+            push!(seen, q)
+        end
+    end
+    isempty(new_qs) && return
+    new_vals = Vector{ComplexF64}(undef, length(new_qs))
+    if parallel && Threads.nthreads() > 1
+        # Pin BLAS to one thread for the Julia-level parallel region. The
+        # residual `f(Q)` calls `det()` (LAPACK); concurrent multithreaded-BLAS
+        # calls from several Julia threads give non-reproducible reductions that
+        # perturb `f` at the ULP level — enough to flip the extracted root
+        # between near-degenerate solutions run-to-run. The residual matrices are
+        # tiny (≤ a few ×msing), so single-threaded BLAS costs nothing here.
+        _blas0 = BLAS.get_num_threads()
+        BLAS.set_num_threads(1)
+        try
+            Threads.@threads for k in eachindex(new_qs)
+                new_vals[k] = ComplexF64(f(new_qs[k]))
+            end
+        finally
+            BLAS.set_num_threads(_blas0)
+        end
+    else
+        @inbounds for k in eachindex(new_qs)
+            new_vals[k] = ComplexF64(f(new_qs[k]))
+        end
+    end
+    @inbounds for k in eachindex(new_qs)
+        cache[new_qs[k]] = new_vals[k]
+    end
+    return
+end
+
+# Sign-crossing test: does `vals` straddle zero? Used in both Re and Im
+# directions on a cell's 4 corners (mirrors check_cell_crossing_sub).
+@inline function _crosses_zero(vals)
+    # A non-finite corner (NaN/Inf) marks a divergence — typically a pole, where
+    # the inner-layer solver returns a NaN sentinel. Flag such cells as active so
+    # they are refined / pre-screened rather than silently dropped: a NaN corner
+    # makes both the product test below and the `abs >= threshold` pole check
+    # evaluate to false, which would hide the pole region entirely.
+    any(!isfinite, vals) && return true
+    return minimum(vals) * maximum(vals) <= 0
+end
+
+# Subdivide a parent cell into 4 quadrants, evaluating Δ at the 5
+# midpoints (BM, TM, LM, RM, MM) via the hash cache.
+function _subdivide_cell(parent::AMRCell,
+    cache::Dict{ComplexF64,ComplexF64}, f)
+    q_bm = 0.5 * (parent.q_bl + parent.q_br)
+    q_tm = 0.5 * (parent.q_tl + parent.q_tr)
+    q_lm = 0.5 * (parent.q_bl + parent.q_tl)
+    q_rm = 0.5 * (parent.q_br + parent.q_tr)
+    q_mm = 0.25 * (parent.q_bl + parent.q_br + parent.q_tl + parent.q_tr)
+
+    d_bm = _cached_eval!(cache, f, q_bm)
+    d_tm = _cached_eval!(cache, f, q_tm)
+    d_lm = _cached_eval!(cache, f, q_lm)
+    d_rm = _cached_eval!(cache, f, q_rm)
+    d_mm = _cached_eval!(cache, f, q_mm)
+
+    return (
+        AMRCell(parent.q_bl, q_bm, q_lm, q_mm,    # bottom-left quadrant
+            parent.d_bl, d_bm, d_lm, d_mm),
+        AMRCell(q_bm, parent.q_br, q_mm, q_rm,    # bottom-right quadrant
+            d_bm, parent.d_br, d_mm, d_rm),
+        AMRCell(q_lm, q_mm, parent.q_tl, q_tm,    # top-left quadrant
+            d_lm, d_mm, parent.d_tl, d_tm),
+        AMRCell(q_mm, q_rm, q_tm, parent.q_tr,    # top-right quadrant
+            d_mm, d_rm, d_tm, parent.d_tr)
+    )
+end
+
+"""
+    amr_scan(f, Q_re_range, Q_im_range;
+              nre0, nim0, passes,
+              max_cells=10_000_000,
+              max_cells_action=:error,
+              snapshot_callback=nothing,
+              parallel=Threads.nthreads() > 1) -> AMRResult
+
+Adaptively refine a Q-plane scan of the residual `f(Q)`. An initial
+`nre0 × nim0` axis-aligned grid of cells is built over `Q_re_range × Q_im_range` and `passes` rounds of refinement are applied. Each pass:
+
+ 1. flags any cell whose 4 corner residuals straddle zero in `Re(Δ)` or
+    `Im(Δ)`;
+ 2. subdivides each flagged cell into 4 quadrant children, evaluating `f`
+    at 5 new midpoints;
+ 3. unflagged cells are kept unchanged.
+
+All evaluations of `f` are deduplicated through a `Dict{ComplexF64, ComplexF64}` hash cache so that adjacent cells share a single evaluation
+per corner. The returned `AMRResult` carries both the final cell list (for
+marching-squares contour extraction) and the flat list of all unique Q/Δ
+evaluations.
+
+# Keyword arguments
+
+  - `nre0`, `nim0`   -- initial coarse-grid cell counts along each axis
+  - `passes`         -- number of refinement passes
+  - `max_cells`      -- safety cap on total cells; behavior on hit is set
+    by `max_cells_action`
+  - `max_cells_action` -- `:error` (raises) or `:warn_truncate` (logs a
+    warning and returns the partial result). The latter is useful for
+    convergence-vs-resolution studies where we deliberately push max_cells
+    and want graceful degradation. Default `:error` preserves the prior
+    safety-rail behaviour.
+  - `snapshot_callback` -- if not `nothing`, a function called after each
+    pass (and once for the initial grid, pass=0) with arguments
+    `(pass::Int, cells::Vector{AMRCell}, cache::Dict{ComplexF64,ComplexF64})`.
+    The callback receives live references — copy if you need persistence.
+    Used by convergence studies to extract intermediate γ at each pass count.
+  - `parallel`       -- evaluate `f` in parallel via `Threads.@threads` within
+    each phase (initial grid + each refinement pass). Defaults to `true`
+    when more than one Julia thread is available. Per-call evaluations of
+    `f` must be thread-safe. The result is deterministic regardless of thread
+    count: cache updates and cell-list construction stay serial, AND BLAS is
+    pinned to one thread during the parallel region so the `det()`-based
+    residual does not pick up non-reproducible multithreaded-LAPACK reductions
+    (which otherwise flip the extracted root between near-degenerate solutions).
+"""
+function amr_scan(f, Q_re_range::NTuple{2,<:Real},
+    Q_im_range::NTuple{2,<:Real};
+    nre0::Integer, nim0::Integer, passes::Integer,
+    max_cells::Integer=10_000_000,
+    max_cells_action::Symbol=:error,
+    snapshot_callback::Union{Nothing,Function}=nothing,
+    parallel::Bool=Threads.nthreads() > 1)
+    nre0 >= 1 || throw(ArgumentError("amr_scan: nre0 must be ≥ 1"))
+    nim0 >= 1 || throw(ArgumentError("amr_scan: nim0 must be ≥ 1"))
+    passes >= 0 || throw(ArgumentError("amr_scan: passes must be ≥ 0"))
+    max_cells_action in (:error, :warn_truncate) ||
+        throw(ArgumentError("amr_scan: max_cells_action must be :error or " *
+                            ":warn_truncate, got :$max_cells_action"))
+
+    re_lo, re_hi = Float64.(Q_re_range)
+    im_lo, im_hi = Float64.(Q_im_range)
+    re_step = (re_hi - re_lo) / nre0
+    im_step = (im_hi - im_lo) / nim0
+
+    cache = Dict{ComplexF64,ComplexF64}()
+
+    # ---- 1. coarse initial grid (nre0 × nim0 cells, (nre0+1)·(nim0+1) corners)
+    # Collect every corner Q, evaluate in parallel, then build the cells using
+    # cache lookups (no further evaluation happens in the build step).
+    ncorners_x = nre0 + 1
+    ncorners_y = nim0 + 1
+    corners = Vector{ComplexF64}(undef, ncorners_x * ncorners_y)
+    @inbounds for j in 0:nim0, i in 0:nre0
+        corners[j*ncorners_x+i+1] =
+            ComplexF64(re_lo + i * re_step, im_lo + j * im_step)
+    end
+    _bulk_eval_into_cache!(cache, f, corners; parallel=parallel)
+
+    cells = Vector{AMRCell}(undef, nre0 * nim0)
+    @inbounds for j in 0:nim0-1, i in 0:nre0-1
+        # Read corner Q values from the same `corners` array used to populate
+        # the cache. Recomputing them with `x + re_step` here would differ in
+        # the last floating-point bit from the cache keys, causing spurious
+        # KeyErrors on lookup.
+        q_bl = corners[j*ncorners_x+i+1]
+        q_br = corners[j*ncorners_x+(i+1)+1]
+        q_tl = corners[(j+1)*ncorners_x+i+1]
+        q_tr = corners[(j+1)*ncorners_x+(i+1)+1]
+        cells[j*nre0+i+1] = AMRCell(q_bl, q_br, q_tl, q_tr,
+            cache[q_bl], cache[q_br],
+            cache[q_tl], cache[q_tr])
+    end
+
+    # Snapshot the initial grid (pass 0) before any refinement.
+    snapshot_callback === nothing || snapshot_callback(0, cells, cache)
+
+    # ---- 2. refinement passes
+    truncated = false   # set true when max_cells is hit and action == :warn_truncate
+    for pass_idx in 1:passes
+        truncated && break
+        # Phase A: identify flagged parent cells and collect the midpoints we
+        # need to evaluate. The 5 midpoints per parent (BM, TM, LM, RM, MM)
+        # mirror _subdivide_cell's coordinates exactly.
+        flagged_idx = Int[]
+        new_qs = Vector{ComplexF64}()
+        sizehint!(new_qs, length(cells))
+        for (idx, cell) in enumerate(cells)
+            re_corners = (real(cell.d_bl), real(cell.d_br),
+                real(cell.d_tl), real(cell.d_tr))
+            im_corners = (imag(cell.d_bl), imag(cell.d_br),
+                imag(cell.d_tl), imag(cell.d_tr))
+            if _crosses_zero(re_corners) || _crosses_zero(im_corners)
+                push!(flagged_idx, idx)
+                push!(new_qs, 0.5 * (cell.q_bl + cell.q_br))
+                push!(new_qs, 0.5 * (cell.q_tl + cell.q_tr))
+                push!(new_qs, 0.5 * (cell.q_bl + cell.q_tl))
+                push!(new_qs, 0.5 * (cell.q_br + cell.q_tr))
+                push!(new_qs, 0.25 * (cell.q_bl + cell.q_br +
+                                      cell.q_tl + cell.q_tr))
+            end
+        end
+
+        # Phase B: evaluate all new midpoints in parallel, fill the cache.
+        _bulk_eval_into_cache!(cache, f, new_qs; parallel=parallel)
+
+        # Phase C: build the refined cell list using cache lookups.
+        new_cells = Vector{AMRCell}()
+        sizehint!(new_cells, length(cells) + 3 * length(flagged_idx))
+        flagged_set = Set(flagged_idx)
+        skip_remaining = false   # true once max_cells is hit (warn_truncate path)
+        for (idx, cell) in enumerate(cells)
+            if idx in flagged_set && !skip_remaining
+                q_bm = 0.5 * (cell.q_bl + cell.q_br)
+                q_tm = 0.5 * (cell.q_tl + cell.q_tr)
+                q_lm = 0.5 * (cell.q_bl + cell.q_tl)
+                q_rm = 0.5 * (cell.q_br + cell.q_tr)
+                q_mm = 0.25 * (cell.q_bl + cell.q_br +
+                               cell.q_tl + cell.q_tr)
+                d_bm = cache[q_bm]
+                d_tm = cache[q_tm]
+                d_lm = cache[q_lm]
+                d_rm = cache[q_rm]
+                d_mm = cache[q_mm]
+                push!(new_cells,
+                    AMRCell(cell.q_bl, q_bm, q_lm, q_mm,
+                        cell.d_bl, d_bm, d_lm, d_mm),
+                    AMRCell(q_bm, cell.q_br, q_mm, q_rm,
+                        d_bm, cell.d_br, d_mm, d_rm),
+                    AMRCell(q_lm, q_mm, cell.q_tl, q_tm,
+                        d_lm, d_mm, cell.d_tl, d_tm),
+                    AMRCell(q_mm, q_rm, q_tm, cell.q_tr,
+                        d_mm, d_rm, d_tm, cell.d_tr))
+            else
+                push!(new_cells, cell)
+            end
+            if length(new_cells) > max_cells
+                if max_cells_action === :error
+                    error(
+                        "amr_scan: exceeded max_cells=$max_cells " *
+                        "(currently $(length(new_cells))). Reduce " *
+                        "`passes` or raise `max_cells`, or pass " *
+                        "max_cells_action=:warn_truncate to truncate gracefully."
+                    )
+                else  # :warn_truncate (validated at function entry)
+                    @warn "amr_scan: max_cells=$max_cells reached at pass=$pass_idx cell=$idx/$(length(cells)); truncating refinement here and skipping remaining passes"
+                    skip_remaining = true
+                    truncated = true
+                end
+            end
+        end
+        cells = new_cells
+        # Snapshot after this pass.
+        snapshot_callback === nothing || snapshot_callback(pass_idx, cells, cache)
+    end
+
+    # ---- 3. flatten the cache into output Q/Δ vectors
+    n = length(cache)
+    Q = Vector{ComplexF64}(undef, n)
+    Δ = Vector{ComplexF64}(undef, n)
+    for (k, (q, d)) in enumerate(cache)
+        Q[k] = q
+        Δ[k] = d
+    end
+
+    return AMRResult(cells, Q, Δ, truncated)
+end
+
+# =============================================================================
+# Multi-box AMR scan with pre-screen
+# =============================================================================
+#
+# Motivation. A single wide AMR box (e.g. ω ∈ [-100, +100] kHz, γ ∈ [-25, +25])
+# spends most of its evaluations on regions that contain neither roots nor
+# poles. Splitting the same area into several smaller boxes and pre-screening
+# each on a coarse 25×25 grid lets us skip refinement on inactive boxes
+# entirely, while keeping full AMR sensitivity on the active ones.
+#
+# A box is flagged ACTIVE if any cell of its pre-screen grid satisfies AT LEAST
+# ONE of:
+#   - sign change in Re(Δ) across the cell's 4 corners (zero-isoline of Re(Δ)
+#     crosses the cell — root candidate);
+#   - sign change in Im(Δ) across the cell's 4 corners (zero-isoline of Im(Δ)
+#     crosses the cell — root candidate);
+#   - any corner with |Δ| ≥ `pole_magnitude_threshold` (likely pole inside or
+#     near the box; sign-only criteria miss poles unless their fringe sign
+#     change happens to land inside the pre-screen resolution).
+#
+# The pole-magnitude criterion is essential: a tight pole tucked inside one
+# pre-screen cell can leave all four corners with the same large-magnitude sign
+# (because Re(Δ) and Im(Δ) flip together as you orbit the pole, and at the
+# corners we may sample the same lobe), so the sign-change tests would miss it.
+
+"""
+    BoxActivity
+
+Why a box was retained or skipped by `multi_box_amr_scan`. `NoActivity` means
+the pre-screen grid showed no zero-isoline crossings and no large-`|Δ|`
+corners; the box is excluded from refinement. The other variants record which
+criterion fired first.
+"""
+@enum BoxActivity NoActivity ReZeroCrossing ImZeroCrossing PoleMagnitude
+
+# Pre-screen activity check: scan the pre-built cells and return the first
+# satisfied criterion (or NoActivity if none fire). Designed for early exit so
+# fully-quiet boxes cost just enough cell scans to confirm.
+function _check_box_activity(cells::AbstractVector{AMRCell},
+    pole_magnitude_threshold::Real)
+    @inbounds for cell in cells
+        re_corners = (real(cell.d_bl), real(cell.d_br),
+            real(cell.d_tl), real(cell.d_tr))
+        im_corners = (imag(cell.d_bl), imag(cell.d_br),
+            imag(cell.d_tl), imag(cell.d_tr))
+        _crosses_zero(re_corners) && return ReZeroCrossing
+        _crosses_zero(im_corners) && return ImZeroCrossing
+        if max(abs(cell.d_bl), abs(cell.d_br),
+            abs(cell.d_tl), abs(cell.d_tr)) >= pole_magnitude_threshold
+            return PoleMagnitude
+        end
+    end
+    return NoActivity
+end
+
+"""
+    MultiBoxAMRResult
+
+Output of `multi_box_amr_scan`. Per-box `AMRResult`s plus the aggregated
+cells/Q/Δ across all *active* boxes. Pre-screen-inactive boxes have `nothing`
+for their `AMRResult` and contribute nothing to the aggregated arrays.
+
+| field             | meaning                                                |
+|:----------------- |:------------------------------------------------------ |
+| `box_results`     | per-box `AMRResult`, or `nothing` if box was skipped   |
+| `box_activity`    | per-box `BoxActivity` enum                             |
+| `cells`           | concatenated `AMRCell`s from all active boxes          |
+| `Q`               | union of all unique `Q` evaluations (active + skipped) |
+| `Δ`               | corresponding `Δ` values                               |
+| `prescreen_evals` | total `f(Q)` evaluations spent on pre-screening        |
+
+The aggregated `(cells, Q, Δ)` are suitable for direct consumption by
+`find_growth_rates`. Pre-screen evaluations are still included in `Q`/`Δ` even
+for skipped boxes, so any downstream pole-magnitude diagnostic that uses the
+flat residual list sees the full sample.
+"""
+struct MultiBoxAMRResult
+    box_results::Vector{Union{Nothing,AMRResult}}
+    box_activity::Vector{BoxActivity}
+    cells::Vector{AMRCell}
+    Q::Vector{ComplexF64}
+    Δ::Vector{ComplexF64}
+    prescreen_evals::Int
+end
+
+"""
+    multi_box_amr_scan(f, boxes;
+                       pole_magnitude_threshold,
+                       prescreen_nre=25, prescreen_nim=25,
+                       nre0=25, nim0=25, passes=4,
+                       max_cells=10_000_000,
+                       max_cells_action=:error,
+                       parallel=Threads.nthreads() > 1) -> MultiBoxAMRResult
+
+Run `amr_scan` over multiple Q-plane boxes with a coarse pre-screen step that
+skips inactive boxes entirely. The typical use case is the three-stripe ω-axis
+scan for SLAYER coupled tearing dispersion:
+
+    ω ∈ [-75, -25],  γ ∈ [-25, +25]   (left stripe)
+    ω ∈ [-25, +25],  γ ∈ [-25, +25]   (centre stripe)
+    ω ∈ [+25, +75],  γ ∈ [-25, +25]   (right stripe)
+
+A single 150×50 box is wasteful when the dispersion is concentrated near a
+narrow ω band; splitting into stripes and pre-screening lets the AMR effort
+land on the active stripe.
+
+# Pre-screen logic
+
+Each box is sampled on a `prescreen_nre × prescreen_nim` corner grid (default
+25×25, matching the typical AMR initial-grid resolution). A box is ACTIVE if
+ANY pre-screen cell satisfies at least one criterion:
+
+ 1. sign change of `Re(Δ)` across the cell's 4 corners (zero-isoline of
+    `Re(Δ)` crosses the cell — root candidate);
+ 2. sign change of `Im(Δ)` across the cell's 4 corners (zero-isoline of
+    `Im(Δ)` crosses the cell — root candidate);
+ 3. any corner with `|Δ| ≥ pole_magnitude_threshold` (likely pole — the
+    sign-only criteria miss poles whose fringe doesn't straddle a corner).
+
+Active boxes get the full `amr_scan` treatment. Inactive boxes are dropped
+(their `AMRResult` is `nothing`).
+
+# Arguments
+
+  - `f`: residual function `Q::ComplexF64 → Δ::ComplexF64`. Must be thread-safe
+    if `parallel=true`.
+  - `boxes`: vector of `(Q_re_range, Q_im_range)` tuples, one per box. Boxes
+    may overlap or share boundaries; the aggregator deduplicates Q values.
+
+# Required keyword
+
+  - `pole_magnitude_threshold`: activity threshold for `|Δ|`. A natural choice
+    is `≈ |mean(Δ)|` from a baseline (or the same value used for adaptive
+    pole_threshold in `find_growth_rates`).
+
+# Optional keywords
+
+  - `prescreen_nre`, `prescreen_nim` (default 25 each): pre-screen grid
+    resolution. Coarser misses small features; finer wastes evaluations on
+    inactive boxes.
+  - `nre0, nim0, passes, max_cells, max_cells_action, parallel`: forwarded to
+    each per-box `amr_scan` call. Defaults match `amr_scan`.
+
+# Returns
+
+A `MultiBoxAMRResult`. The aggregated `(cells, Q, Δ)` can be wrapped in an
+`AMRResult` (helper `as_amr_result` below) for direct use with
+`find_growth_rates`.
+
+# Notes / TODO
+
+  - Each per-box `amr_scan` rebuilds its own cache, so the 25×25 pre-screen
+    corners get re-evaluated by the AMR initial pass on active boxes
+    (≈ 676 wasted evals per active box). A future refactor could thread a
+    shared cache through `amr_scan`. For now the cost is small relative to
+    the AMR refinement evals.
+  - Boxes that share a boundary line (e.g. the three ω-stripe layout above)
+    duplicate ≈ `prescreen_nim+1` corner evaluations per shared edge. Also
+    small.
+
+# Example
+
+```julia
+boxes = [((-75.0, -25.0), (-25.0, 25.0)),
+    ((-25.0, 25.0), (-25.0, 25.0)),
+    ((25.0, 75.0), (-25.0, 25.0))]
+result = multi_box_amr_scan(f_residual, boxes;
+    pole_magnitude_threshold=1e-3,
+    prescreen_nre=25, prescreen_nim=25,
+    nre0=25, nim0=25, passes=4)
+amr = AMRResult(result.cells, result.Q, result.Δ)
+roots = find_growth_rates(amr, tauk; pole_threshold=1e-3)
+```
+"""
+function multi_box_amr_scan(f,
+    boxes::AbstractVector;
+    pole_magnitude_threshold::Real,
+    prescreen_nre::Integer=25, prescreen_nim::Integer=25,
+    nre0::Integer=25, nim0::Integer=25, passes::Integer=4,
+    max_cells::Integer=10_000_000,
+    max_cells_action::Symbol=:error,
+    parallel::Bool=Threads.nthreads() > 1)
+    prescreen_nre >= 1 || throw(ArgumentError("multi_box_amr_scan: prescreen_nre must be ≥ 1"))
+    prescreen_nim >= 1 || throw(ArgumentError("multi_box_amr_scan: prescreen_nim must be ≥ 1"))
+    pole_magnitude_threshold >= 0 ||
+        throw(ArgumentError("multi_box_amr_scan: pole_magnitude_threshold must be ≥ 0"))
+
+    n_boxes = length(boxes)
+    box_results = Vector{Union{Nothing,AMRResult}}(undef, n_boxes)
+    box_activity = Vector{BoxActivity}(undef, n_boxes)
+    prescreen_evals_total = 0
+
+    # Aggregator: dedupe Q/Δ across all per-box caches and the pre-screen samples.
+    # Using a Dict keyed by Q gives O(1) dedup and lets us merge results in any
+    # order. We also collect cells (from active boxes only) for downstream
+    # marching-squares extraction.
+    qd_aggregate = Dict{ComplexF64,ComplexF64}()
+    cells_aggregate = AMRCell[]
+
+    for (b_idx, box) in enumerate(boxes)
+        Q_re_range, Q_im_range = box
+        re_lo, re_hi = Float64.(Q_re_range)
+        im_lo, im_hi = Float64.(Q_im_range)
+        re_step = (re_hi - re_lo) / prescreen_nre
+        im_step = (im_hi - im_lo) / prescreen_nim
+        ncorners_x = prescreen_nre + 1
+        ncorners_y = prescreen_nim + 1
+
+        # Pre-screen corners for THIS box. Local cache so we can both drive the
+        # activity check and feed into the aggregate without polluting an
+        # eventual per-box AMR cache.
+        box_cache = Dict{ComplexF64,ComplexF64}()
+        corners = Vector{ComplexF64}(undef, ncorners_x * ncorners_y)
+        @inbounds for j in 0:prescreen_nim, i in 0:prescreen_nre
+            corners[j*ncorners_x+i+1] =
+                ComplexF64(re_lo + i * re_step, im_lo + j * im_step)
+        end
+        _bulk_eval_into_cache!(box_cache, f, corners; parallel=parallel)
+        prescreen_evals_total += length(box_cache)
+
+        # Build pre-screen cells
+        ps_cells = Vector{AMRCell}(undef, prescreen_nre * prescreen_nim)
+        @inbounds for j in 0:prescreen_nim-1, i in 0:prescreen_nre-1
+            q_bl = corners[j*ncorners_x+i+1]
+            q_br = corners[j*ncorners_x+(i+1)+1]
+            q_tl = corners[(j+1)*ncorners_x+i+1]
+            q_tr = corners[(j+1)*ncorners_x+(i+1)+1]
+            ps_cells[j*prescreen_nre+i+1] =
+                AMRCell(q_bl, q_br, q_tl, q_tr,
+                    box_cache[q_bl], box_cache[q_br],
+                    box_cache[q_tl], box_cache[q_tr])
+        end
+
+        # Activity check
+        activity = _check_box_activity(ps_cells, pole_magnitude_threshold)
+        box_activity[b_idx] = activity
+
+        # Merge pre-screen evals into aggregate (for both active and skipped
+        # boxes — diagnostics see all samples).
+        for (q, d) in box_cache
+            qd_aggregate[q] = d
+        end
+
+        if activity == NoActivity
+            box_results[b_idx] = nothing
+        else
+            res = amr_scan(f, Q_re_range, Q_im_range;
+                nre0=nre0, nim0=nim0, passes=passes,
+                max_cells=max_cells,
+                max_cells_action=max_cells_action,
+                parallel=parallel)
+            box_results[b_idx] = res
+            append!(cells_aggregate, res.cells)
+            for k in eachindex(res.Q)
+                qd_aggregate[res.Q[k]] = res.Δ[k]
+            end
+        end
+    end
+
+    # Flatten aggregator
+    n = length(qd_aggregate)
+    Q_all = Vector{ComplexF64}(undef, n)
+    Δ_all = Vector{ComplexF64}(undef, n)
+    for (k, (q, d)) in enumerate(qd_aggregate)
+        Q_all[k] = q
+        Δ_all[k] = d
+    end
+
+    return MultiBoxAMRResult(box_results, box_activity, cells_aggregate,
+        Q_all, Δ_all, prescreen_evals_total)
+end
+
+"""
+    as_amr_result(mbres::MultiBoxAMRResult) -> AMRResult
+
+Wrap the aggregated cells/Q/Δ from a multi-box scan as a plain `AMRResult` so
+it can be passed directly to `find_growth_rates(::AMRResult, tauk; ...)`.
+"""
+as_amr_result(mbres::MultiBoxAMRResult) =
+    AMRResult(mbres.cells, mbres.Q, mbres.Δ,
+        any(r -> r !== nothing && r.truncated, mbres.box_results))
diff --git a/src/Tearing/Dispersion/Coupled.jl b/src/Tearing/Dispersion/Coupled.jl
new file mode 100644
index 000000000..4029943b9
--- /dev/null
+++ b/src/Tearing/Dispersion/Coupled.jl
@@ -0,0 +1,110 @@
+# Coupled.jl
+#
+# Multi-surface coupled tearing dispersion residual `det(M(Q))` (the
+# coupled, tearing-only reduction of the full Pletzer-Dewar matching in
+# `CoupledFullMatch.jl`). Shares the per-surface `SurfaceCoupling`
+# Q-callable interface so a brute-force or AMR scan can evaluate either
+# residual identically.
+#
+# Construction:
+#
+#   mc = multi_surface_coupling(surfaces, dp_matrix; ref_idx=1, msing_max=...)
+#
+# Evaluation:
+#
+#   det = mc(Q::ComplexF64)
+#
+# At each evaluation, for k = 1 .. msing_max, the inner-layer Δ is computed
+# at a Q rescaled by `tauk_ref / tauk_k`, then subtracted (with the dc
+# offset) from the diagonal of an `msing_max × msing_max` upper-left
+# submatrix of `dp_matrix`. The off-diagonal Δ' couplings are passed
+# through unchanged.
+
+"""
+    MultiSurfaceCoupling{V<:AbstractVector{<:SurfaceCoupling}}
+
+Multi-surface dispersion data: a vector of `SurfaceCoupling`, the full Δ'
+matrix, the index of the reference surface (whose `tauk` defines the Q
+normalization), and the truncation `msing_max` (number of surfaces actually
+participating in the determinant). Calling `mc(Q)` returns `det(M(Q))` where
+
+```
+M[k,k] = dp_matrix[k,k] - scale_k · Δ_inner_k(Q · tauk_ref / tauk_k) - dc_k
+M[i,j] = dp_matrix[i,j]      for i ≠ j        (off-diagonal Δ' couplings)
+```
+
+A root of `mc` in the complex `Q` plane is a coupled tearing eigenvalue.
+"""
+struct MultiSurfaceCoupling{V<:AbstractVector{<:SurfaceCoupling}}
+    surfaces::V
+    dp_matrix::Matrix{ComplexF64}
+    ref_idx::Int
+    msing_max::Int
+end
+
+"""
+    multi_surface_coupling(surfaces, dp_matrix;
+                            ref_idx=1,
+                            msing_max=min(3, length(surfaces)))
+        -> MultiSurfaceCoupling
+
+Construct a multi-surface coupling from a vector of `SurfaceCoupling` and
+the full outer-region Δ' matrix. `dp_matrix` must be square with side
+length `length(surfaces)` (it is the same matrix returned by
+`PerturbedEquilibrium.SingularCoupling`'s Δ' boundary-value problem).
+
+# Keyword arguments
+
+  - `ref_idx`   -- index of the reference surface whose `tauk` defines the
+    Q normalization. Defaults to `1`.
+  - `msing_max` -- number of surfaces from the front of `surfaces` to
+    include in the determinant. Defaults to `min(3, length(surfaces))`:
+    Δ' off-diagonal couplings beyond the third surface tend to be erratic
+    in practice, so the determinant is conservatively truncated to the
+    upper-left `msing_max × msing_max` submatrix of `dp_matrix`. Set
+    explicitly (up to `length(surfaces)`) to override.
+"""
+function multi_surface_coupling(surfaces::AbstractVector{<:SurfaceCoupling},
+    dp_matrix::AbstractMatrix;
+    ref_idx::Integer=1,
+    msing_max::Integer=min(3, length(surfaces)))
+    n = length(surfaces)
+    size(dp_matrix) == (n, n) ||
+        throw(ArgumentError("multi_surface_coupling: dp_matrix size " *
+                            "$(size(dp_matrix)) ≠ ($n, $n)"))
+    1 <= ref_idx <= n ||
+        throw(ArgumentError("multi_surface_coupling: ref_idx=$ref_idx out " *
+                            "of range 1:$n"))
+    1 <= msing_max <= n ||
+        throw(ArgumentError("multi_surface_coupling: msing_max=$msing_max " *
+                            "out of range 1:$n"))
+    return MultiSurfaceCoupling(surfaces,
+        Matrix{ComplexF64}(dp_matrix),
+        Int(ref_idx), Int(msing_max))
+end
+
+function (mc::MultiSurfaceCoupling)(Q::Number)
+    n = mc.msing_max
+    Qc = ComplexF64(Q)
+    ref_tauk = mc.surfaces[mc.ref_idx].tauk
+
+    M = mc.dp_matrix[1:n, 1:n]
+    @inbounds for k in 1:n
+        sc = mc.surfaces[k]
+        Q_k = Qc * (ref_tauk / sc.tauk)
+        # m×m scalar coupling: use only the tearing channel. The
+        # interchange (Glasser-stabilization) channel is carried in the
+        # full 4m×4m dispersion in `CoupledFullMatch.jl`; this reduced
+        # form is equivalent for pressureless SLAYER surfaces
+        # (Δ_interchange=0) and approximate for GGJ surfaces (drops
+        # Glasser stabilization).
+        Δ_k = solve_inner(sc.model, sc.params, Q_k).tearing * sc.scale
+        # The inner-layer solver returns NaN when the Riccati integration fails
+        # (clustered near poles). Propagate it as the residual so the scan flags
+        # this Q-cell via its isfinite checks, rather than feeding NaN into the
+        # LU factorization where it silently contaminates the whole determinant.
+        isfinite(Δ_k) || return ComplexF64(NaN, NaN)
+        M[k, k] -= Δ_k + sc.dc
+    end
+    return det(M)
+end
diff --git a/src/Tearing/Dispersion/CoupledFullMatch.jl b/src/Tearing/Dispersion/CoupledFullMatch.jl
new file mode 100644
index 000000000..8bf5fb97c
--- /dev/null
+++ b/src/Tearing/Dispersion/CoupledFullMatch.jl
@@ -0,0 +1,214 @@
+# CoupledFullMatch.jl
+#
+# Full Pletzer-Dewar 4m × 4m tearing+interchange dispersion matrix, with
+# the m inner-layer resonances decoupled via the matching-identity rows
+#
+#     C^j_L = d^j_+ − d^j_-
+#     C^j_R = -(d^j_+ + d^j_-)
+#
+# (see Wang-Glasser-Brennan-Liu-Park 2020, Phys. Plasmas **27**, 122503,
+# Eq. (11a)-(11d) and Glasser-Wang-Park 2016, Phys. Plasmas **23**, 112506,
+# Eq. (36)-(40); original matching construction Pletzer & Dewar 1991,
+# J. Plasma Phys. **45**, 427).
+#
+# Why 4m × 4m and not 2m × 2m?
+#
+#   The outer-region matching matrix D' (Julia `intr.delta_prime_raw`) is
+#   expressed in the side-major basis `[L_s1, R_s1, L_s2, R_s2, …]` of
+#   large-solution driving amplitudes. The inner-layer Galerkin solver
+#   (`solve_inner(GGJModel, …)`) returns Δ_tearing and Δ_interchange in
+#   the even/odd parity (+/−) basis instead. The naive relation
+#   `det(D' − diag(Δ_+, Δ_-)) = 0` cannot be written directly because
+#   the two quantities live in different bases. The fix is to introduce
+#   both sets of amplitudes (`C^j_{L,R}` for outer, `d^j_±` for inner) as
+#   explicit unknowns and use the ±1 matching identity as two extra rows
+#   per surface, yielding the 4m × 4m linear system. A naive 2m × 2m
+#   `det(D' − diag(Δ_+, Δ_-))` form cannot work here: it subtracts the
+#   inner Δ (parity ± basis) from the outer D' (side-major L/R basis), two
+#   quantities living in different bases, producing a determinant with
+#   structurally-wrong magnitude and topology. This module reproduces the
+#   full Pletzer-Dewar result.
+#
+# Per surface `k` (1-indexed), the 4 block indices are
+#
+#     idx1 = 2k − 1                      (row/col for C^k_L)
+#     idx2 = 2k                          (row/col for C^k_R)
+#     idx3 = idx1 + 2m                   (row/col for d^k_+)
+#     idx4 = idx2 + 2m                   (row/col for d^k_-)
+#
+# The global 4m × 4m matrix has:
+#
+#   - lower-left 2m × 2m block = transpose(dp_raw)
+#   - upper-left 2m × 2m block: per-surface 2 × 2 identity
+#   - upper-right 2m × 2m block: per-surface 2 × 2 matching identity
+#   - lower-right 2m × 2m block: per-surface 2 × 2 inner Δ block
+#
+# See the per-surface fill table in the body of `(::MultiSurfaceCouplingFull)`.
+
+"""
+    MultiSurfaceCouplingFull{V<:AbstractVector{<:SurfaceCoupling}}
+
+Full 4m × 4m tearing+interchange Pletzer-Dewar dispersion matrix
+(Wang et al. 2020, Phys. Plasmas **27**, 122503).
+
+Given the raw 2m × 2m outer-region matrix `dp_raw` (side-major ordering
+`[L_s1, R_s1, L_s2, R_s2, …]`, from `intr.delta_prime_raw`) and a vector
+of `SurfaceCoupling` (each containing the inner-layer model and
+parameters), calling `mc(Q)` assembles the 4m × 4m Pletzer-Dewar
+matching matrix and returns `det(mat)`.
+
+This is the correct Pletzer-Dewar dispersion relation for
+tearing+interchange coupling. A naive 2m × 2m `det(D' − D(γ))` form is
+not equivalent: it subtracts the inner Δ (parity ± basis) from the outer
+D' (side-major L/R basis), mixing two different bases. The 4m × 4m
+matching system introduced here keeps the bases separate via the explicit
+`C^j_{L,R}` / `d^j_±` unknowns. For pure-tearing (pressureless SLAYER)
+studies use the reduced m × m `MultiSurfaceCoupling` instead.
+
+# Fields
+
+  - `surfaces::V`               — per-surface `SurfaceCoupling`.
+  - `dp_raw::Matrix{ComplexF64}` — 2m × 2m outer-region matrix (side-major).
+  - `ref_idx::Int`              — reference surface for Q rescaling (1-based).
+  - `msing_max::Int`            — number of surfaces to include (truncates).
+  - `rotation::Vector{Float64}` — per-surface rotation frequencies (s⁻¹).
+  - `ntor::Int`                 — toroidal mode number `n` (default 1).
+"""
+struct MultiSurfaceCouplingFull{V<:AbstractVector{<:SurfaceCoupling},K<:NamedTuple}
+    surfaces::V
+    dp_raw::Matrix{ComplexF64}
+    ref_idx::Int
+    msing_max::Int
+    rotation::Vector{Float64}
+    ntor::Int
+    inner_kwargs::K    # kwargs forwarded to solve_inner; e.g. (pfac=0.1, nx=128, nq=5)
+end
+
+"""
+    multi_surface_coupling_full(surfaces, dp_raw;
+                                ref_idx=1,
+                                msing_max=length(surfaces),
+                                rotation=zeros(length(surfaces)),
+                                ntor=1) -> MultiSurfaceCouplingFull
+
+Construct the 4m × 4m dispersion matrix driver. `dp_raw` must be the
+2m × 2m matrix in side-major ordering (the `intr.delta_prime_raw`
+field populated by `ForceFreeStates.compute_delta_prime_matrix!` on the
+`use_parallel=true` path). `rotation[k]` is the per-surface rotation
+frequency; it shifts the per-surface inner Q argument by
+`i·ntor·rotation[k]`. Default zero rotation matches the static-equilibrium
+case.
+
+# Keyword arguments
+
+  - `ref_idx`   — index of the reference surface whose `tauk` defines the
+    Q normalization (1 ≤ ref_idx ≤ m). Defaults to 1.
+  - `msing_max` — truncate to the leading `msing_max` surfaces; the
+    matching matrix becomes 4·msing_max × 4·msing_max, built from the
+    corresponding 2·msing_max × 2·msing_max submatrix of `dp_raw`.
+    Defaults to `length(surfaces)`.
+  - `rotation`  — per-surface rotation frequencies in s⁻¹ (length m).
+    Defaults to all zero.
+  - `ntor`      — toroidal mode number n. Defaults to 1.
+  - `inner_kwargs` — NamedTuple of kwargs forwarded to `solve_inner` at
+    every Q evaluation, e.g. `(pfac=0.1, xfac=10.0, nx=128, nq=5)` for
+    Galerkin grid tuning. Defaults to `NamedTuple()`.
+"""
+function multi_surface_coupling_full(surfaces::AbstractVector{<:SurfaceCoupling},
+    dp_raw::AbstractMatrix;
+    ref_idx::Integer=1,
+    msing_max::Integer=length(surfaces),
+    rotation::AbstractVector{<:Real}=zeros(length(surfaces)),
+    ntor::Integer=1,
+    inner_kwargs::NamedTuple=NamedTuple())
+    m = length(surfaces)
+    size(dp_raw) == (2m, 2m) ||
+        throw(ArgumentError("multi_surface_coupling_full: dp_raw size " *
+                            "$(size(dp_raw)) ≠ ($(2m), $(2m))"))
+    1 <= ref_idx <= m ||
+        throw(ArgumentError("multi_surface_coupling_full: ref_idx=$ref_idx " *
+                            "out of range 1:$m"))
+    1 <= msing_max <= m ||
+        throw(ArgumentError("multi_surface_coupling_full: msing_max=$msing_max " *
+                            "out of range 1:$m"))
+    length(rotation) == m ||
+        throw(ArgumentError("multi_surface_coupling_full: rotation length " *
+                            "$(length(rotation)) ≠ $m"))
+    return MultiSurfaceCouplingFull(surfaces,
+        Matrix{ComplexF64}(dp_raw),
+        Int(ref_idx), Int(msing_max),
+        Float64.(collect(rotation)),
+        Int(ntor),
+        inner_kwargs)
+end
+
+# Assemble and return det(mat) where mat is the 4·msing_max × 4·msing_max
+# Pletzer-Dewar matching matrix (Wang et al. 2020, Eq. 11; Glasser-Wang-Park
+# 2016, Eqs. 36-40).
+function (mc::MultiSurfaceCouplingFull)(Q::Number)
+    m = mc.msing_max
+    s2 = 2m
+    s4 = 4m
+    Qc = ComplexF64(Q)
+    ref_tauk = mc.surfaces[mc.ref_idx].tauk
+
+    # Allocate the matching matrix and fill the lower-left 2m × 2m block
+    # with transpose(dp_raw[1:s2, 1:s2]).
+    mat = zeros(ComplexF64, s4, s4)
+    @views mat[s2+1:s4, 1:s2] .= transpose(mc.dp_raw[1:s2, 1:s2])
+
+    # Per-surface inner-layer assembly
+    @inbounds for k in 1:m
+        sc = mc.surfaces[k]
+        idx1 = 2k - 1          # C^k_L
+        idx2 = 2k              # C^k_R
+        idx3 = idx1 + s2       # d^k_+
+        idx4 = idx2 + s2       # d^k_-
+
+        # Per-surface Q shift: guess_modify = Q + i·n·rotation[k].
+        # Also apply ref_tauk / sc.tauk rescaling (we keep the SurfaceCoupling
+        # tauk normalization that SLAYER needs; GGJ has tauk=1 so it's a no-op).
+        Q_k = Qc * (ref_tauk / sc.tauk) + 1im * mc.ntor * mc.rotation[k]
+        resp = solve_inner(sc.model, sc.params, Q_k; mc.inner_kwargs...)
+
+        # delta1 = interchange (parity −), delta2 = tearing (parity +); named
+        # fields expose the two channels directly. sc.scale converts inner-basis
+        # Δ to outer units (1.0 for GGJ since rescale_delta is applied inside
+        # solve_inner; S^(1/3) for SLAYER).
+        #
+        # NOTE: the fulldomain matching does NOT add any Δ_crit offset here —
+        # delta1, delta2 are the raw inner-layer outputs. The full 4m×4m
+        # Pletzer-Dewar residual includes the interchange channel, which
+        # provides Glasser (Mercier) stabilization natively; Δ_crit is a
+        # slab-layer proxy only relevant to SLAYER's tearing-only model.
+        delta1 = resp.interchange * sc.scale
+        delta2 = resp.tearing * sc.scale
+        # The inner-layer solver returns NaN when the Riccati integration fails
+        # (clustered near poles). Propagate it as the residual so the scan flags
+        # this Q-cell via its isfinite checks, rather than feeding NaN into the
+        # LU factorization where it silently contaminates the whole determinant.
+        (isfinite(delta1) && isfinite(delta2)) || return ComplexF64(NaN, NaN)
+
+        # --- Upper-left 2×2 block: per-surface identity on C_{L,R} ---
+        mat[idx1, idx1] = 1
+        mat[idx2, idx2] = 1
+
+        # --- Upper-right 2×2 block: matching identity ---
+        #   C^k_L = d^k_+ − d^k_-         ⇒ mat[idx1,idx3]=-1, mat[idx1,idx4]=+1
+        #   C^k_R = -(d^k_+ + d^k_-)      ⇒ mat[idx2,idx3]=-1, mat[idx2,idx4]=-1
+        mat[idx1, idx3] = -1
+        mat[idx1, idx4] = 1
+        mat[idx2, idx3] = -1
+        mat[idx2, idx4] = -1
+
+        # --- Lower-right 2×2 block: inner Δ matching ---
+        #   d^k_+ eqn: -Δ_int·d^k_+ + Δ_tear·d^k_- + (outer D' terms) = 0
+        #   d^k_- eqn: -Δ_int·d^k_+ - Δ_tear·d^k_- + (outer D' terms) = 0
+        mat[idx3, idx3] = -delta1
+        mat[idx3, idx4] = delta2
+        mat[idx4, idx3] = -delta1
+        mat[idx4, idx4] = -delta2
+    end
+
+    return det(mat)
+end
diff --git a/src/Tearing/Dispersion/Dispersion.jl b/src/Tearing/Dispersion/Dispersion.jl
new file mode 100644
index 000000000..67731e5aa
--- /dev/null
+++ b/src/Tearing/Dispersion/Dispersion.jl
@@ -0,0 +1,55 @@
+# Dispersion.jl
+#
+# Tearing-dispersion-relation solver shared between GGJ and SLAYER inner-layer
+# models. Combines the outer-region Δ' from `PerturbedEquilibrium.SingularCoupling`
+# with the inner-layer Δ(Q) from any `InnerLayerModel` to find growth-rate
+# eigenvalues.
+#
+# Operating modes:
+#   - `SurfaceCoupling`     -- per-surface residual r(Q)
+#   - `multi_surface_coupling` -- multi-surface determinant (Coupled.jl)
+#   - `brute_force_scan`    -- regular 2D Q-plane scan
+#   - `find_growth_rates`   -- contour-intersection root extraction (Re=0 ∩ Im=0)
+#   - `amr_scan`            -- adaptive Q-plane refinement
+#
+# Roots are ISOLATED by 2D contour intersection on Nyquist-style Q-plane scans
+# (`find_growth_rates`, Re=0 ∩ Im=0), then optionally POLISHED to the true zero
+# of the residual by a bounded, neighbour-aware local solve (pass the residual
+# callable to `find_growth_rates`). Polishing makes the extracted root
+# resolution- and thread-independent; without it the reported root is the
+# marching-squares estimate, sensitive to grid/rounding. This module provides
+# both the residual building blocks and the scan/extraction machinery.
+#
+# The per-surface residual at one rational surface is
+#
+#   r(Q) = Δ'_diag - scale · Δ_inner(Q) - Δ_crit
+#
+# where `scale` is the inner→outer-units conversion factor (S^(1/3) for SLAYER,
+# 1 for GGJ since `rescale_delta` is applied internally) and `Δ_crit` is the
+# `dc_tmp` chi-parallel offset (zero by default).
+
+module Dispersion
+
+using LinearAlgebra
+using StaticArrays
+
+using ..InnerLayer
+using ..InnerLayer: InnerLayerModel, solve_inner, GGJModel, GGJParameters,
+    SLAYERModel, SLAYERParameters
+
+include("SurfaceCoupling.jl")
+include("Coupled.jl")
+include("CoupledFullMatch.jl")
+include("BruteForceScan.jl")
+include("ContourSearchAMR.jl")
+include("GrowthRateExtraction.jl")
+
+export SurfaceCoupling, surface_coupling
+export MultiSurfaceCoupling, multi_surface_coupling
+export MultiSurfaceCouplingFull, multi_surface_coupling_full
+export ScanResult, brute_force_scan
+export AMRCell, AMRResult, amr_scan
+export BoxActivity, MultiBoxAMRResult, multi_box_amr_scan, as_amr_result
+export GrowthRateResult, find_growth_rates
+
+end # module Dispersion
diff --git a/src/Tearing/Dispersion/GrowthRateExtraction.jl b/src/Tearing/Dispersion/GrowthRateExtraction.jl
new file mode 100644
index 000000000..42c2c9681
--- /dev/null
+++ b/src/Tearing/Dispersion/GrowthRateExtraction.jl
@@ -0,0 +1,992 @@
+# GrowthRateExtraction.jl
+#
+# Extracts tearing-mode growth rates by contour intersection (Re=0 ∩ Im=0) on a
+# complex Q-plane scan: finds intersections of the Re(Δ)=0 and Im(Δ)=0 contours,
+# classifies each intersection as a root or pole, and applies the "outside Re=0
+# contour, above pole" filter for spurious upper-branch roots.
+#
+# This PR (5/9) handles the regular-grid path via Contour.jl. PR 6 will add
+# a scattered-data path (triangulation) for AMR scans.
+#
+# Algorithm summary:
+#   1. Extract Re(Δ) = re_target and Im(Δ) = im_target contour polylines.
+#   2. Find all segment-segment intersections of the two contour families.
+#   3. For each intersection, find the closest Im=0 contour and classify as
+#      a pole if `max(|Re(Δ)|)` along the local arc exceeds `pole_threshold`.
+#   4. For each non-pole intersection, find the closest Re=0 contour. If
+#      that contour is approximately closed, take a small +γ step along the
+#      Im=0 contour and test whether the step lands inside the Re=0 loop.
+#      Roots whose +γ step exits the loop AND that lie above the highest
+#      pole are filtered out (spurious upper branches).
+#   5. Return the highest-γ surviving root in physical units.
+
+using Contour
+using DelaunayTriangulation
+
+# ---------------------------------------------------------------------
+# Public result struct + main entry point.
+# ---------------------------------------------------------------------
+
+"""
+    GrowthRateResult
+
+Output of `find_growth_rates`.
+
+| field                | meaning                                               |
+|:-------------------- |:----------------------------------------------------- |
+| `Q_root`             | Best (highest-γ surviving) root, normalized           |
+| `omega_Hz`           | `Re(Q_root) / tauk` — physical rotation frequency     |
+| `gamma_Hz`           | `Im(Q_root) / tauk` — physical growth rate            |
+| `Q_root_secondary`   | Second-most-unstable root flagged for ambiguity, or   |
+|                      | `NaN+NaNim` if the primary root was unambiguous.      |
+| `omega_Hz_secondary` | physical ω of the secondary root, or 0 if none        |
+| `gamma_Hz_secondary` | physical γ of the secondary root, or 0 if none        |
+| `warning_flags`      | `Vector{Symbol}` of warnings raised on `Q_root`:      |
+|                      | `:geom`, `:gap` (root accepted with caveat);          |
+|                      | `:spurious` when ≥1 contour near-miss was dropped by  |
+|                      | the validity gate (parked in `filtered_roots`); or    |
+|                      | `:no_root` when NO usable root was found (`Q_root` is |
+|                      | `NaN`; `omega_Hz`/`gamma_Hz` fall back to 0 — check   |
+|                      | this flag to tell that apart from a true γ≈0 result). |
+|                      | Empty if root is clean.                               |
+| `valid_roots`        | All non-pole intersections that survived pole filter  |
+| `poles`              | Intersections classified as poles                     |
+| `filtered_roots`     | Intersections rejected by the above-pole/outside-Re   |
+|                      | filter or the new geom+gap recursion                  |
+| `re_contours`        | Extracted Re(Δ)=`re_target` polylines                 |
+| `im_contours`        | Extracted Im(Δ)=`im_target` polylines                 |
+| `pole_threshold`     | Threshold used for pole classification                |
+"""
+struct GrowthRateResult
+    Q_root::ComplexF64
+    omega_Hz::Float64
+    gamma_Hz::Float64
+    Q_root_secondary::ComplexF64
+    omega_Hz_secondary::Float64
+    gamma_Hz_secondary::Float64
+    warning_flags::Vector{Symbol}
+    valid_roots::Vector{ComplexF64}
+    poles::Vector{ComplexF64}
+    filtered_roots::Vector{ComplexF64}
+    re_contours::Vector{Vector{ComplexF64}}
+    im_contours::Vector{Vector{ComplexF64}}
+    pole_threshold::Float64
+end
+
+"""
+    find_growth_rates(scan::ScanResult, tauk::Real;
+                       re_target=0.0, im_target=0.0,
+                       pole_threshold=10.0,
+                       filter_above_poles=true,
+                       filter_outside_re=true,
+                       gap_kHz_threshold=1.0) -> GrowthRateResult
+
+Extract tearing growth-rate eigenvalues from a brute-force `ScanResult` by
+contour-intersection analysis. `tauk` is the per-surface time normalization
+used to convert `Q` back to physical (Hz) units (`SurfaceCoupling.tauk` for
+single-surface scans; `mc.surfaces[mc.ref_idx].tauk` for coupled scans).
+
+# Keyword arguments
+
+  - `re_target`, `im_target` -- contour levels (zero for vanilla dispersion
+    root-finding; nonzero values let the caller probe iso-residual contours)
+  - `pole_threshold`   -- intersection is classified as a pole when
+    `max(|Re(Δ)|)` along the local arc of the nearest Im=0 contour exceeds
+    this value
+  - `filter_above_poles` -- discard roots whose γ exceeds the highest pole γ
+  - `filter_outside_re`  -- restrict the above-pole rejection to roots whose
+    +γ step along the Im=0 contour exits the Re=0 contour loop. When `true`,
+    roots that are above a pole but geometrically inside the Re=0 contour
+    survive (matches the Python default). Note this gate fails when the
+    Re=0 contour is OPEN (e.g., exits the Q box edge), letting spurious
+    upper-branch roots through; the `:geom` and `:gap` flags below cover
+    that case.
+  - `gap_kHz_threshold` -- if the highest-γ root is unstable (γ > 0) AND its
+    γ exceeds the next root by more than this many kHz, it is flagged as
+    a `:gap` warning. Default 1.0 kHz.
+  - `residual` -- optional dispersion-residual callable `f(Q::Complex)`. When
+    supplied, each contour-intersection root is POLISHED to the true zero of
+    `f` by a bounded, neighbour-aware local solve (see `_polish_root`), making
+    the extracted root resolution- and thread-independent. `nothing` (default)
+    reports the raw marching-squares estimate, which is sensitive to grid and
+    floating-point rounding.
+  - `polish_maxit` -- max polish iterations per root (default 20). Each costs a
+    handful of `f` evaluations; failures fall back to the unpolished point.
+  - `validity_rtol` -- with `residual` supplied, a polished root is kept only if
+    `|residual| < validity_rtol · median(|Δ|)` (default 1e-3). Intersections
+    that don't polish to a true zero (contour near-misses, or surfaces whose
+    Δ' BVP failed → `|Δ'|` huge and uncancellable) are dropped from
+    `valid_roots`, parked in `filtered_roots`, and flagged `:spurious`. If every
+    candidate is spurious, the result carries `:no_root`.
+
+# Spurious-root recursion
+
+After the per-intersection pole / above-pole filters, the remaining roots
+are sorted by descending γ. The selection loop walks down this list and at
+each candidate evaluates two flags:
+
+  - `:geom` — Re(Δ)=0 contour is locally a downward-concave "hill" at the
+    candidate (clean polyline-following quadratic fit).
+  - `:gap`  — candidate is unstable AND its γ exceeds the next root's by
+    more than `gap_kHz_threshold` kHz.
+
+If BOTH fire, the candidate is discarded as spurious and the next-most-
+unstable root is tried. If exactly ONE fires, the candidate is accepted as
+primary with that warning recorded, and the next root is exposed as
+`Q_root_secondary` so downstream tools can plot or reanalyse it. If
+neither fires, the candidate is accepted cleanly.
+"""
+function find_growth_rates(scan::ScanResult, tauk::Real;
+    re_target::Real=0.0, im_target::Real=0.0,
+    pole_threshold::Real=10.0,
+    filter_above_poles::Bool=true,
+    filter_outside_re::Bool=true,
+    gap_kHz_threshold::Real=1.0,
+    residual=nothing,
+    polish_maxit::Integer=20,
+    validity_rtol::Real=1e-3)
+    return _extract_growth_rates(scan.re_axis, scan.im_axis, scan.Δ,
+        Float64(tauk);
+        re_target=Float64(re_target),
+        im_target=Float64(im_target),
+        pole_threshold=Float64(pole_threshold),
+        filter_above_poles=filter_above_poles,
+        filter_outside_re=filter_outside_re,
+        gap_kHz_threshold=Float64(gap_kHz_threshold),
+        residual=residual,
+        polish_maxit=Int(polish_maxit),
+        validity_scale=_residual_scale(scan.Δ),
+        validity_rtol=Float64(validity_rtol))
+end
+
+"""
+    find_growth_rates(amr::AMRResult, tauk::Real;
+                       re_target=0.0, im_target=0.0,
+                       pole_threshold=10.0,
+                       filter_above_poles=true,
+                       filter_outside_re=true) -> GrowthRateResult
+
+Extract tearing growth-rate eigenvalues from an AMR `AMRResult` via Delaunay
+triangulation + marching triangles on the scattered evaluation points. The
+pipeline after contour extraction (segment intersection, pole classification,
+outside-Re filter, physical-Hz conversion) is identical to the brute-force
+grid path — only the contour extractor changes. Hanging-node issues from the
+quadtree's mixed refinement levels are resolved by the triangulation
+respecting every evaluated point uniformly.
+"""
+function find_growth_rates(amr::AMRResult, tauk::Real;
+    re_target::Real=0.0, im_target::Real=0.0,
+    pole_threshold::Real=10.0,
+    filter_above_poles::Bool=true,
+    filter_outside_re::Bool=true,
+    gap_kHz_threshold::Real=1.0,
+    residual=nothing,
+    polish_maxit::Integer=20,
+    validity_rtol::Real=1e-3)
+    return _extract_growth_rates_amr(amr.Q, amr.Δ, Float64(tauk);
+        re_target=Float64(re_target),
+        im_target=Float64(im_target),
+        pole_threshold=Float64(pole_threshold),
+        filter_above_poles=filter_above_poles,
+        filter_outside_re=filter_outside_re,
+        gap_kHz_threshold=Float64(gap_kHz_threshold),
+        residual=residual,
+        polish_maxit=Int(polish_maxit),
+        validity_scale=_residual_scale(amr.Δ),
+        validity_rtol=Float64(validity_rtol))
+end
+
+# ---------------------------------------------------------------------
+# Implementation.
+# ---------------------------------------------------------------------
+
+# Bilinear interpolation of `values` on the regular grid `(re_axis, im_axis)`
+# at point (qr, qi). Out-of-grid points are clamped to the boundary.
+function _bilinear(re_axis::Vector{Float64}, im_axis::Vector{Float64},
+    values::Matrix{Float64}, qr::Real, qi::Real)
+    nre = length(re_axis)
+    nim = length(im_axis)
+    i = clamp(searchsortedlast(re_axis, qr), 1, nre - 1)
+    j = clamp(searchsortedlast(im_axis, qi), 1, nim - 1)
+    tx = (qr - re_axis[i]) / (re_axis[i+1] - re_axis[i])
+    ty = (qi - im_axis[j]) / (im_axis[j+1] - im_axis[j])
+    tx = clamp(tx, 0.0, 1.0)
+    ty = clamp(ty, 0.0, 1.0)
+    return (1 - tx) * (1 - ty) * values[i, j] + tx * (1 - ty) * values[i+1, j] +
+           (1 - tx) * ty * values[i, j+1] + tx * ty * values[i+1, j+1]
+end
+
+# Extract polylines for a single contour level on a regular grid.
+# Returns Vector{Vector{ComplexF64}} (one polyline per closed/open curve).
+function _extract_contours(re_axis::Vector{Float64}, im_axis::Vector{Float64},
+    values::Matrix{Float64}, level::Float64)
+    polylines = Vector{Vector{ComplexF64}}()
+    for cl in lines(contour(re_axis, im_axis, values, level))
+        xs, ys = coordinates(cl)
+        path = ComplexF64[xs[i] + ys[i] * im for i in eachindex(xs)]
+        length(path) >= 2 && push!(polylines, path)
+    end
+    return polylines
+end
+
+# Segment-segment intersection on the complex plane. Returns the
+# intersection point if segments [a,b] and [c,d] cross strictly (parameters
+# in (0,1)), else nothing. Endpoint touches return the touch point.
+function _segment_intersection(a::ComplexF64, b::ComplexF64,
+    c::ComplexF64, d::ComplexF64)
+    d1r, d1i = real(b - a), imag(b - a)
+    d2r, d2i = real(d - c), imag(d - c)
+    denom = d1r * d2i - d1i * d2r
+    abs(denom) < 1e-30 && return nothing      # parallel or degenerate
+    diffr, diffi = real(c - a), imag(c - a)
+    t = (diffr * d2i - diffi * d2r) / denom
+    u = (diffr * d1i - diffi * d1r) / denom
+    if 0 <= t <= 1 && 0 <= u <= 1
+        return a + t * (b - a)
+    end
+    return nothing
+end
+
+# Find all intersections between two families of polylines. Returns
+# Vector{ComplexF64}.
+function _all_intersections(re_paths::Vector{Vector{ComplexF64}},
+    im_paths::Vector{Vector{ComplexF64}})
+    out = ComplexF64[]
+    for re_path in re_paths
+        for i in 1:length(re_path)-1
+            a, b = re_path[i], re_path[i+1]
+            for im_path in im_paths
+                for j in 1:length(im_path)-1
+                    c, d = im_path[j], im_path[j+1]
+                    pt = _segment_intersection(a, b, c, d)
+                    pt !== nothing && push!(out, pt)
+                end
+            end
+        end
+    end
+    return out
+end
+
+# Index of the closest vertex in a polyline to a point.
+function _closest_vertex(path::Vector{ComplexF64}, pt::ComplexF64)
+    best_i = 0
+    best_d = Inf
+    for i in eachindex(path)
+        d = abs(path[i] - pt)
+        if d < best_d
+            best_d = d
+            best_i = i
+        end
+    end
+    return best_i, best_d
+end
+
+# Find the polyline (and vertex within it) whose vertex is closest to pt.
+function _closest_polyline_vertex(paths::Vector{Vector{ComplexF64}},
+    pt::ComplexF64)
+    best_path_idx = 0
+    best_vert_idx = 0
+    best_d = Inf
+    for (pi_, path) in enumerate(paths)
+        vi, d = _closest_vertex(path, pt)
+        if d < best_d
+            best_d = d
+            best_path_idx = pi_
+            best_vert_idx = vi
+        end
+    end
+    return best_path_idx, best_vert_idx, best_d
+end
+
+# Ray-casting point-in-polygon. `polygon` need not be closed (function
+# closes it internally).
+function _point_in_polygon(pt::ComplexF64, polygon::Vector{ComplexF64})
+    n = length(polygon)
+    n < 3 && return false
+    inside = false
+    pr, pi_ = real(pt), imag(pt)
+    j = n
+    for i in 1:n
+        xi, yi = real(polygon[i]), imag(polygon[i])
+        xj, yj = real(polygon[j]), imag(polygon[j])
+        if ((yi > pi_) != (yj > pi_)) &&
+           (pr < (xj - xi) * (pi_ - yi) / (yj - yi) + xi)
+            inside = !inside
+        end
+        j = i
+    end
+    return inside
+end
+
+# ---------------------------------------------------------------------
+# Shared analysis: intersections + pole classification + outside-Re filter.
+# Both the regular-grid path (_extract_growth_rates) and the AMR
+# triangulation path (_extract_growth_rates_amr) funnel through this.
+# ---------------------------------------------------------------------
+# Geometric "spurious upper-branch" detector — flags candidates where the
+# Re(Δ)=0 contour is locally a downward-concave "hill" or "hump" (⌒) at the
+# candidate location. Legitimate tearing roots sit at the bottom of upward-
+# concave "wells" (∪); spurious upper-branch roots sit at the top of hills.
+#
+# Algorithm:
+#  1. Find the closest Re=0 polyline + closest vertex on it.
+#  2. Walk outward along that polyline, collecting consecutive vertices
+#     within `max_walk` Q-distance of the candidate. Walking the polyline
+#     (rather than averaging over a radius) avoids polluting the fit with
+#     vertices from disconnected nearby Re=0 fragments — important on
+#     AMR-triangulated meshes where the contour is fragmented.
+#  3. Fit γ = a + b·Δω + c·(Δω)² to the collected vertices via least squares.
+#     Sign of `c` is the local concavity:
+#        c < 0  → contour is concave-DOWN (hill, ⌒) ← SPURIOUS pattern
+#        c > 0  → contour is concave-UP (well, ∪)   ← legitimate pattern
+#  4. Gate on fit quality: only flag when RMS_residual / γ_spread is below
+#     `quality_threshold`. Noisy fits (e.g. multiple overlapping contour
+#     fragments) leave the candidate unflagged — letting the gap criterion
+#     and downstream review handle ambiguous cases.
+#
+# Returns `true` when the candidate is on a CLEAN concave-down arc; else
+# `false`. The orientation-invariance of the previous 3-point stencil
+# version is preserved because we fit γ = f(ω) which has a sign-stable
+# second derivative regardless of traversal direction.
+function _is_geom_spurious(pt::ComplexF64,
+    re_paths::Vector{Vector{ComplexF64}};
+    max_walk::Float64=0.5,
+    curvature_threshold::Float64=0.05,
+    quality_threshold::Float64=0.15)
+    re_idx, re_v_idx, _ = _closest_polyline_vertex(re_paths, pt)
+    re_idx == 0 && return false
+    re_path = re_paths[re_idx]
+    n_path = length(re_path)
+    n_path < 5 && return false
+
+    # Walk outward from re_v_idx along the polyline, collecting vertices
+    # within max_walk Q-distance of pt. Stop in each direction at the first
+    # vertex that exceeds the walk radius.
+    collected_idx = Int[re_v_idx]
+    @inbounds for k in (re_v_idx+1):n_path
+        if abs(re_path[k] - pt) < max_walk
+            push!(collected_idx, k)
+        else
+            break
+        end
+    end
+    @inbounds for k in (re_v_idx-1):-1:1
+        if abs(re_path[k] - pt) < max_walk
+            push!(collected_idx, k)
+        else
+            break
+        end
+    end
+    n = length(collected_idx)
+    n < 5 && return false
+
+    ω₀ = real(pt)
+    ωs = Vector{Float64}(undef, n)
+    γs = Vector{Float64}(undef, n)
+    @inbounds for (i, k) in enumerate(collected_idx)
+        ωs[i] = real(re_path[k]) - ω₀
+        γs[i] = imag(re_path[k])
+    end
+    ω_sp = maximum(ωs) - minimum(ωs)
+    γ_sp = maximum(γs) - minimum(γs)
+    (ω_sp < 1e-6 || γ_sp < 1e-12) && return false
+
+    # Quadratic least-squares fit γ = a + b·ω + c·ω² via the normal equations
+    # MᵀM·coeffs = Mᵀγ, where M = [1 ω ω²]. Hand-rolled to avoid an allocation
+    # for the n×3 design matrix (we just need the 3×3 normal-equation matrix).
+    sx = 0.0
+    sx2 = 0.0
+    sx3 = 0.0
+    sx4 = 0.0
+    sy = 0.0
+    sxy = 0.0
+    sx2y = 0.0
+    @inbounds for i in 1:n
+        ω = ωs[i]
+        γ = γs[i]
+        ω2 = ω * ω
+        sx += ω
+        sx2 += ω2
+        sx3 += ω2 * ω
+        sx4 += ω2 * ω2
+        sy += γ
+        sxy += ω * γ
+        sx2y += ω2 * γ
+    end
+    M = [Float64(n) sx sx2;
+        sx sx2 sx3;
+        sx2 sx3 sx4]
+    rhs = [sy, sxy, sx2y]
+    coeffs = M \ rhs
+    c = coeffs[3]
+
+    # Fit-quality residual norm
+    rms_sq = 0.0
+    @inbounds for i in 1:n
+        pred = coeffs[1] + coeffs[2] * ωs[i] + coeffs[3] * ωs[i]^2
+        rms_sq += (γs[i] - pred)^2
+    end
+    rms = sqrt(rms_sq / n)
+    rms_norm = rms / γ_sp
+
+    # Spurious if concave-down AND fit is clean enough to trust
+    return c < -curvature_threshold && rms_norm < quality_threshold
+end
+
+# γ-gap separation: the candidate at `idx` (in γ-descending order) is unstable
+# AND clearly separated above the next-most-unstable candidate by more than
+# `gap_kHz_threshold` kHz. Flags an outlier "lone peak" root.
+function _is_gap_spurious(sorted_roots::Vector{ComplexF64}, idx::Int,
+    tauk::Float64, gap_kHz_threshold::Float64)
+    γ_idx = imag(sorted_roots[idx]) / tauk * 1e-3   # kHz
+    γ_idx > 0.0 || return false                       # only suspicious if unstable
+    idx >= length(sorted_roots) && return false       # nothing below to compare
+    γ_next = imag(sorted_roots[idx+1]) / tauk * 1e-3
+    return (γ_idx - γ_next) > gap_kHz_threshold
+end
+
+# ---------------------------------------------------------------------
+# Root polishing (isolate-then-polish).
+#
+# The contour scan only *locates* roots to the grid/cell resolution: the
+# marching-squares intersection is a linear estimate of where Re(f)=0 and
+# Im(f)=0 cross, so the residual there is small only relative to the scan's
+# huge dynamic range, not zero. Its exact position is sensitive to ULP-level
+# jitter in the sampled `f` values (BLAS/thread reduction order), which can
+# shift the reported γ between near-degenerate estimates of the SAME root.
+#
+# `_polish_root` refines one contour intersection to the actual zero of `f`
+# by bounded minimization of `|f(Q)|`, so the reported root becomes
+# resolution- and thread-independent. It is a no-op-or-better layer: any
+# failure (non-finite, no decrease, pole approach) falls back to the
+# unpolished contour point.
+# ---------------------------------------------------------------------
+
+# Median contour-segment length — a robust proxy for the local grid/cell
+# resolution, used to cap how far a polish may move a root.
+function _median_segment_length(re_paths::Vector{Vector{ComplexF64}},
+    im_paths::Vector{Vector{ComplexF64}})
+    lens = Float64[]
+    for paths in (re_paths, im_paths), p in paths
+        @inbounds for i in 1:length(p)-1
+            push!(lens, abs(p[i+1] - p[i]))
+        end
+    end
+    isempty(lens) && return 0.0
+    sort!(lens)
+    return lens[cld(length(lens), 2)]
+end
+
+# Trust-region radius for polishing the intersection at `pts[idx]`. Capped by
+# `nn_frac` × distance to the NEAREST other intersection (root OR pole), so a
+# polished root can never migrate into a neighbour's basin — `nn_frac < 0.5`
+# guarantees, by the triangle inequality, that the result stays strictly
+# closer to its own contour point than to any other. Also capped at a few grid
+# cells (`k_cell·h`) so refinement only corrects the discretization error.
+function _polish_trust_radius(pts::Vector{ComplexF64}, idx::Int, h_grid::Real;
+    k_cell::Float64=3.0, nn_frac::Float64=0.45)
+    p0 = pts[idx]
+    d_nn = Inf
+    @inbounds for j in eachindex(pts)
+        j == idx && continue
+        d = abs(pts[j] - p0)
+        d < d_nn && (d_nn = d)
+    end
+    r_cell = k_cell * Float64(h_grid)
+    r = isfinite(d_nn) ? min(r_cell, nn_frac * d_nn) : r_cell
+    return r > 0 ? r : r_cell
+end
+
+# Bounded Gauss-Newton minimization of |f(Q)| within a disk of radius `R`
+# around `Q0`. Returns (Q_best, n_evals, improved). `improved == false` ⇒
+# Q0 is returned unchanged (hard fallback). Secant derivative + trust-region
+# projection + |f|-decrease backtracking keep it strictly "improve-or-no-op":
+# it cannot diverge, jump to another root, or be pulled into a pole.
+function _polish_root(f, Q0::ComplexF64, R::Float64; maxit::Int=8,
+    tol_step::Float64=1e-12, tol_f::Float64=1e-8)
+    (R > 0) || return (Q0, 0, false)
+    f0 = ComplexF64(f(Q0))
+    nev = 1
+    isfinite(f0) || return (Q0, nev, false)
+    a0 = abs(f0)
+    a0 == 0 && return (Q0, nev, false)
+
+    # Second point for the secant slope: a small real offset scaled to R.
+    Qk, fk = Q0, f0
+    Qp = Q0 + complex(max(R * 1e-3, eps(Float64)), 0.0)
+    fp = ComplexF64(f(Qp))
+    nev += 1
+    Qbest, abest = Q0, a0
+
+    for _ in 1:maxit
+        df = (Qp == Qk) ? ComplexF64(0) : (fp - fk) / (Qp - Qk)
+        (isfinite(df) && abs(df) > 0) || break
+        step = -fk / df                                   # zero of local linear model
+        Qt = Qk + step
+        if abs(Qt - Q0) > R                               # project into trust region
+            Qt = Q0 + R * (Qt - Q0) / abs(Qt - Q0)
+        end
+        ft = ComplexF64(f(Qt))
+        nev += 1
+        bt = 0
+        while (!isfinite(ft) || abs(ft) >= abest) && bt < 3      # backtrack on no-decrease
+            Qt = Qk + 0.5 * (Qt - Qk)
+            (abs(Qt - Q0) <= R) || break
+            ft = ComplexF64(f(Qt))
+            nev += 1
+            bt += 1
+        end
+        if isfinite(ft) && abs(ft) < abest
+            Qp, fp = Qk, fk
+            Qk, fk = Qt, ft
+            Qbest, abest = Qt, abs(ft)
+            (abs(step) < tol_step * (abs(Qk) + tol_step) || abest < tol_f * a0) && break
+        else
+            break                                          # no further decrease → stop
+        end
+    end
+    return (abest < a0 ? Qbest : Q0, nev, abest < a0)
+end
+
+# Median |Δ| over the finite scan values — the "typical residual magnitude"
+# reference for the validity gate. A genuine root drives the polished |residual|
+# orders of magnitude below this; a near-miss stays near it.
+function _residual_scale(Δ)
+    a = Float64[]
+    for z in Δ
+        az = abs(z)
+        (isfinite(az) && az < 1e30) && push!(a, az)
+    end
+    isempty(a) && return 0.0
+    sort!(a)
+    n = length(a)
+    return isodd(n) ? a[(n+1)÷2] : 0.5 * (a[n÷2] + a[n÷2+1])
+end
+
+function _run_analysis(re_paths::Vector{Vector{ComplexF64}},
+    im_paths::Vector{Vector{ComplexF64}},
+    im_re_vals::Vector{Vector{Float64}},
+    tauk::Float64;
+    pole_threshold::Float64,
+    filter_above_poles::Bool,
+    filter_outside_re::Bool,
+    gap_kHz_threshold::Float64=1.0,
+    residual=nothing,
+    polish_maxit::Int=20,
+    validity_scale::Float64=0.0,
+    validity_rtol::Float64=1e-3)
+    raw_intersections = _all_intersections(re_paths, im_paths)
+
+    poles = ComplexF64[]
+    candidates = Tuple{ComplexF64,Bool}[]    # (pt, on_top_half_re_flag)
+    spurious_roots = ComplexF64[]               # polished, but |residual| ≫ 0
+
+    # Isolate-then-polish: when a residual callable is supplied, refine each
+    # (non-pole) contour intersection to the true zero of `f`. Trust radii are
+    # capped by the spacing to neighbouring intersections so closely-spaced
+    # coupled roots stay coherently bound to their own contour crossing.
+    do_polish = residual !== nothing
+    h_grid = do_polish ? _median_segment_length(re_paths, im_paths) : 0.0
+    # Validity gate: a genuine root drives |residual| ≈ 0; a spurious contour
+    # near-miss (e.g. a surface whose Δ' BVP failed → |Δ'| huge and
+    # uncancellable by the inner layer) leaves |residual| stuck near the typical
+    # scan magnitude. Only active when polishing AND a scale is supplied.
+    do_gate = do_polish && validity_scale > 0
+    polish_evals = 0
+
+    for (i_pt, pt) in enumerate(raw_intersections)
+        # --- 1. classify as pole or root via local Re-magnitude on Im contour
+        best_im_path_idx, best_im_vert_idx, _ =
+            _closest_polyline_vertex(im_paths, pt)
+        is_pole = false
+        if best_im_path_idx > 0
+            re_vals = im_re_vals[best_im_path_idx]
+            n = length(re_vals)
+            i_prev = max(1, best_im_vert_idx - 1)
+            i_next = min(n, best_im_vert_idx + 1)
+            local_max = max(abs(re_vals[i_prev]),
+                abs(re_vals[i_next]),
+                abs(re_vals[best_im_vert_idx]))
+            is_pole = local_max > pole_threshold
+        end
+
+        if is_pole
+            push!(poles, pt)
+            continue
+        end
+
+        # --- 1b. polish this (non-pole) intersection to the true zero of `f`,
+        # bounded to a neighbour-aware trust region (no-op on failure).
+        if do_polish
+            R = _polish_trust_radius(raw_intersections, i_pt, h_grid)
+            pt, nev, _ = _polish_root(residual, pt, R; maxit=polish_maxit)
+            polish_evals += nev
+            # --- 1c. validity gate: drop intersections that don't polish to a
+            # true zero (|residual| stays ≫ 0 relative to the scan scale). These
+            # are contour near-misses / failed-BVP surfaces, not real roots.
+            if do_gate
+                rmag = abs(ComplexF64(residual(pt)))
+                polish_evals += 1
+                if !(rmag < validity_rtol * validity_scale)   # NaN ⇒ spurious
+                    push!(spurious_roots, pt)
+                    continue
+                end
+            end
+        end
+
+        # --- 2. "+γ step inside Re contour" flag for spurious-upper-branch filter
+        on_top_half_re = false
+        best_re_path_idx, _, _ = _closest_polyline_vertex(re_paths, pt)
+        if best_im_path_idx > 0 && best_re_path_idx > 0
+            re_path = re_paths[best_re_path_idx]
+            xs = real.(re_path)
+            ys = imag.(re_path)
+            contour_extent = max(maximum(xs) - minimum(xs),
+                maximum(ys) - minimum(ys))
+            closure_gap = abs(re_path[1] - re_path[end])
+
+            if contour_extent > 0 && closure_gap < 0.1 * contour_extent
+                # Re=0 contour is approximately closed → containment test applies
+                im_path = im_paths[best_im_path_idx]
+                n_im = length(im_path)
+                im_nearest = best_im_vert_idx
+                i_a = min(im_nearest + 1, n_im)
+                i_b = max(im_nearest - 1, 1)
+                gamma_a = imag(im_path[i_a])
+                gamma_b = imag(im_path[i_b])
+                gamma_here = imag(im_path[im_nearest])
+
+                tangent = if gamma_a >= gamma_b && gamma_a > gamma_here
+                    im_path[i_a] - im_path[im_nearest]
+                elseif gamma_b > gamma_here
+                    im_path[i_b] - im_path[im_nearest]
+                else
+                    ComplexF64(0.0, 1.0)        # fall back to straight up
+                end
+
+                tlen = abs(tangent)
+                if tlen > 0
+                    step_size = 0.01 * contour_extent
+                    step_pt = pt + (step_size / tlen) * tangent
+                    inside = _point_in_polygon(step_pt, re_path)
+                    on_top_half_re = !inside
+                end
+            end
+        end
+
+        push!(candidates, (pt, on_top_half_re))
+    end
+
+    # --- 3. pole + closed-loop filter (legacy), then geom + gap recursion (new)
+    valid_roots = ComplexF64[c[1] for c in candidates]
+    filtered_roots = copy(spurious_roots)     # gate-rejected near-misses kept here
+    Q_root = ComplexF64(NaN, NaN)
+    Q_root_2nd = ComplexF64(NaN, NaN)
+    warning_flags = Symbol[]
+    isempty(spurious_roots) || push!(warning_flags, :spurious)
+
+    if !isempty(valid_roots)
+        order = sortperm(valid_roots; by=q -> -imag(q))
+        sorted_pts = valid_roots[order]
+        sorted_top = Bool[c[2] for c in candidates][order]
+
+        max_pole_gamma = isempty(poles) ? -Inf : maximum(imag, poles)
+
+        chosen_idx = 0
+        for k in 1:length(sorted_pts)
+            cand = sorted_pts[k]
+            top_re = sorted_top[k]
+            # Legacy filter: above-pole + closed-loop outside-Re
+            legacy_reject = filter_above_poles && imag(cand) > max_pole_gamma &&
+                            (!filter_outside_re || top_re)
+            if legacy_reject
+                push!(filtered_roots, cand)
+                continue
+            end
+            # New checks: 2 spurious-root flags — :geom and :gap.
+            #   :geom — Re=0 contour is locally a downward-concave "hill"
+            #           at the candidate (clean polyline-following fit)
+            #   :gap  — candidate is unstable AND >1 kHz above next root
+            #           (isolated γ peak — spurious outlier signature)
+            #
+            # Policy: WARN, DO NOT DISCARD. The both-flags-fire criterion
+            # is too aggressive in the kink-approach regime where valid
+            # roots become sparse — a few-kHz γ separation between the
+            # dominant unstable root and the next-stable root can be
+            # genuine dispersion structure rather than a "lone peak"
+            # artifact, yet :gap fires regardless. So every candidate is
+            # accepted with whatever warnings apply, and downstream tools
+            # see the same valid_roots regardless of flag combination.
+            # filtered_roots is preserved for the legacy above-pole +
+            # outside-Re reject branch only.
+            geom_flag = _is_geom_spurious(cand, re_paths)
+            gap_flag = _is_gap_spurious(sorted_pts, k, tauk,
+                gap_kHz_threshold)
+            chosen_idx = k
+            geom_flag && push!(warning_flags, :geom)
+            gap_flag && push!(warning_flags, :gap)
+            break
+        end
+
+        if chosen_idx > 0
+            Q_root = sorted_pts[chosen_idx]
+            # When a warning fired, expose the next-down root as secondary so
+            # downstream tools can plot/reanalyse. (Indices > chosen_idx in
+            # sorted_pts are the next-most-unstable.)
+            if !isempty(warning_flags) && chosen_idx < length(sorted_pts)
+                Q_root_2nd = sorted_pts[chosen_idx+1]
+            end
+        end
+    end
+
+    # No usable root: either the scan produced no zero-crossing intersections
+    # (`valid_roots` empty) or every candidate was rejected by the legacy
+    # above-pole filter (`chosen_idx == 0`). Flag this explicitly so callers
+    # can distinguish a genuine marginally-stable result (γ ≈ 0) from a failed
+    # extraction — both otherwise surface as `gamma_Hz == 0.0` below.
+    isnan(real(Q_root)) && push!(warning_flags, :no_root)
+
+    omega_Hz = isnan(real(Q_root)) ? 0.0 : real(Q_root) / tauk
+    gamma_Hz = isnan(imag(Q_root)) ? 0.0 : imag(Q_root) / tauk
+    omega_Hz_2nd = isnan(real(Q_root_2nd)) ? 0.0 : real(Q_root_2nd) / tauk
+    gamma_Hz_2nd = isnan(imag(Q_root_2nd)) ? 0.0 : imag(Q_root_2nd) / tauk
+
+    return GrowthRateResult(Q_root, omega_Hz, gamma_Hz,
+        Q_root_2nd, omega_Hz_2nd, gamma_Hz_2nd,
+        warning_flags,
+        valid_roots, poles, filtered_roots,
+        re_paths, im_paths, pole_threshold)
+end
+
+# Regular-grid path: extract contours via Contour.jl, compute im_re_vals by
+# bilinear interpolation on the grid, then run the shared analysis.
+function _extract_growth_rates(re_axis::Vector{Float64},
+    im_axis::Vector{Float64},
+    Δ_grid::Matrix{ComplexF64},
+    tauk::Float64;
+    re_target::Float64,
+    im_target::Float64,
+    pole_threshold::Float64,
+    filter_above_poles::Bool,
+    filter_outside_re::Bool,
+    gap_kHz_threshold::Float64=1.0,
+    residual=nothing,
+    polish_maxit::Int=20,
+    validity_scale::Float64=0.0,
+    validity_rtol::Float64=1e-3)
+    re_field = real.(Δ_grid)
+    im_field = imag.(Δ_grid)
+
+    re_paths = _extract_contours(re_axis, im_axis, re_field, re_target)
+    im_paths = _extract_contours(re_axis, im_axis, im_field, im_target)
+
+    im_re_vals = [Float64[_bilinear(re_axis, im_axis, re_field,
+        real(v), imag(v))
+                          for v in path]
+                  for path in im_paths]
+
+    return _run_analysis(re_paths, im_paths, im_re_vals, tauk;
+        pole_threshold=pole_threshold,
+        filter_above_poles=filter_above_poles,
+        filter_outside_re=filter_outside_re,
+        gap_kHz_threshold=gap_kHz_threshold,
+        residual=residual,
+        polish_maxit=polish_maxit,
+        validity_scale=validity_scale,
+        validity_rtol=validity_rtol)
+end
+
+# ---------------------------------------------------------------------
+# AMR path: Delaunay triangulation + marching triangles. Hanging nodes
+# from the quadtree's mixed refinement levels become first-class vertices
+# in the triangulation, so contour segments piece together without gaps.
+# ---------------------------------------------------------------------
+
+# Emit a Re=0 and Im=0 segment (if any) from a single triangle. Returns
+# `(re_seg, im_seg)` where each may be `nothing`. A segment is a
+# `@NamedTuple{p1::ComplexF64, p2::ComplexF64, a1::Float64, a2::Float64}`
+# where `a1`, `a2` carry the *complementary* field value at the endpoints
+# (Re-value for Im=0 segments, Im-value for Re=0 segments).
+function _march_triangle(p1::ComplexF64, p2::ComplexF64, p3::ComplexF64,
+    v1::ComplexF64, v2::ComplexF64, v3::ComplexF64,
+    re_target::Float64, im_target::Float64)
+    return (_march_single(p1, p2, p3, real(v1), real(v2), real(v3),
+            imag(v1), imag(v2), imag(v3), re_target),
+        _march_single(p1, p2, p3, imag(v1), imag(v2), imag(v3),
+            real(v1), real(v2), real(v3), im_target))
+end
+
+# Core marching step for one scalar field `f` with complementary field `g`.
+# Produces the contour segment at level=L (if any) along with the value of
+# `g` linearly interpolated at each endpoint.
+@inline function _march_single(p1::ComplexF64, p2::ComplexF64, p3::ComplexF64,
+    f1::Float64, f2::Float64, f3::Float64,
+    g1::Float64, g2::Float64, g3::Float64,
+    L::Float64)
+    a1 = f1 >= L
+    a2 = f2 >= L
+    a3 = f3 >= L
+    count = Int(a1) + Int(a2) + Int(a3)
+    (count == 0 || count == 3) && return nothing
+
+    # Identify the "odd" vertex and produce crossings on the two edges
+    # incident to it.
+    if a1 != a2 && a1 != a3
+        pt_a, ga = _cross_edge(p1, p2, f1, f2, g1, g2, L)
+        pt_b, gb = _cross_edge(p1, p3, f1, f3, g1, g3, L)
+    elseif a2 != a1 && a2 != a3
+        pt_a, ga = _cross_edge(p2, p1, f2, f1, g2, g1, L)
+        pt_b, gb = _cross_edge(p2, p3, f2, f3, g2, g3, L)
+    else
+        pt_a, ga = _cross_edge(p3, p1, f3, f1, g3, g1, L)
+        pt_b, gb = _cross_edge(p3, p2, f3, f2, g3, g2, L)
+    end
+    return (p1=pt_a, p2=pt_b, a1=ga, a2=gb)
+end
+
+# Linear crossing on edge (pa, pb) for field `f` at level `L`, with
+# complementary value `g` interpolated at the same parameter.
+@inline function _cross_edge(pa::ComplexF64, pb::ComplexF64,
+    fa::Float64, fb::Float64,
+    ga::Float64, gb::Float64, L::Float64)
+    denom = fb - fa
+    t = denom == 0 ? 0.0 : (L - fa) / denom
+    t = clamp(t, 0.0, 1.0)
+    return (pa + t * (pb - pa), ga + t * (gb - ga))
+end
+
+# Chain segments into polylines by endpoint matching. Each segment endpoint
+# is a `ComplexF64` that is shared bit-exactly with any adjacent triangle's
+# crossing (both sides of a triangulation edge compute the same linear
+# crossing from identical endpoint values). Returns
+# `(paths::Vector{Vector{ComplexF64}}, aux::Vector{Vector{Float64}})`.
+function _chain_segments(segs::Vector{<:NamedTuple})
+    # Build an endpoint → list-of-segment-indices adjacency map.
+    adj = Dict{ComplexF64,Vector{Int}}()
+    for (i, s) in enumerate(segs)
+        push!(get!(adj, s.p1, Int[]), i)
+        push!(get!(adj, s.p2, Int[]), i)
+    end
+
+    used = falses(length(segs))
+    paths = Vector{Vector{ComplexF64}}()
+    aux_vals = Vector{Vector{Float64}}()
+
+    # Walk a polyline starting from segment `start_seg` via endpoint
+    # `start_pt`; returns the path and aux values.
+    function _walk(start_seg::Int, start_pt::ComplexF64)
+        path = ComplexF64[start_pt]
+        aux = Float64[]
+        # Emit the aux value for start_pt on the first segment
+        s0 = segs[start_seg]
+        push!(aux, start_pt == s0.p1 ? s0.a1 : s0.a2)
+
+        cur_seg = start_seg
+        cur_pt = start_pt
+        while true
+            used[cur_seg] = true
+            s = segs[cur_seg]
+            next_pt = cur_pt == s.p1 ? s.p2 : s.p1
+            next_aux = cur_pt == s.p1 ? s.a2 : s.a1
+            push!(path, next_pt)
+            push!(aux, next_aux)
+
+            nbrs = adj[next_pt]
+            nxt = 0
+            for j in nbrs
+                if !used[j] && j != cur_seg
+                    nxt = j
+                    break
+                end
+            end
+            nxt == 0 && break
+            cur_seg = nxt
+            cur_pt = next_pt
+        end
+        return path, aux
+    end
+
+    # Open polylines first: start from any endpoint touched by exactly
+    # one still-unused segment.
+    for (pt, nbrs) in adj
+        count = 0
+        start_seg = 0
+        for j in nbrs
+            if !used[j]
+                count += 1
+                start_seg = j
+            end
+        end
+        if count == 1
+            path, aux = _walk(start_seg, pt)
+            length(path) >= 2 && (push!(paths, path); push!(aux_vals, aux))
+        end
+    end
+
+    # Remaining segments form closed loops.
+    for i in eachindex(segs)
+        used[i] && continue
+        path, aux = _walk(i, segs[i].p1)
+        length(path) >= 2 && (push!(paths, path); push!(aux_vals, aux))
+    end
+
+    return paths, aux_vals
+end
+
+# AMR entry point: triangulate the scattered (Q, Δ) points, march triangles
+# to extract Re=0 and Im=0 contour segments with complementary-field values
+# at endpoints, chain into polylines, then run the shared analysis.
+function _extract_growth_rates_amr(Q::Vector{ComplexF64},
+    Δ::Vector{ComplexF64},
+    tauk::Float64;
+    re_target::Float64,
+    im_target::Float64,
+    pole_threshold::Float64,
+    filter_above_poles::Bool,
+    filter_outside_re::Bool,
+    gap_kHz_threshold::Float64=1.0,
+    residual=nothing,
+    polish_maxit::Int=20,
+    validity_scale::Float64=0.0,
+    validity_rtol::Float64=1e-3)
+    length(Q) == length(Δ) ||
+        throw(ArgumentError("_extract_growth_rates_amr: length(Q) ≠ length(Δ)"))
+    length(Q) >= 3 ||
+        throw(ArgumentError("_extract_growth_rates_amr: need ≥ 3 points to triangulate"))
+
+    pts = [(real(q), imag(q)) for q in Q]
+    tri = triangulate(pts)
+
+    # Segment types (carry complementary-field value at each endpoint)
+    re_segs = NamedTuple{(:p1, :p2, :a1, :a2),
+        Tuple{ComplexF64,ComplexF64,Float64,Float64}}[]
+    im_segs = NamedTuple{(:p1, :p2, :a1, :a2),
+        Tuple{ComplexF64,ComplexF64,Float64,Float64}}[]
+
+    for T in each_solid_triangle(tri)
+        i1, i2, i3 = T
+        p1 = Q[i1]
+        p2 = Q[i2]
+        p3 = Q[i3]
+        v1 = Δ[i1]
+        v2 = Δ[i2]
+        v3 = Δ[i3]
+        re_seg, im_seg = _march_triangle(p1, p2, p3, v1, v2, v3,
+            re_target, im_target)
+        re_seg !== nothing && push!(re_segs, re_seg)
+        im_seg !== nothing && push!(im_segs, im_seg)
+    end
+
+    re_paths, _ = _chain_segments(re_segs)
+    im_paths, im_re_vals = _chain_segments(im_segs)
+
+    return _run_analysis(re_paths, im_paths, im_re_vals, tauk;
+        pole_threshold=pole_threshold,
+        filter_above_poles=filter_above_poles,
+        filter_outside_re=filter_outside_re,
+        gap_kHz_threshold=gap_kHz_threshold,
+        residual=residual,
+        polish_maxit=polish_maxit,
+        validity_scale=validity_scale,
+        validity_rtol=validity_rtol)
+end
diff --git a/src/Tearing/Dispersion/SurfaceCoupling.jl b/src/Tearing/Dispersion/SurfaceCoupling.jl
new file mode 100644
index 000000000..271162c1a
--- /dev/null
+++ b/src/Tearing/Dispersion/SurfaceCoupling.jl
@@ -0,0 +1,103 @@
+# SurfaceCoupling.jl
+#
+# `SurfaceCoupling` packages everything the dispersion solver needs at one
+# rational surface: the inner-layer model, its parameters, the outer Δ'
+# diagonal element, the critical-Δ offset, the inner→outer-units scale
+# factor, and the per-surface time normalization `tauk`. The struct is
+# `Q`-callable and returns the complex residual
+#
+#   r(Q) = Δ'_diag - scale · Δ_inner(Q) - Δ_crit
+#
+# `tauk` is unused for single-surface evaluation but is required by the
+# multi-surface `MultiSurfaceCoupling` to rescale Q between each surface's
+# normalization.
+#
+# Constructor convenience: `surface_coupling(model, params, dp_diag; dc=0.0)`
+# auto-fills `scale` and `tauk` based on the model type — `scale = S^(1/3)`
+# and `tauk = params.tauk` for SLAYER (de-normalizes the inner-layer Δ to
+# outer units), `scale = 1` and `tauk = 1` for GGJ (Δ already in outer units
+# after `rescale_delta`; no inter-surface Q rescaling).
+
+"""
+    SurfaceCoupling{M<:InnerLayerModel, P}
+
+Per-surface dispersion data: `(model, params, dp_diag, dc, scale, tauk)`.
+Calling `sc(Q)` returns the complex residual
+
+```
+r(Q) = dp_diag - scale * solve_inner(model, params, Q).tearing - dc
+```
+
+A root of `sc` in the complex `Q` plane is a **tearing** eigenvalue at
+this surface in the *uncoupled* approximation (only the tearing channel
+of the inner-layer response appears — the interchange channel enters the
+full 2m×2m dispersion via `MultiSurfaceCoupling`, not this scalar form).
+Coupled multi-surface eigenvalues come from `MultiSurfaceCoupling`
+evaluating the determinant of the modified Δ' matrix.
+"""
+struct SurfaceCoupling{M<:InnerLayerModel,P}
+    model::M
+    params::P
+    dp_diag::ComplexF64
+    dc::Float64
+    scale::Float64
+    tauk::Float64
+end
+
+function (sc::SurfaceCoupling)(Q::Number)
+    Δ = solve_inner(sc.model, sc.params, ComplexF64(Q)).tearing
+    return sc.dp_diag - sc.scale * Δ - sc.dc
+end
+
+"""
+    surface_coupling(model::SLAYERModel, params::SLAYERParameters,
+                     dp_diag::Number; dc::Real=0.0) -> SurfaceCoupling
+
+SLAYER convenience constructor. `scale` is set to `params.lu^(1/3)` so that
+the dimensionless Δ from `riccati_f` is mapped to outer ψ-units before
+subtraction from the Δ' diagonal. `tauk` is taken from `params.tauk` for use
+by `MultiSurfaceCoupling` Q rescaling.
+"""
+function surface_coupling(model::SLAYERModel, params::SLAYERParameters,
+    dp_diag::Number; dc::Real=0.0)
+    return SurfaceCoupling(model, params, ComplexF64(dp_diag),
+        Float64(dc), params.lu^(1 / 3), params.tauk)
+end
+
+"""
+    surface_coupling(model::GGJModel, params::GGJParameters,
+                     dp_diag::Number) -> SurfaceCoupling
+
+GGJ convenience constructor. `scale` is `1.0` because GGJ's `solve_inner`
+applies its own `rescale_delta` (S^(2p₁/3)·v1^(2p₁)) internally, so the
+returned Δ is already in outer units. `tauk` defaults to `1.0` (GGJ has no
+direct analogue of SLAYER's per-surface time normalization, so multi-surface
+Q rescaling is a no-op for GGJ surfaces unless overridden).
+
+**No `dc` kwarg**: GGJ's 4m×4m Pletzer-Dewar residual already includes the
+interchange channel, which provides Glasser (Mercier) stabilization
+natively. A Δ_crit proxy (χ_parallel-matching offset on the diagonal) is
+meaningful only for tearing-only slab-layer approximations like SLAYER;
+for GGJ it would double-count the interchange physics. The `SurfaceCoupling`
+struct's `dc` field is hard-wired to 0 here.
+"""
+function surface_coupling(model::GGJModel, params::GGJParameters,
+    dp_diag::Number)
+    return SurfaceCoupling(model, params, ComplexF64(dp_diag),
+        0.0, 1.0, 1.0)
+end
+
+"""
+    surface_coupling(model::InnerLayerModel, params, dp_diag::Number;
+                     dc::Real=0.0, scale::Real=1.0, tauk::Real=1.0)
+        -> SurfaceCoupling
+
+Generic fallback constructor. Use this when wiring a new inner-layer model
+into the dispersion solver — pass the appropriate inner→outer-units `scale`
+and per-surface `tauk` explicitly.
+"""
+function surface_coupling(model::InnerLayerModel, params, dp_diag::Number;
+    dc::Real=0.0, scale::Real=1.0, tauk::Real=1.0)
+    return SurfaceCoupling(model, params, ComplexF64(dp_diag),
+        Float64(dc), Float64(scale), Float64(tauk))
+end
diff --git a/src/InnerLayer/GGJ/GGJ.jl b/src/Tearing/InnerLayer/GGJ/GGJ.jl
similarity index 86%
rename from src/InnerLayer/GGJ/GGJ.jl
rename to src/Tearing/InnerLayer/GGJ/GGJ.jl
index 1b8aacb23..773c5b64b 100644
--- a/src/InnerLayer/GGJ/GGJ.jl
+++ b/src/Tearing/InnerLayer/GGJ/GGJ.jl
@@ -17,13 +17,13 @@ module GGJ
 using LinearAlgebra
 using StaticArrays
 
-import ..InnerLayerModel, ..solve_inner
+import ..InnerLayerModel, ..InnerLayerResponse, ..solve_inner, ..InnerLayerParameters
 
 """
     GGJModel{S} <: InnerLayerModel
 
 Glasser–Greene–Johnson resistive inner-layer model. The type parameter `S`
-selects the solver: `:galerkin` (default) for the Hermite-cubic finite element 
+selects the solver: `:galerkin` (default) for the Hermite-cubic finite element
 solver and `:shooting` for the backward stable-shoot solver. Both
 implementations consume the same `inps` asymptotic-basis kernel and return
 the parity-projected matching data.
@@ -37,11 +37,14 @@ include("InnerAsymptotics.jl")
 include("Reference.jl")
 include("Shooting.jl")
 include("Galerkin.jl")
+include("LayerInputs.jl")
 
 export GGJModel, GGJParameters
 export mercier_di, mercier_dr, inner_Q, rescale_delta
 export build_asymptotics, evaluate_asymptotics, pick_xmax
 export InnerAsymptoticsCache
 export glasser_wang_2020_eq55
+export build_ggj_inputs
+export NeoResistivityModel, SpitzerModel, SauterNeoModel, RedlNeoModel
 
 end # module GGJ
diff --git a/src/InnerLayer/GGJ/GGJParameters.jl b/src/Tearing/InnerLayer/GGJ/GGJParameters.jl
similarity index 77%
rename from src/InnerLayer/GGJ/GGJParameters.jl
rename to src/Tearing/InnerLayer/GGJ/GGJParameters.jl
index c6986ea2a..8a27393c8 100644
--- a/src/InnerLayer/GGJ/GGJParameters.jl
+++ b/src/Tearing/InnerLayer/GGJ/GGJParameters.jl
@@ -2,8 +2,7 @@
 #
 # Physical parameters for the Glasser–Greene–Johnson inner-layer model and
 # the derived scale factors that map between physical and inner-layer
-# (Wasow-normalized) variables. Mirrors the Fortran `resist_type` defined
-# in rmatch/deltar_mod and rmatch/deltac_mod.
+# (Wasow-normalized) variables.
 
 """
     GGJParameters
@@ -12,16 +11,16 @@ Dimensionless parameters of the Glasser–Greene–Johnson inner-layer model
 at a single rational surface, plus the local Alfvén/resistive timescales
 needed to scale the matching data back to physical Δ.
 
-Fields are the same as the Fortran `resist_type`:
+Fields:
 
-| field   | meaning                                                       |
-|---------|---------------------------------------------------------------|
+| field   | meaning                                                        |
+|:------- |:-------------------------------------------------------------- |
 | `E`     | Glasser interchange parameter (enters Mercier `D_I = E+F+H−¼`) |
-| `F`     | Glasser interchange parameter                                 |
-| `G`     | Coupling coefficient (curvature × pressure gradient)          |
-| `H`     | Pfirsch–Schlüter coefficient                                  |
-| `K`     | Glasser parameter                                             |
-| `M`     | Mercier-related auxiliary parameter (held but not used here)  |
+| `F`     | Glasser interchange parameter                                  |
+| `G`     | Coupling coefficient (curvature × pressure gradient)           |
+| `H`     | Pfirsch–Schlüter coefficient                                   |
+| `K`     | Glasser parameter                                              |
+| `M`     | Mercier-related auxiliary parameter (held but not used here)   |
 | `taua`  | Local Alfvén time at the rational surface                      |
 | `taur`  | Local resistive time at the rational surface                   |
 | `v1`    | Linear scale factor used in the V₁ rescaling                   |
@@ -30,7 +29,7 @@ Fields are the same as the Fortran `resist_type`:
 The complex growth rate `γ` is **not** stored here; it is passed as a
 separate argument to `solve_inner`.
 """
-Base.@kwdef struct GGJParameters
+Base.@kwdef struct GGJParameters <: InnerLayerParameters
     E::Float64
     F::Float64
     G::Float64
@@ -46,15 +45,14 @@ end
 """
     mercier_di(p::GGJParameters) -> Float64
 
-Mercier interchange index `D_I = E + F + H − 1/4` (deltac_run line 143).
+Mercier interchange index `D_I = E + F + H − 1/4`.
 """
 mercier_di(p::GGJParameters) = p.E + p.F + p.H - 0.25
 
 """
     mercier_dr(p::GGJParameters) -> Float64
 
-Resistive interchange index `D_R = E + F + H²` (deltar_run derivation,
-deltac_run line 142).
+Resistive interchange index `D_R = E + F + H²`.
 """
 mercier_dr(p::GGJParameters) = p.E + p.F + p.H * p.H
 
@@ -81,14 +79,14 @@ sfac(p::GGJParameters) = p.taur / p.taua
 """
     x0(p::GGJParameters) -> Float64
 
-Inner-layer length scale `X₀ = S^(−1/3)` (deltac_run line 149).
+Inner-layer length scale `X₀ = S^(−1/3)`.
 """
 x0(p::GGJParameters) = sfac(p)^(-1.0 / 3.0)
 
 """
     q0(p::GGJParameters) -> Float64
 
-Inner-layer growth-rate scale `Q₀ = X₀ / τ_A` (deltac_run line 150).
+Inner-layer growth-rate scale `Q₀ = X₀ / τ_A`.
 """
 q0(p::GGJParameters) = x0(p) / p.taua
 
@@ -96,7 +94,7 @@ q0(p::GGJParameters) = x0(p) / p.taua
     inner_Q(p::GGJParameters, γ::Number) -> ComplexF64
 
 Dimensionless scaled inner-layer growth rate `Q = γ / Q₀` used by the inps Wasow
-basis (deltac_run line 151). The argument `γ` may be real or complex; the
+basis. The argument `γ` may be real or complex; the
 result is always complex.
 """
 inner_Q(p::GGJParameters, γ::Number) = ComplexF64(γ) / q0(p)
@@ -105,8 +103,8 @@ inner_Q(p::GGJParameters, γ::Number) = ComplexF64(γ) / q0(p)
     rescale_delta(Δ, p::GGJParameters) -> SVector{2,ComplexF64}
 
 Apply the Wang 2020 `X₀^(2√(−D_I))` rescaling that maps the inner-layer
-matching data back to physical Δ at the rational surface (deltac_run line
-192). Operates element-wise on a 2-vector of `(Δ_odd, Δ_even)`.
+matching data back to physical Δ at the rational surface. Operates
+element-wise on a 2-vector of `(Δ_odd, Δ_even)`.
 """
 function rescale_delta(Δ::AbstractVector, p::GGJParameters)
     s = sfac(p)
diff --git a/src/InnerLayer/GGJ/Galerkin.jl b/src/Tearing/InnerLayer/GGJ/Galerkin.jl
similarity index 71%
rename from src/InnerLayer/GGJ/Galerkin.jl
rename to src/Tearing/InnerLayer/GGJ/Galerkin.jl
index 93f889018..f14173175 100644
--- a/src/InnerLayer/GGJ/Galerkin.jl
+++ b/src/Tearing/InnerLayer/GGJ/Galerkin.jl
@@ -1,11 +1,11 @@
 # Galerkin.jl
 #
 # Hermite-cubic finite element (Galerkin) solver for the GGJ inner-layer
-# model. Direct port of rmatch/deltac.f in the "resonant + noexp + inps"
-# configuration. The half-domain problem (x ∈ [0, xmax]) is solved with
-# parity boundary conditions at x = 0 and asymptotic matching at x = xmax.
+# model, in the "resonant + noexp + inps" configuration. The half-domain
+# problem (x ∈ [0, xmax]) is solved with parity boundary conditions at
+# x = 0 and asymptotic matching at x = xmax.
 #
-# Configuration assumed (matches deltac.f defaults when called with inps):
+# Configuration assumed:
 #   gal_method = "resonant"    method = true     noexp = true
 #   basis_type = 0 (Hermite)   fulldomain = 0    inps_type = "inps"
 #   mpert = 3 (W, N, Θ)        np = 3 (Hermite cubic → 4 DOFs/node)
@@ -18,23 +18,23 @@ using FastGaussQuadrature: gausslobatto
 using QuadGK: quadgk
 
 # -----------------------------------------------------------------------
-# Physical-variable helpers (replacements for inpso_get_ua/dua/uv).
+# Physical-variable helpers.
 # -----------------------------------------------------------------------
 
 """
 Convert the inps 6×2 output U_inps at coordinate `x` to the physical
-(W, N, Θ) and (W', N', Θ') representation used by deltac/inpso.
+(W, N, Θ) and (W', N', Θ') representation.
 Returns `(ua, dua)` each 3×2 complex, where columns are the two algebraic
 solutions and rows are (W, N, Θ).
 """
 function _physical_ua_dua(cache::InnerAsymptoticsCache, x::Real)
     U, dU = evaluate_asymptotics(cache, x; derivative=true, apply_T=true)
-    ua  = zeros(ComplexF64, 3, 2)
+    ua = zeros(ComplexF64, 3, 2)
     dua = zeros(ComplexF64, 3, 2)
     @inbounds for j in 1:2
-        ua[1, j]  = U[1, j] / x
-        ua[2, j]  = U[2, j]
-        ua[3, j]  = U[3, j]
+        ua[1, j] = U[1, j] / x
+        ua[2, j] = U[2, j]
+        ua[3, j] = U[3, j]
         dua[1, j] = U[4, j] - U[1, j] / (x * x)
         dua[2, j] = U[5, j] * x
         dua[3, j] = U[6, j] * x
@@ -44,70 +44,74 @@ end
 
 """
 Build the (I, U, V) coefficient matrices of the second-order system
-`I·u'' − V·u' − U·u = 0` at coordinate `x`. Port of inpso_get_uv.
+`I·u'' − V·u' − U·u = 0` at coordinate `x`.
 All matrices are 3×3 complex.
 """
 function _physical_uv(params::GGJParameters, Q::ComplexF64, x::Real)
-    e = ComplexF64(params.E); f = ComplexF64(params.F)
-    h = ComplexF64(params.H); g = ComplexF64(params.G)
-    k = ComplexF64(params.K); q = Q
+    e = ComplexF64(params.E)
+    f = ComplexF64(params.F)
+    h = ComplexF64(params.H)
+    g = ComplexF64(params.G)
+    k = ComplexF64(params.K)
+    q = Q
     q2 = q * q
     x2 = x * x
 
     Imat = @SMatrix ComplexF64[1 0 0; 0 q2 0; 0 0 q]
 
-    # Build U and V matching inpso_get_uv: pre-scaled from column-major RESHAPE, then row 2 *=q², row 3 *=q.
-    # Pre-scaled U (column-major Fortran RESHAPE → Julia row-major):
+    # Build U and V: pre-scaled from column-major reshape, then row 2 *=q², row 3 *=q.
+    # Pre-scaled U (column-major reshape → Julia row-major):
     #   U[1,:] = (q,      -q*x,         0)
     #   U[2,:] = (-x/q,    x²/q,        -(e+f)/q²)
     #   U[3,:] = (-x/q,   -(g-ke)*q,     x²/q+(g+kf)*q)
     U = @SMatrix ComplexF64[
-        q                (-q * x)                     0
-        (-x / q) * q2    (x2 / q) * q2                (-(e + f) / q2) * q2
-        (-x / q) * q     (-(g - k * e) * q) * q       (x2 / q + (g + k * f) * q) * q
+        q (-q*x) 0
+        (-x/q)*q2 (x2/q)*q2 (-(e + f)/q2)*q2
+        (-x/q)*q (-(g - k * e)*q)*q (x2/q+(g+k*f)*q)*q
     ]
 
-    # Pre-scaled V (column-major Fortran RESHAPE → Julia row-major):
+    # Pre-scaled V (column-major reshape → Julia row-major):
     #   V[1,:] = (0,       0,  h)
     #   V[2,:] = (-h/q²,   0,  0)
     #   V[3,:] = (h*k*q,   0,  0)
     V = @SMatrix ComplexF64[
-        0                0  h
-        (-h / q2) * q2   0  0
-        (h * k * q) * q  0  0
+        0 0 h
+        (-h/q2)*q2 0 0
+        (h*k*q)*q 0 0
     ]
 
     return Imat, U, V
 end
 
 # -----------------------------------------------------------------------
-# Hermite cubic basis functions — port of deltac_hermite.
+# Hermite cubic basis functions.
 # Returns (pb[1:4], qb[1:4]) where pb = values, qb = derivatives,
-# indexed 1:4 → Fortran 0:3.
+# indexed 1:4 → physical 0:3.
 # -----------------------------------------------------------------------
 
 function _hermite(x::Real, x0::Real, x1::Real)
     dx = x1 - x0
     t0 = (x - x0) / dx
     t1 = 1 - t0
-    t02 = t0 * t0; t12 = t1 * t1
+    t02 = t0 * t0
+    t12 = t1 * t1
     pb = SVector{4,Float64}(
         t12 * (1 + 2t0),
         t12 * t0 * dx,
         t02 * (1 + 2t1),
-        -t02 * t1 * dx,
+        -t02 * t1 * dx
     )
     qb = SVector{4,Float64}(
         -6t0 * t1 / dx,
         t0 * (3t0 - 4) + 1,
         6t1 * t0 / dx,
-        t0 * (3t0 - 2),
+        t0 * (3t0 - 2)
     )
     return pb, qb
 end
 
 # -----------------------------------------------------------------------
-# Grid packing — port of deltac_pack.
+# Grid packing.
 # -----------------------------------------------------------------------
 
 function _pack(nx::Int, pfac::Float64, side::String)
@@ -149,13 +153,13 @@ function _pack(nx::Int, pfac::Float64, side::String)
 end
 
 # -----------------------------------------------------------------------
-# Three-level xmax sweep — replaces inpso_xmax for inps mode.
+# Three-level xmax sweep for inps mode.
 # Returns (xmax, dx1, dx2, cache).
 # -----------------------------------------------------------------------
 
 function _xmax_3level(params::GGJParameters, Q::ComplexF64;
-                     kmax::Int=8, xfac::Float64=1.0,
-                     eps_vec::NTuple{3,Float64}=(1e-2, 5e-7, 1e-7))
+    kmax::Int=8, xfac::Float64=1.0,
+    eps_vec::NTuple{3,Float64}=(1e-2, 5e-7, 1e-7))
     cache = build_asymptotics(params, Q; kmax=kmax)
 
     # Bisection-based root finder: find the exact x where max(residual) = eps,
@@ -179,7 +183,8 @@ function _xmax_3level(params::GGJParameters, Q::ComplexF64;
         if !any(set)
             break
         end
-        x_prev = x; delta_prev = dmax
+        x_prev = x
+        delta_prev = dmax
         x *= dxfac
     end
     any(set) && error("_xmax_3level: failed to bracket all xmax levels")
@@ -227,13 +232,21 @@ struct GalerkinWorkspace
     ndim::Int
     nx::Int
     kl::Int
-    mat::Array{ComplexF64,3}   # (ldab, ndim, 2) banded storage
-    rhs::Matrix{ComplexF64}    # (ndim, 2)
-    sol::Matrix{ComplexF64}    # (ndim, 2)
+    mat::Array{ComplexF64,3}              # (ldab, ndim, 2) banded storage
+    rhs::Matrix{ComplexF64}               # (ndim, 2)
+    sol::Matrix{ComplexF64}               # (ndim, 2)
+    # Reusable scratch buffers, zeroed per-cell via `fill!`. Eliminates the
+    # per-cell `zeros(...)` that otherwise allocates thousands of MiB over a
+    # full dispersion scan.
+    cell_mat_buf::Array{ComplexF64,4}     # (mpert=3, mpert, np+1=4, np+1=4)
+    cell_mat_ext_buf::Array{ComplexF64,4} # (3, 3, 4, 4)  max over CT_EXT/EXT1/EXT2
+    cell_rhs_ext_buf::Matrix{ComplexF64}  # (3, 4)
+    ab_buf::Matrix{ComplexF64}            # (ldab, ndim) scratch for banded LU
+    rhs_buf::Vector{ComplexF64}           # (ndim,) scratch for banded solve
 end
 
 function _build_grid_and_workspace(nx::Int, xmax::Float64, dx1::Float64, dx2::Float64,
-                                    pfac::Float64, cutoff::Int, nq::Int)
+    pfac::Float64, cutoff::Int, nq::Int)
     mpert = 3
     np = 3
     side = pfac < 1 ? "left" : "right"
@@ -255,8 +268,10 @@ function _build_grid_and_workspace(nx::Int, xmax::Float64, dx1::Float64, dx2::Fl
     x_nodes[nx-1] = xmax - (dx1 + dx2)
     ixmax = nx - 2  # packed region is 0..ixmax
 
-    x0 = x_nodes[1]; x1_packed = x_nodes[ixmax+1]
-    xm = (x1_packed + x0) / 2; dxp = (x1_packed - x0) / 2
+    x0 = x_nodes[1]
+    x1_packed = x_nodes[ixmax+1]
+    xm = (x1_packed + x0) / 2
+    dxp = (x1_packed - x0) / 2
     mx = ixmax ÷ 2
     packed = xm .+ dxp .* _pack(mx, pfac, side)
     for i in 1:(ixmax+1)
@@ -270,7 +285,8 @@ function _build_grid_and_workspace(nx::Int, xmax::Float64, dx1::Float64, dx2::Fl
     imap = 1
     for ix in 1:nx
         et = etypes[ix]
-        xl = x_nodes[ix]; xr = x_nodes[ix+1]
+        xl = x_nodes[ix]
+        xr = x_nodes[ix+1]
 
         cell_np = if et == CT_NONE || et == CT_EXT1 || et == CT_EXT2
             np
@@ -282,7 +298,7 @@ function _build_grid_and_workspace(nx::Int, xmax::Float64, dx1::Float64, dx2::Fl
 
         x_lsode = (et == CT_RES) ? xr : 0.0
 
-        # Build map (matching deltac_make_map_hermite logic)
+        # Build local-to-global Hermite DOF map.
         if et == CT_RES
             # Will be set after the loop
             map_local = zeros(Int, mpert, 1)  # map(:, 0:0)
@@ -307,7 +323,7 @@ function _build_grid_and_workspace(nx::Int, xmax::Float64, dx1::Float64, dx2::Fl
         if et == CT_EXT
             # Extra asymptotic DOFs: emap = (imap, imap+1, imap+2).
             # With noexp, ndim = imap so only emap[1] is active; emap[2,3] > ndim
-            # and are skipped during assembly. This matches Fortran convention.
+            # and are skipped during assembly.
             emap_local = [imap, imap + 1, imap + 2]
             map_extra = [imap; imap + 1; imap + 2]  # 3-element column
             map_local = hcat(map_local, map_extra)
@@ -321,30 +337,39 @@ function _build_grid_and_workspace(nx::Int, xmax::Float64, dx1::Float64, dx2::Fl
     emap_vals = ext_cell.emap
     res_map = reshape(copy(emap_vals), mpert, 1)
     cells[nx] = GalerkinCell(CT_RES, x_nodes[nx], x_nodes[nx+1], x_nodes[nx+1],
-                             -1, res_map, copy(emap_vals))
+        -1, res_map, copy(emap_vals))
 
     ndim = imap  # noexp → ndim = imap (only emap[1] ≤ ndim)
 
     # Bandwidth
-    kl = mpert * (np + 1) + 1 - 1  # resonant method with noexp; +1-1 kept for agreement with Fortran indexing
+    kl = mpert * (np + 1) + 1 - 1  # resonant method with noexp; +1-1 kept for indexing agreement
     ku = kl
     ldab = 2kl + ku + 1
 
     mat = zeros(ComplexF64, ldab, ndim, 2)
     rhs = zeros(ComplexF64, ndim, 2)
     sol = zeros(ComplexF64, ndim, 2)
-
-    return GalerkinWorkspace(cells, ndim, nx, kl, mat, rhs, sol)
+    # Preallocate per-cell scratch buffers sized to the max case (np+1=4).
+    # Smaller cells (e.g. CT_EXT with cell.np=1) use a (2×2) sub-slice and
+    # rely on fill!(buf, 0) to keep the remainder zero.
+    cell_mat_buf = zeros(ComplexF64, mpert, mpert, np + 1, np + 1)
+    cell_mat_ext_buf = zeros(ComplexF64, mpert, mpert, np + 1, np + 1)
+    cell_rhs_ext_buf = zeros(ComplexF64, mpert, np + 1)
+    ab_buf = zeros(ComplexF64, ldab, ndim)
+    rhs_buf = zeros(ComplexF64, ndim)
+
+    return GalerkinWorkspace(cells, ndim, nx, kl, mat, rhs, sol,
+        cell_mat_buf, cell_mat_ext_buf, cell_rhs_ext_buf,
+        ab_buf, rhs_buf)
 end
 
 # -----------------------------------------------------------------------
 # Local assembly: Gauss quadrature for "none" cells.
-# Port of deltac_gauss_quad.
 # -----------------------------------------------------------------------
 
 function _gauss_quad!(cell_mat::Array{ComplexF64,4}, cell::GalerkinCell,
-                     quad_nodes::Vector{Float64}, quad_weights::Vector{Float64},
-                     params::GGJParameters, Q::ComplexF64)
+    quad_nodes::Vector{Float64}, quad_weights::Vector{Float64},
+    params::GGJParameters, Q::ComplexF64)
     np_cell = cell.np
     nq = length(quad_nodes)
     x0 = (cell.x_right + cell.x_left) / 2
@@ -360,26 +385,27 @@ function _gauss_quad!(cell_mat::Array{ComplexF64,4}, cell::GalerkinCell,
         for ip in 0:np_cell, jp in 0:np_cell
             # cell_mat[:,:,ip+1,jp+1] += w*(I*qb[ip]*qb[jp] + V*pb[ip]*qb[jp] + U*pb[ip]*pb[jp])
             for jpert in 1:3, ipert in 1:3
-                cell_mat[ipert, jpert, ip+1, jp+1] += w * (
-                    Imat[ipert, jpert] * qb[ip+1] * qb[jp+1] +
-                    Vmat[ipert, jpert] * pb[ip+1] * qb[jp+1] +
-                    Umat[ipert, jpert] * pb[ip+1] * pb[jp+1]
-                )
+                cell_mat[ipert, jpert, ip+1, jp+1] +=
+                    w * (
+                        Imat[ipert, jpert] * qb[ip+1] * qb[jp+1] +
+                        Vmat[ipert, jpert] * pb[ip+1] * qb[jp+1] +
+                        Umat[ipert, jpert] * pb[ip+1] * pb[jp+1]
+                    )
             end
         end
     end
 end
 
 # -----------------------------------------------------------------------
-# Extension assembly — port of deltac_extension.
+# Extension assembly.
 # Handles ext, ext1, ext2 cell types.
 # -----------------------------------------------------------------------
 
 function _extension!(cell_mat::Array{ComplexF64,4}, cell_rhs::Matrix{ComplexF64},
-                    cell::GalerkinCell,
-                    quad_nodes::Vector{Float64}, quad_weights::Vector{Float64},
-                    params::GGJParameters, Q::ComplexF64,
-                    cache::InnerAsymptoticsCache)
+    cell::GalerkinCell,
+    quad_nodes::Vector{Float64}, quad_weights::Vector{Float64},
+    params::GGJParameters, Q::ComplexF64,
+    cache::InnerAsymptoticsCache)
     np_cell = cell.np
     x0c = (cell.x_right + cell.x_left) / 2
     dxc = (cell.x_right - cell.x_left) / 2
@@ -462,15 +488,15 @@ function _extension!(cell_mat::Array{ComplexF64,4}, cell_rhs::Matrix{ComplexF64}
 end
 
 # -----------------------------------------------------------------------
-# Resonant integral — replaces deltac_lsode_int with QuadGK.
+# Resonant integral via QuadGK.
 # Computes ∫_{x_left}^{x_lsode} ua_1^T L ua_j dx for j=1 and j=2.
 # With noexp, only (1,1) and (1,2) entries of the 1×2 result matter
 # (ip=1 only for test function, jp=1 for stiffness, jp=2 for RHS).
 # -----------------------------------------------------------------------
 
 function _resonant_integral(cell::GalerkinCell, params::GGJParameters,
-                           Q::ComplexF64, cache::InnerAsymptoticsCache;
-                           tol::Float64=1e-5)
+    Q::ComplexF64, cache::InnerAsymptoticsCache;
+    tol::Float64=1e-5)
     x_left = cell.x_left
     x_right = cell.x_lsode
 
@@ -480,7 +506,8 @@ function _resonant_integral(cell::GalerkinCell, params::GGJParameters,
     function integrand_11(x)
         ua, dua = _physical_ua_dua(cache, x)
         Imat, Umat, Vmat = _physical_uv(params, Q, x)
-        ua1 = ua[:, 1]; dua1 = dua[:, 1]
+        ua1 = ua[:, 1]
+        dua1 = dua[:, 1]
         return transpose(dua1) * Imat * dua1 + transpose(ua1) * Vmat * dua1 + transpose(ua1) * Umat * ua1
     end
 
@@ -488,7 +515,9 @@ function _resonant_integral(cell::GalerkinCell, params::GGJParameters,
         ua, dua = _physical_ua_dua(cache, x)
         Imat, Umat, Vmat = _physical_uv(params, Q, x)
         dua1 = dua[:, 1]
-        ua1 = ua[:, 1]; ua2 = ua[:, 2]; dua2 = dua[:, 2]
+        ua1 = ua[:, 1]
+        ua2 = ua[:, 2]
+        dua2 = dua[:, 2]
         return transpose(dua1) * Imat * dua2 + transpose(ua1) * Vmat * dua2 + transpose(ua1) * Umat * ua2
     end
 
@@ -503,69 +532,79 @@ end
 # -----------------------------------------------------------------------
 
 function _assemble_and_solve!(ws::GalerkinWorkspace,
-                             params::GGJParameters, Q::ComplexF64,
-                             cache::InnerAsymptoticsCache;
-                             nq::Int=4, tol_res::Float64=1e-5)
-    mpert = 3; np = 3
+    params::GGJParameters, Q::ComplexF64,
+    cache::InnerAsymptoticsCache;
+    nq::Int=4, tol_res::Float64=1e-5)
+    mpert = 3
+    np = 3
     quad_nodes, quad_weights = gausslobatto(nq + 1)
     offset = ws.kl + ws.kl + 1  # kl + ku + 1 since ku = kl
 
     fill!(ws.mat, 0)
     fill!(ws.rhs, 0)
 
-    # Per-cell assembly
+    # Per-cell assembly — reuse the preallocated scratch buffers, zeroing
+    # only the sub-slice actually used by this cell's np_eff.
+    cell_mat = ws.cell_mat_buf
+    cell_mat_ext = ws.cell_mat_ext_buf
+    cell_rhs_ext = ws.cell_rhs_ext_buf
     for ix in 1:ws.nx
         cell = ws.cells[ix]
 
         # Gauss quadrature for Hermite contribution (all cell types)
         if cell.np >= 0
             np_eff = cell.np
-            cell_mat = zeros(ComplexF64, mpert, mpert, np_eff + 1, np_eff + 1)
+            fill!(cell_mat, 0)
             _gauss_quad!(cell_mat, cell, quad_nodes, quad_weights, params, Q)
 
             # Assemble into global banded matrix (both parities use same base matrix)
             for ip in 0:np_eff, ipert in 1:mpert
                 i = cell.map[ipert, ip+1]
-                if i > ws.ndim; continue; end
+                if i > ws.ndim
+                    continue
+                end
                 for jp in 0:np_eff, jpert in 1:mpert
                     j = cell.map[jpert, jp+1]
-                    if j > ws.ndim; continue; end
-                    ws.mat[offset + i - j, j, 1] += cell_mat[ipert, jpert, ip+1, jp+1]
+                    if j > ws.ndim
+                        continue
+                    end
+                    ws.mat[offset+i-j, j, 1] += cell_mat[ipert, jpert, ip+1, jp+1]
                 end
             end
         end
 
         # Extension terms
         if cell.etype in (CT_EXT, CT_EXT1, CT_EXT2)
+            # np_eff matches the semantic size: CT_EXT has cell.np=1 → ext slot
+            # at index cell.np+1=2 (using 0-based; +1 in Julia), so the array
+            # used by the current code is (3,3,cell.np+2,cell.np+2)=(3,3,3,3).
+            # For CT_EXT1/EXT2 it's (3,3,cell.np+1,cell.np+1)=(3,3,4,4).
+            # Either way npp = cell.etype == CT_EXT ? cell.np + 1 : cell.np.
             np_eff = cell.etype == CT_EXT ? cell.np + 1 : cell.np
-            cell_mat_ext = zeros(ComplexF64, mpert, mpert, np_eff + 1, np_eff + 1)
-            cell_rhs_ext = zeros(ComplexF64, mpert, np_eff + 1)
-            # For ext, we need to create a temporary cell_mat that includes the extra DOF
-            if cell.etype == CT_EXT
-                cell_mat_ext = zeros(ComplexF64, mpert, mpert, cell.np + 2, cell.np + 2)
-                cell_rhs_ext = zeros(ComplexF64, mpert, cell.np + 2)
-            else
-                cell_mat_ext = zeros(ComplexF64, mpert, mpert, cell.np + 1, cell.np + 1)
-                cell_rhs_ext = zeros(ComplexF64, mpert, cell.np + 1)
-            end
+            fill!(cell_mat_ext, 0)
+            fill!(cell_rhs_ext, 0)
             _extension!(cell_mat_ext, cell_rhs_ext, cell, quad_nodes, quad_weights, params, Q, cache)
 
             # Assemble ext contributions
-            npp = size(cell_mat_ext, 3) - 1
+            npp = np_eff
             for ip in 0:npp, ipert in 1:mpert
                 i = ip < size(cell.map, 2) ? cell.map[ipert, ip+1] : cell.emap[1]
                 # For the extra DOF, only ipert=1 is meaningful (noexp)
                 if cell.etype == CT_EXT && ip == cell.np + 1 && ipert > 1
                     continue
                 end
-                if i > ws.ndim; continue; end
+                if i > ws.ndim
+                    continue
+                end
                 for jp in 0:npp, jpert in 1:mpert
                     j = jp < size(cell.map, 2) ? cell.map[jpert, jp+1] : cell.emap[1]
                     if cell.etype == CT_EXT && jp == cell.np + 1 && jpert > 1
                         continue
                     end
-                    if j > ws.ndim; continue; end
-                    ws.mat[offset + i - j, j, 1] += cell_mat_ext[ipert, jpert, ip+1, jp+1]
+                    if j > ws.ndim
+                        continue
+                    end
+                    ws.mat[offset+i-j, j, 1] += cell_mat_ext[ipert, jpert, ip+1, jp+1]
                 end
                 # RHS (both parities get the same base RHS)
                 ws.rhs[i, 1] += cell_rhs_ext[ipert, ip+1]
@@ -611,44 +650,58 @@ function _assemble_and_solve!(ws::GalerkinWorkspace,
         i = cell1.map[ipert, 1]   # ip=0
         j = cell1.map[jpert, 2]   # ip=1
         if i <= ws.ndim && j <= ws.ndim
-            ws.mat[offset + i - j, j, 1] += Imat0[ipert, jpert]
-            ws.mat[offset + i - j, j, 2] += Imat0[ipert, jpert]
+            ws.mat[offset+i-j, j, 1] += Imat0[ipert, jpert]
+            ws.mat[offset+i-j, j, 2] += Imat0[ipert, jpert]
         end
     end
 
-    # Apply parity BCs for each solution (isol=1: odd, isol=2: even).
-    # Mirrors deltac_set_boundary: for each isol, build a modified local
-    # matrix for ip=0..1 of cell 1, then write it into the global matrix.
+    # Apply parity BCs for each solution.
+    #   isol=1 → "odd mode" = PHYSICS TEARING channel
+    #            (W'(0)=0 → W even across x=0; N(0)=0, Θ(0)=0 → N,Θ odd).
+    #            Even W ⇒ sheet-current reconnecting mode. This is the Δ_+
+    #            of Glasser-Wang-Park 2016.
+    #   isol=2 → "even mode" = PHYSICS INTERCHANGE channel
+    #            (W(0)=0 → W odd; N'(0)=0, Θ'(0)=0 → N,Θ even). Non-reconnecting;
+    #            carries Glasser stabilization. This is GWP Δ_−.
+    # The raw ordering out of this loop is therefore (tearing, interchange).
+    # Downstream code receives an `InnerLayerResponse` whose fields are named
+    # by physics channel, not by parity label, eliminating the ambiguity.
     for isol in 1:2
         # Zero out ip=0 rows in the global matrix
         for ipert in 1:mpert
             i = cell1.map[ipert, 1]  # ip=0 DOFs
-            if i > ws.ndim; continue; end
+            if i > ws.ndim
+                continue
+            end
             for jj in max(1, i - ws.kl):min(ws.ndim, i + ws.kl)
-                ws.mat[offset + i - jj, jj, isol] = 0
+                ws.mat[offset+i-jj, jj, isol] = 0
             end
         end
-        # Odd parity (isol=1): W'(0)=0, N(0)=0, Θ(0)=0
+        # isol=1 (tearing, "odd"): W'(0)=0, N(0)=0, Θ(0)=0
         # → row=W(ip=0), col=W(ip=1): A[map[1,1], map[1,2]] = 1
         # → row=N(ip=0), col=N(ip=0): A[map[2,1], map[2,1]] = 1
         # → row=Θ(ip=0), col=Θ(ip=0): A[map[3,1], map[3,1]] = 1
-        # Even parity (isol=2): W(0)=0, N'(0)=0, Θ'(0)=0
+        # isol=2 (interchange, "even"): W(0)=0, N'(0)=0, Θ'(0)=0
         # → row=W(ip=0), col=W(ip=0): A[map[1,1], map[1,1]] = 1
         # → row=N(ip=0), col=N(ip=1): A[map[2,1], map[2,2]] = 1
         # → row=Θ(ip=0), col=Θ(ip=1): A[map[3,1], map[3,2]] = 1
         if isol == 1
-            i = cell1.map[1, 1]; j = cell1.map[1, 2]
-            ws.mat[offset + i - j, j, isol] = 1
+            i = cell1.map[1, 1]
+            j = cell1.map[1, 2]
+            ws.mat[offset+i-j, j, isol] = 1
             for ipert in 2:3
-                i = cell1.map[ipert, 1]; j = cell1.map[ipert, 1]
-                ws.mat[offset + i - j, j, isol] = 1
+                i = cell1.map[ipert, 1]
+                j = cell1.map[ipert, 1]
+                ws.mat[offset+i-j, j, isol] = 1
             end
         else
-            i = cell1.map[1, 1]; j = cell1.map[1, 1]
-            ws.mat[offset + i - j, j, isol] = 1
+            i = cell1.map[1, 1]
+            j = cell1.map[1, 1]
+            ws.mat[offset+i-j, j, isol] = 1
             for ipert in 2:3
-                i = cell1.map[ipert, 1]; j = cell1.map[ipert, 2]
-                ws.mat[offset + i - j, j, isol] = 1
+                i = cell1.map[ipert, 1]
+                j = cell1.map[ipert, 2]
+                ws.mat[offset+i-j, j, isol] = 1
             end
         end
         for ipert in 1:mpert
@@ -659,14 +712,19 @@ function _assemble_and_solve!(ws::GalerkinWorkspace,
         end
     end
 
-    # Solve for each parity using LAPACK banded LU (gbtrf! + gbtrs!)
-    n = ws.ndim; kl = ws.kl; ku = kl
+    # Solve for each parity using LAPACK banded LU (gbtrf! + gbtrs!).
+    # Reuse the preallocated `ab_buf` / `rhs_buf` instead of `copy`, which
+    # avoided two (ldab × ndim) ComplexF64 allocations per call (≈7 MiB at
+    # ndim=3000).
+    n = ws.ndim
+    kl = ws.kl
+    ku = kl
     for isol in 1:2
-        ab = copy(ws.mat[:, :, isol])
-        rhs_col = copy(ws.rhs[:, isol])
-        ab, ipiv = LinearAlgebra.LAPACK.gbtrf!(kl, ku, n, ab)
-        LinearAlgebra.LAPACK.gbtrs!('N', kl, ku, n, ab, ipiv, rhs_col)
-        ws.sol[:, isol] .= rhs_col
+        copyto!(ws.ab_buf, @view(ws.mat[:, :, isol]))
+        copyto!(ws.rhs_buf, @view(ws.rhs[:, isol]))
+        _, ipiv = LinearAlgebra.LAPACK.gbtrf!(kl, ku, n, ws.ab_buf)
+        LinearAlgebra.LAPACK.gbtrs!('N', kl, ku, n, ws.ab_buf, ipiv, ws.rhs_buf)
+        ws.sol[:, isol] .= ws.rhs_buf
     end
 end
 
@@ -678,18 +736,22 @@ end
     solve_inner(::GGJModel{:galerkin}, params::GGJParameters, γ::Number;
                 kmax::Int=8, nx::Int=512, nq::Int=4, pfac::Float64=1.0,
                 cutoff::Int=5, xfac::Float64=1.0, tol_res::Float64=1e-5)
-                -> SVector{2,ComplexF64}
+                -> InnerLayerResponse
 
 Solve the GGJ inner-layer matching problem using the Hermite-cubic finite
-element (Galerkin) method. Direct port of rmatch/deltac.f in the
-"resonant + noexp + inps" configuration.
+element (Galerkin) method, in the "resonant + noexp + inps" configuration.
+
+Returns an `InnerLayerResponse(tearing, interchange)` with rescaling
+applied. `tearing` comes from `isol=1` (W even, N/Θ odd — "odd mode";
+reconnecting channel, GWP Δ_+); `interchange` comes from `isol=2`
+(W odd, N/Θ even — "even mode"; Glasser stabilization channel, GWP Δ_−).
 
-Returns `(Δ₁, Δ₂)` with rescaling applied. The ordering matches deltac.f's
-output convention (swapped relative to deltar.f).
+The two parity channels are returned in named fields, avoiding the
+ambiguity between parity-by-W and parity-by-N,Θ conventions.
 """
 function solve_inner(::GGJModel{:galerkin}, params::GGJParameters, γ::Number;
-                     kmax::Int=8, nx::Int=512, nq::Int=4, pfac::Float64=1.0,
-                     cutoff::Int=5, xfac::Float64=1.0, tol_res::Float64=1e-5)
+    kmax::Int=8, nx::Int=512, nq::Int=4, pfac::Float64=1.0,
+    cutoff::Int=5, xfac::Float64=1.0, tol_res::Float64=1e-5)
     Q = inner_Q(params, γ)
 
     # Build asymptotic cache and determine grid parameters
@@ -698,18 +760,20 @@ function solve_inner(::GGJModel{:galerkin}, params::GGJParameters, γ::Number;
 
     # Build grid and workspace
     ws = _build_grid_and_workspace(nx, xmax_info.xmax, xmax_info.dx1, xmax_info.dx2,
-                                   pfac, cutoff, nq)
+        pfac, cutoff, nq)
 
     # Assemble and solve
     _assemble_and_solve!(ws, params, Q, cache; nq=nq, tol_res=tol_res)
 
-    # Extract delta from the resonant cell's emap DOF
+    # Extract delta from the resonant cell's emap DOF. isol=1 = tearing,
+    # isol=2 = interchange (see BC block above for the parity derivation).
     res_cell = ws.cells[ws.nx]
     emap1 = res_cell.emap[1]
     Δ_raw = SVector{2,ComplexF64}(ws.sol[emap1, 1], ws.sol[emap1, 2])
 
-    # Apply deltac.f's swap convention (line 194-196)
-    Δ_swapped = SVector{2,ComplexF64}(Δ_raw[2], Δ_raw[1])
+    # Rescaling is linear & diagonal; apply to the (tearing, interchange)
+    # pair directly, no parity swap.
+    Δ_rescaled = rescale_delta(Δ_raw, params)
 
-    return rescale_delta(Δ_swapped, params)
+    return InnerLayerResponse(Δ_rescaled[1], Δ_rescaled[2])
 end
diff --git a/src/InnerLayer/GGJ/InnerAsymptotics.jl b/src/Tearing/InnerLayer/GGJ/InnerAsymptotics.jl
similarity index 73%
rename from src/InnerLayer/GGJ/InnerAsymptotics.jl
rename to src/Tearing/InnerLayer/GGJ/InnerAsymptotics.jl
index 4f4de84a9..436bfa1af 100644
--- a/src/InnerLayer/GGJ/InnerAsymptotics.jl
+++ b/src/Tearing/InnerLayer/GGJ/InnerAsymptotics.jl
@@ -1,7 +1,6 @@
 # InnerAsymptotics.jl
 #
 # Wasow asymptotic basis ("inps" basis) for the GGJ inner-layer system.
-# Direct port of rmatch/inps.f.
 #
 # Reference: A. H. Glasser & Z. R. Wang, Phys. Plasmas 27, 012506 (2020),
 # Sections II.A–II.G (Eqs. 4–53). Notation map:
@@ -15,9 +14,6 @@
 #   Y0, R     -> Eq. 49        (lowest-order solution and Frobenius exponents)
 #   Z_k       -> Eq. 52        (Y-series in shifted-exponent form)
 #   U(x)      -> Eq. 53        (final asymptotic solution at large x)
-#
-# This file ports `inps_tjmat`, `inps_lyap_solve`, `inps_split`, `inps_coefs`,
-# `inps_horner`, `inps_ua`, `inps_delta`, and `inps_xmax` from the Fortran.
 
 using LinearAlgebra
 using StaticArrays
@@ -79,7 +75,6 @@ end
 
 # -----------------------------------------------------------------------
 # Step 1: build T, Tinv, A_0..A_2, J_0..J_2.
-# Mirrors inps_tjmat (inps.f lines 145–233).
 # -----------------------------------------------------------------------
 
 function _build_tjmat(p::GGJParameters, Q::ComplexF64)
@@ -92,27 +87,27 @@ function _build_tjmat(p::GGJParameters, Q::ComplexF64)
     q2 = q * q
     λ = 1 / sqrt(q)
 
-    # T (Eq. 7) — Fortran constructs this with column-major RESHAPE; the
-    # listing below is row-major Julia order matching that layout.
+    # T (Eq. 7) — built by column-major reshape; the listing below is
+    # row-major Julia order matching that layout.
     T = @SMatrix ComplexF64[
-        1   0           h*q             q2/λ        h*q          -q2/λ
-        0   0           0              -1/λ          0            1/λ
-        0   0          -1/λ             0            1/λ          0
-        0   1          -h/λ            -q2          h/λ          -q2
-        0   0           0               1            0            1
-        0   0           1               0            1            0
+        1 0 h*q q2/λ h*q -q2/λ
+        0 0 0 -1/λ 0 1/λ
+        0 0 -1/λ 0 1/λ 0
+        0 1 -h/λ -q2 h/λ -q2
+        0 0 0 1 0 1
+        0 0 1 0 1 0
     ]
 
     Tinv = @SMatrix ComplexF64[
-        1    q2     0          0     0          -h*q
-        0    0     -h          1     q2          0
-        0    0     -λ/2        0     0           1/2
-        0   -λ/2    0          0     1/2         0
-        0    0      λ/2        0     0           1/2
-        0    λ/2    0          0     1/2         0
+        1 q2 0 0 0 -h*q
+        0 0 -h 1 q2 0
+        0 0 -λ/2 0 0 1/2
+        0 -λ/2 0 0 1/2 0
+        0 0 λ/2 0 0 1/2
+        0 λ/2 0 0 1/2 0
     ]
 
-    # A_0, A_1, A_2 — see inps_tjmat lines 188–202. Build mutable then freeze.
+    # A_0, A_1, A_2 — physical-system coefficient matrices. Build mutable then freeze.
     A0 = zeros(ComplexF64, 6, 6)
     A1 = zeros(ComplexF64, 6, 6)
     A2 = zeros(ComplexF64, 6, 6)
@@ -158,7 +153,6 @@ end
 
 # -----------------------------------------------------------------------
 # Step 2: closed-form Lyapunov solve for the splitting transformation.
-# Mirrors inps_lyap_solve (inps.f lines 241–270).
 #
 # Given a 6×6 K, returns (B, P) such that:
 #   - B is block-diagonal with B[r1,r1] = K[r1,r1] and B[r2,r2] = K[r2,r2]
@@ -172,8 +166,10 @@ function _lyap_solve(K::SMatrix{6,6,ComplexF64}, λ::ComplexF64)
     Pm = zeros(ComplexF64, 6, 6)
 
     # B is block-diagonal in the (r1, r2) split.
-    Bm[1, 1] = K[1, 1]; Bm[1, 2] = K[1, 2]
-    Bm[2, 1] = K[2, 1]; Bm[2, 2] = K[2, 2]
+    Bm[1, 1] = K[1, 1]
+    Bm[1, 2] = K[1, 2]
+    Bm[2, 1] = K[2, 1]
+    Bm[2, 2] = K[2, 2]
     for i in 3:6, j in 3:6
         Bm[i, j] = K[i, j]
     end
@@ -182,22 +178,22 @@ function _lyap_solve(K::SMatrix{6,6,ComplexF64}, λ::ComplexF64)
     @inbounds begin
         Pm[2, 3] = -K[2, 3] / λ
         Pm[2, 4] = -K[2, 4] / λ
-        Pm[2, 5] =  K[2, 5] / λ
-        Pm[2, 6] =  K[2, 6] / λ
+        Pm[2, 5] = K[2, 5] / λ
+        Pm[2, 6] = K[2, 6] / λ
         Pm[1, 3] = -(K[1, 3] + Pm[2, 3]) / λ
         Pm[1, 4] = -(K[1, 4] + Pm[2, 4]) / λ
-        Pm[1, 5] =  (K[1, 5] + Pm[2, 5]) / λ
-        Pm[1, 6] =  (K[1, 6] + Pm[2, 6]) / λ
+        Pm[1, 5] = (K[1, 5] + Pm[2, 5]) / λ
+        Pm[1, 6] = (K[1, 6] + Pm[2, 6]) / λ
     end
 
     # P[r2, r1] = P[3:6, 1:2] (bottom-left off-diagonal block)
     @inbounds begin
-        Pm[3, 1] =  K[3, 1] / λ
-        Pm[4, 1] =  K[4, 1] / λ
+        Pm[3, 1] = K[3, 1] / λ
+        Pm[4, 1] = K[4, 1] / λ
         Pm[5, 1] = -K[5, 1] / λ
         Pm[6, 1] = -K[6, 1] / λ
-        Pm[3, 2] =  (K[3, 2] - Pm[3, 1]) / λ
-        Pm[4, 2] =  (K[4, 2] - Pm[4, 1]) / λ
+        Pm[3, 2] = (K[3, 2] - Pm[3, 1]) / λ
+        Pm[4, 2] = (K[4, 2] - Pm[4, 1]) / λ
         Pm[5, 2] = -(K[5, 2] - Pm[5, 1]) / λ
         Pm[6, 2] = -(K[6, 2] - Pm[6, 1]) / λ
     end
@@ -206,7 +202,7 @@ function _lyap_solve(K::SMatrix{6,6,ComplexF64}, λ::ComplexF64)
 end
 
 # -----------------------------------------------------------------------
-# Step 3: split recurrence (inps_split lines 278–361).
+# Step 3: split recurrence.
 # Builds B_k, P_k, K6_k for k = 0..kmax+2.
 # -----------------------------------------------------------------------
 
@@ -221,21 +217,21 @@ function _split_recurrence(J::NTuple{3,SMatrix{6,6,ComplexF64}}, λ::ComplexF64,
     B[1] = J[1]                            # B_0 = J_0
     K6[1] = J[1]
 
-    for k in 1:(kmax + 2)
+    for k in 1:(kmax+2)
         kk = k + 1                         # 1-based slot for K6/B/P at index k
         # K6_k starts as J_k (only k = 1, 2 contribute), else 0
-        Kacc = (k <= 2) ? J[k + 1] : Z6
+        Kacc = (k <= 2) ? J[k+1] : Z6
         # +2*(k-1)*P_{k-1}  (k > 1 only)
         if k > 1
             Kacc = Kacc + ComplexF64(2 * (k - 1)) * P[k]
         end
         # convolution: l = 1..k-1
-        for l in 1:(k - 1)
+        for l in 1:(k-1)
             kml = k - l
             if kml <= 2
-                Kacc = Kacc + J[kml + 1] * P[l + 1]
+                Kacc = Kacc + J[kml+1] * P[l+1]
             end
-            Kacc = Kacc - P[l + 1] * B[kml + 1]
+            Kacc = Kacc - P[l+1] * B[kml+1]
         end
         K6[kk] = Kacc
         Bk, Pk = _lyap_solve(Kacc, λ)
@@ -246,12 +242,12 @@ function _split_recurrence(J::NTuple{3,SMatrix{6,6,ComplexF64}}, λ::ComplexF64,
 end
 
 # -----------------------------------------------------------------------
-# Step 4: coefs recurrence (inps_coefs lines 369–496).
+# Step 4: coefs recurrence.
 # Builds K2_k, Q_k, C_k for k = 0..kmax+2 and D_k, E_k, Y_k, Z_k.
 # -----------------------------------------------------------------------
 
 function _coefs(B::Vector{SMatrix{6,6,ComplexF64,36}},
-                R::SVector{2,Float64}, kmax::Int)
+    R::SVector{2,Float64}, kmax::Int)
 
     N = kmax + 3
     Z2 = zero(SMatrix{2,2,ComplexF64})
@@ -267,53 +263,54 @@ function _coefs(B::Vector{SMatrix{6,6,ComplexF64,36}},
     B0_11 = SMatrix{2,2,ComplexF64}(@view B[1][1:2, 1:2])
     Cm[1] = B0_11
 
-    for k in 1:(kmax + 2)
-        Bk_11 = SMatrix{2,2,ComplexF64}(@view B[k + 1][1:2, 1:2])
+    for k in 1:(kmax+2)
+        Bk_11 = SMatrix{2,2,ComplexF64}(@view B[k+1][1:2, 1:2])
         K2acc = Bk_11 + ComplexF64(2 * (k - 1)) * Qm[k]
-        for l in 1:(k - 1)
-            Bkml_11 = SMatrix{2,2,ComplexF64}(@view B[k - l + 1][1:2, 1:2])
-            K2acc = K2acc + Bkml_11 * Qm[l + 1] - Qm[l + 1] * Cm[k - l + 1]
+        for l in 1:(k-1)
+            Bkml_11 = SMatrix{2,2,ComplexF64}(@view B[k-l+1][1:2, 1:2])
+            K2acc = K2acc + Bkml_11 * Qm[l+1] - Qm[l+1] * Cm[k-l+1]
         end
-        K2[k + 1] = K2acc
+        K2[k+1] = K2acc
 
-        # Q_k = [[0, 0], [-K2[1,1], -K2[1,2]]] (Fortran reshape gives this layout)
-        Qm[k + 1] = @SMatrix ComplexF64[
-            0           0
-           -K2acc[1, 1] -K2acc[1, 2]
+        # Q_k = [[0, 0], [-K2[1,1], -K2[1,2]]] (column-major reshape gives this layout)
+        Qm[k+1] = @SMatrix ComplexF64[
+            0 0
+            -K2acc[1, 1] -K2acc[1, 2]
         ]
         # C_k = [[0, 0], [K2[2,1], K2[1,1] + K2[2,2]]]
-        Cm[k + 1] = @SMatrix ComplexF64[
-            0           0
-           K2acc[2, 1]  K2acc[1, 1] + K2acc[2, 2]
+        Cm[k+1] = @SMatrix ComplexF64[
+            0 0
+            K2acc[2, 1] K2acc[1, 1]+K2acc[2, 2]
         ]
     end
 
-    # Build D_k for k = 0..kmax (inps_coefs lines 408–413).
+    # Build D_k for k = 0..kmax.
     D = Vector{SMatrix{2,2,ComplexF64,4}}(undef, kmax + 1)
     # D_0 = [[0, 1], [C_2[2,1], 3]]
     D[1] = @SMatrix ComplexF64[
-        0          1
+        0 1
         Cm[3][2, 1] 3
     ]
     for k in 1:kmax
-        D[k + 1] = @SMatrix ComplexF64[
-            0                0
-            Cm[k + 3][2, 1]  Cm[k + 2][2, 2]
+        D[k+1] = @SMatrix ComplexF64[
+            0 0
+            Cm[k+3][2, 1] Cm[k+2][2, 2]
         ]
     end
 
     # Lowest-order Y solution and inverse (Eq. 49).
-    r1 = ComplexF64(R[1]); r2 = ComplexF64(R[2])
+    r1 = ComplexF64(R[1])
+    r2 = ComplexF64(R[2])
     Y0 = @SMatrix ComplexF64[
-        1   1
-        r1  r2
+        1 1
+        r1 r2
     ]
     Y0inv = (1 / (r1 - r2)) * @SMatrix ComplexF64[
-        -r2  1
-         r1 -1
+        -r2 1
+        r1 -1
     ]
 
-    # E_k, Z_k, Y_k recurrence (lines 423–438).
+    # E_k, Z_k, Y_k recurrence.
     Y = Vector{SMatrix{2,2,ComplexF64,4}}(undef, kmax + 1)
     Z = Vector{SMatrix{2,2,ComplexF64,4}}(undef, kmax + 1)
     Z[1] = I2
@@ -322,16 +319,16 @@ function _coefs(B::Vector{SMatrix{6,6,ComplexF64,36}},
         # Build Z_k = sum_{l=1..k} E_l * Z_{k-l}, with E_l = Y0inv * D_l * Y0
         Zacc = Z2
         for l in 1:k
-            El = Y0inv * D[l + 1] * Y0
-            Zacc = Zacc + El * Z[k - l + 1]
+            El = Y0inv * D[l+1] * Y0
+            Zacc = Zacc + El * Z[k-l+1]
         end
         # Divide each entry by (R[j] - R[i] - 2k)
         Zk = MMatrix{2,2,ComplexF64}(Zacc)
         for i in 1:2, j in 1:2
             Zk[i, j] = Zk[i, j] / (R[j] - R[i] - 2 * k)
         end
-        Z[k + 1] = SMatrix{2,2,ComplexF64}(Zk)
-        Y[k + 1] = Y0 * Z[k + 1]
+        Z[k+1] = SMatrix{2,2,ComplexF64}(Zk)
+        Y[k+1] = Y0 * Z[k+1]
     end
 
     return K2, Qm, Cm, D, Y0, Y0inv, Y, Z
@@ -362,7 +359,7 @@ function build_asymptotics(params::GGJParameters, Q::ComplexF64; kmax::Int=8)
 
     return InnerAsymptoticsCache(
         params, Q, kmax, λ, R, T, Tinv, J,
-        P, B, K2, Qm, Cm, D, Y0, Y0inv, Y, Z,
+        P, B, K2, Qm, Cm, D, Y0, Y0inv, Y, Z
     )
 end
 
@@ -371,7 +368,6 @@ build_asymptotics(params::GGJParameters, Q::Number; kmax::Int=8) =
 
 # -----------------------------------------------------------------------
 # Horner evaluator with optional fractional-power prefactor.
-# Mirrors inps_horner (inps.f lines 587–631).
 #
 # Computes y[i] = (Σ_{k=0..n} c[i,k] * x^k) * x^rvec[i]
 # and       dy[i] = d/dx of the above.
@@ -381,15 +377,15 @@ build_asymptotics(params::GGJParameters, Q::Number; kmax::Int=8) =
 # -----------------------------------------------------------------------
 
 function _horner(x::Real, c::AbstractMatrix{ComplexF64};
-                 rvec::Union{Nothing,AbstractVector{<:Real}}=nothing,
-                 derivative::Bool=false)
+    rvec::Union{Nothing,AbstractVector{<:Real}}=nothing,
+    derivative::Bool=false)
     nrows, ncols = size(c)
     n = ncols - 1   # highest power
 
     y = ComplexF64.(c[:, ncols])
-    @inbounds for k in (n - 1):-1:0
+    @inbounds for k in (n-1):-1:0
         for i in 1:nrows
-            y[i] = y[i] * x + c[i, k + 1]
+            y[i] = y[i] * x + c[i, k+1]
         end
     end
 
@@ -409,9 +405,9 @@ function _horner(x::Real, c::AbstractMatrix{ComplexF64};
         @inbounds for i in 1:nrows
             dy[i] = c[i, ncols] * (rvec[i] + n)
         end
-        @inbounds for k in (n - 1):-1:0
+        @inbounds for k in (n-1):-1:0
             for i in 1:nrows
-                dy[i] = dy[i] * x + c[i, k + 1] * (rvec[i] + k)
+                dy[i] = dy[i] * x + c[i, k+1] * (rvec[i] + k)
             end
         end
         @inbounds for i in 1:nrows
@@ -422,9 +418,9 @@ function _horner(x::Real, c::AbstractMatrix{ComplexF64};
         @inbounds for i in 1:nrows
             dy[i] = c[i, ncols] * n
         end
-        @inbounds for k in (n - 1):-1:0
+        @inbounds for k in (n-1):-1:0
             for i in 1:nrows
-                dy[i] = dy[i] * x + c[i, k + 1] * k
+                dy[i] = dy[i] * x + c[i, k+1] * k
             end
         end
         @inbounds for i in 1:nrows
@@ -435,19 +431,19 @@ function _horner(x::Real, c::AbstractMatrix{ComplexF64};
 end
 
 # Pack the cache's Y, Qmat, and the [r2, r1] block of P into the row-major
-# Fortran-reshape order used by inps_horner. Returned matrices are
+# reshape order used by `_horner`. Returned matrices are
 # n_components × (kmax+1).
 function _pack_y_coefs(cache::InnerAsymptoticsCache)
     kmax = cache.kmax
     cc = Matrix{ComplexF64}(undef, 4, kmax + 1)
     @inbounds for k in 0:kmax
-        Yk = cache.Y[k + 1]
-        # Fortran column-major reshape of (2,2) → 4 entries:
+        Yk = cache.Y[k+1]
+        # column-major reshape of (2,2) → 4 entries:
         # (Y[1,1], Y[2,1], Y[1,2], Y[2,2])
-        cc[1, k + 1] = Yk[1, 1]
-        cc[2, k + 1] = Yk[2, 1]
-        cc[3, k + 1] = Yk[1, 2]
-        cc[4, k + 1] = Yk[2, 2]
+        cc[1, k+1] = Yk[1, 1]
+        cc[2, k+1] = Yk[2, 1]
+        cc[3, k+1] = Yk[1, 2]
+        cc[4, k+1] = Yk[2, 2]
     end
     return cc
 end
@@ -456,28 +452,28 @@ function _pack_qp_coefs(cache::InnerAsymptoticsCache)
     kmax = cache.kmax
     dd = Matrix{ComplexF64}(undef, 12, kmax + 1)
     @inbounds for k in 0:kmax
-        Qk = cache.Qmat[k + 1]
-        Pk = cache.P[k + 1]
-        # dd[1:4] from Q (column-major reshape of 2×2)
-        dd[1, k + 1] = Qk[1, 1]
-        dd[2, k + 1] = Qk[2, 1]
-        dd[3, k + 1] = Qk[1, 2]
-        dd[4, k + 1] = Qk[2, 2]
+        Qk = cache.Qmat[k+1]
+        Pk = cache.P[k+1]
+        # dd[1:4] from Q (column-major reshape of 2×2 block)
+        dd[1, k+1] = Qk[1, 1]
+        dd[2, k+1] = Qk[2, 1]
+        dd[3, k+1] = Qk[1, 2]
+        dd[4, k+1] = Qk[2, 2]
         # dd[5:12] from P[r2, r1] = P[3:6, 1:2] (4×2 → column-major 8 entries)
-        dd[5,  k + 1] = Pk[3, 1]
-        dd[6,  k + 1] = Pk[4, 1]
-        dd[7,  k + 1] = Pk[5, 1]
-        dd[8,  k + 1] = Pk[6, 1]
-        dd[9,  k + 1] = Pk[3, 2]
-        dd[10, k + 1] = Pk[4, 2]
-        dd[11, k + 1] = Pk[5, 2]
-        dd[12, k + 1] = Pk[6, 2]
+        dd[5, k+1] = Pk[3, 1]
+        dd[6, k+1] = Pk[4, 1]
+        dd[7, k+1] = Pk[5, 1]
+        dd[8, k+1] = Pk[6, 1]
+        dd[9, k+1] = Pk[3, 2]
+        dd[10, k+1] = Pk[4, 2]
+        dd[11, k+1] = Pk[5, 2]
+        dd[12, k+1] = Pk[6, 2]
     end
     return dd
 end
 
 # -----------------------------------------------------------------------
-# Step 5: evaluator (inps_ua, inps.f lines 504–579).
+# Step 5: evaluator.
 # -----------------------------------------------------------------------
 
 """
@@ -491,12 +487,12 @@ asymptotic solutions of the GGJ system, and (if `derivative=true`) the
 6×2 matrix `dU` of their derivatives `dU/dx`.
 
 If `apply_T=false`, the result is left in the J-rotated coordinate basis
-(used by `inps_delta` for residual checks). The default `apply_T=true`
+(used by `asymptotic_residual` for residual checks). The default `apply_T=true`
 returns the solutions in the original 6-component first-order-system
-basis used by `inpso_get_uv` and the shooting / Galerkin solvers.
+basis used by `_physical_uv` and the shooting / Galerkin solvers.
 """
 function evaluate_asymptotics(cache::InnerAsymptoticsCache, x::Real;
-                              derivative::Bool=true, apply_T::Bool=true)
+    derivative::Bool=true, apply_T::Bool=true)
     xfac = 1.0 / (x * x)
     R = cache.R
     rvec = SVector(-R[1] / 2, -R[1] / 2, -R[2] / 2, -R[2] / 2)
@@ -508,16 +504,17 @@ function evaluate_asymptotics(cache::InnerAsymptoticsCache, x::Real;
     zz, dzz = _horner(xfac, dd; derivative=derivative)
 
     # Reshape yy → y (2×2), zz → q (2×2) and p21 (4×2)
-    y  = SMatrix{2,2,ComplexF64}(yy[1], yy[2], yy[3], yy[4])
-    q  = SMatrix{2,2,ComplexF64}(zz[1], zz[2], zz[3], zz[4])
+    y = SMatrix{2,2,ComplexF64}(yy[1], yy[2], yy[3], yy[4])
+    q = SMatrix{2,2,ComplexF64}(zz[1], zz[2], zz[3], zz[4])
     p21 = SMatrix{4,2,ComplexF64}(zz[5], zz[6], zz[7], zz[8],
-                                  zz[9], zz[10], zz[11], zz[12])
+        zz[9], zz[10], zz[11], zz[12])
 
     # Splitting matrix pp (6×2): top 2×2 = I, bottom 4×2 = p21.
     pp_m = zeros(ComplexF64, 6, 2)
-    pp_m[1, 1] = 1; pp_m[2, 2] = 1
+    pp_m[1, 1] = 1
+    pp_m[2, 2] = 1
     @inbounds for i in 1:4, j in 1:2
-        pp_m[i + 2, j] = p21[i, j]
+        pp_m[i+2, j] = p21[i, j]
     end
     pp = SMatrix{6,2,ComplexF64}(pp_m)
 
@@ -530,26 +527,26 @@ function evaluate_asymptotics(cache::InnerAsymptoticsCache, x::Real;
         # Apply chain rule: derivatives from horner are wrt xfac, convert to
         # derivatives wrt x via factor (-2*xfac/x) = d(xfac)/dx.
         chain = -2 * xfac / x
-        dy_v  = chain .* dyy
-        dz_v  = chain .* dzz
+        dy_v = chain .* dyy
+        dz_v = chain .* dzz
 
         dy = SMatrix{2,2,ComplexF64}(dy_v[1], dy_v[2], dy_v[3], dy_v[4])
         dq = SMatrix{2,2,ComplexF64}(dz_v[1], dz_v[2], dz_v[3], dz_v[4])
         dp21 = SMatrix{4,2,ComplexF64}(dz_v[5], dz_v[6], dz_v[7], dz_v[8],
-                                       dz_v[9], dz_v[10], dz_v[11], dz_v[12])
+            dz_v[9], dz_v[10], dz_v[11], dz_v[12])
 
         dpp_m = zeros(ComplexF64, 6, 2)
         @inbounds for i in 1:4, j in 1:2
-            dpp_m[i + 2, j] = dp21[i, j]
+            dpp_m[i+2, j] = dp21[i, j]
         end
         dpp = SMatrix{6,2,ComplexF64}(dpp_m)
 
-        dsmat = @SMatrix ComplexF64[0 0; 0 (-2 * xfac / x)]
+        dsmat = @SMatrix ComplexF64[0 0; 0 (-2*xfac/x)]
         dqsy = q * smat * dy + q * dsmat * y + dq * smat * y
         dU = pp * dqsy + dpp * qsy
 
         if apply_T
-            U  = cache.T * U
+            U = cache.T * U
             dU = cache.T * dU
         end
         return U, dU
@@ -562,14 +559,14 @@ function evaluate_asymptotics(cache::InnerAsymptoticsCache, x::Real;
 end
 
 # -----------------------------------------------------------------------
-# Step 6: residual `delta` and adaptive xmax (inps_delta + inps_xmax).
+# Step 6: residual `delta` and adaptive xmax.
 # -----------------------------------------------------------------------
 
 """
     asymptotic_residual(cache::InnerAsymptoticsCache, x::Real) -> SVector{2,Float64}
 
 Compute the residual `D₆(x)` of the asymptotic basis at `x` for each of
-the two algebraic columns. Mirrors `inps_delta` (inps.f lines 713–752):
+the two algebraic columns:
 returns `‖dU − x·matrix·U‖∞ / max(‖dU‖∞, ‖x·matrix·U‖∞)` per column,
 where `matrix = J₀ + xfac·J₁ + xfac²·J₂` is the J-rotated coefficient
 matrix.
@@ -586,15 +583,17 @@ function asymptotic_residual(cache::InnerAsymptoticsCache, x::Real)
         M = M + xfac * xfac * cache.J[3]
     end
 
-    # Match the Fortran convention: matvec(:,:,1) = dU; matvec(:,:,2) = -x*M*U;
-    # matvec(:,:,0) = sum. delta(j) = ||matvec(:,j,0)||∞ / max(||matvec(:,j,1)||∞, ||matvec(:,j,2)||∞).
+    # matvec(:,:,1) = dU; matvec(:,:,2) = -x*M*U; matvec(:,:,0) = sum.
+    # delta(j) = ||matvec(:,j,0)||∞ / max(||matvec(:,j,1)||∞, ||matvec(:,j,2)||∞).
     matvec1 = dU
     matvec2 = -x * (M * U)
     matvec0 = matvec1 + matvec2
 
     delta = MVector{2,Float64}(0.0, 0.0)
     @inbounds for j in 1:2
-        n0 = 0.0; n1 = 0.0; n2 = 0.0
+        n0 = 0.0
+        n1 = 0.0
+        n2 = 0.0
         for i in 1:6
             n0 = max(n0, abs(matvec0[i, j]))
             n1 = max(n1, abs(matvec1[i, j]))
@@ -614,16 +613,15 @@ end
 
 Sweep `x` log-uniformly upward from `10^xlogmin` and return the smallest
 `x` at which `max(asymptotic_residual(cache, x)) < eps`. Also returns the
-`InnerAsymptoticsCache` it built so callers can reuse it. Mirrors
-`inps_xmax` (inps.f lines 639–705).
+`InnerAsymptoticsCache` it built so callers can reuse it.
 
 Throws an `ErrorException` if no `x` in the sweep range achieves the
 target tolerance.
 """
 function pick_xmax(params::GGJParameters, Q::ComplexF64;
-                   eps::Float64=1e-7, kmax::Int=8,
-                   xlogmin::Float64=-1.0, xlogmax::Float64=4.0,
-                   dxlog::Float64=0.01)
+    eps::Float64=1e-7, kmax::Int=8,
+    xlogmin::Float64=-1.0, xlogmax::Float64=4.0,
+    dxlog::Float64=0.01)
     cache = build_asymptotics(params, Q; kmax=kmax)
     dxfac = 10.0^dxlog
     xlog = xlogmin
diff --git a/src/Tearing/InnerLayer/GGJ/LayerInputs.jl b/src/Tearing/InnerLayer/GGJ/LayerInputs.jl
new file mode 100644
index 000000000..eb9f7be0f
--- /dev/null
+++ b/src/Tearing/InnerLayer/GGJ/LayerInputs.jl
@@ -0,0 +1,134 @@
+# LayerInputs.jl (GGJ)
+#
+# Build per-surface `GGJParameters` from a solved `PlasmaEquilibrium`, the
+# `SingType` rational-surface list (each carrying a populated
+# `restype::ResistGeometry` from `ForceFreeStates.resist_eval_all!`), and a
+# `KineticProfiles` object — the same three ingredients `build_slayer_inputs`
+# consumes. Produces the (E, F, G, H, K, τ_A, τ_R) tuple that GGJ's
+# `solve_inner` needs, with τ_A / τ_R built from kinetic profiles using the
+# same Spitzer resistivity and mass-density formulas SLAYER uses.
+#
+# Deliberately does *not* use any hardcoded `ne`/`te` defaults. The kinetic
+# content enters through `profiles` alone; this keeps GGJ and SLAYER using
+# bit-identical plasma inputs when both are driven by the same
+# `KineticProfiles`.
+
+using ...Utilities: KineticProfiles
+using ....Utilities.PhysicalConstants: MU_0, M_E, M_P, E_CHG, EPS_0
+using ....Utilities.NeoclassicalResistivity
+using ....Utilities.NeoclassicalResistivity: NeoResistivityModel, SpitzerModel,
+    SauterNeoModel, RedlNeoModel,
+    coulomb_log_e, eta_spitzer, nu_star_e, eta_neoclassical
+using ....ForceFreeStates: ResistGeometry
+
+"""
+    build_ggj_inputs(equil, sings, profiles; mu_i=2.0, zeff=1.0,
+                      v1_scale=1.0,
+                      resistivity_model::NeoResistivityModel=SpitzerModel(),
+                      lnLambda_form::Symbol=:nrl) -> Vector{GGJParameters}
+
+Construct a `GGJParameters` for each rational surface in `sings`. Each
+surface's geometric coefficients (E, F, G, H, K, M) come from the
+`sing.restype::ResistGeometry` populated by `resist_eval_all!`. Kinetic
+timescales are derived from the `KineticProfiles` at `sing.psifac`:
+
+```
+ρ(ψ)   = μ_i · m_p · n_e(ψ)
+η(ψ)   = eta_neoclassical(model, n_e, T_e, Z_eff, f_t, ν*_e)     [Ω·m]
+τ_A    = √(ρ · M · μ_0) / |2π · n · q' · χ₁ / V'|                 [Alfvén time]
+τ_R    = (⟨B²/|∇ψ|²⟩ / ⟨B²⟩) · μ_0 / η                             [resistive diffusion]
+```
+
+The mode number `n` is taken from `sings[k].n[1]` (first resonant mode at
+the surface). `χ₁ = 2π · psio`. The `v1_scale` kwarg is an optional
+multiplicative factor on `V'` in the τ_A denominator (a `v1 / volume`
+normalization option); default `1.0` means use the raw `V'`.
+
+# Resistivity model
+
+`resistivity_model` selects the η closure:
+
+  - `SpitzerModel()` (default) — Sauter 1999 Eq. 18a (Zeff-aware Spitzer),
+    with the NRL Coulomb log.
+  - `SauterNeoModel()` — multiplies by Sauter 1999 F_33 using f_t and ν*_e
+    from the surface's `ResistGeometry`. Produces the physically-correct
+    trapped-particle-corrected η for H-mode tearing stability.
+  - `RedlNeoModel()` — Redl 2021 F_33 (improved high-ν* fit).
+
+`lnLambda_form` selects `:nrl` (default), `:sauter`, or `:wesson`.
+
+Throws if any surface's `restype` is still `nothing` — call
+`ForceFreeStates.resist_eval_all!(intr, equil)` first.
+"""
+function build_ggj_inputs(equil, sings, profiles::KineticProfiles;
+    mu_i::Real=2.0, zeff::Real=1.0,
+    v1_scale::Real=1.0,
+    resistivity_model::NeoResistivityModel=SpitzerModel(),
+    lnLambda_form::Symbol=:nrl)
+    psio = equil.psio
+    chi1 = 2π * psio
+
+    out = Vector{GGJParameters}(undef, length(sings))
+    for (k, sing) in enumerate(sings)
+        rg = sing.restype
+        rg === nothing &&
+            throw(
+                ArgumentError(
+                    "build_ggj_inputs: surface $k has " *
+                    "restype = nothing. Call " *
+                    "ForceFreeStates.resist_eval_all!(intr, equil) " *
+                    "after sing_find! to populate it."
+                )
+            )
+        rg isa ResistGeometry ||
+            throw(ArgumentError("build_ggj_inputs: surface $k has " *
+                                "restype of unexpected type $(typeof(rg))."))
+
+        # Kinetic profiles at this surface
+        prof = profiles(sing.psifac)
+        n_e = prof.n_e          # [m⁻³]
+        t_e = prof.T_e          # [eV]
+
+        # Shared Coulomb log and resistivity closure (identical to SLAYER
+        # when the same resistivity_model is selected).
+        lnLamb = coulomb_log_e(n_e, t_e; form=lnLambda_form)
+        if resistivity_model isa SpitzerModel
+            eta_use = eta_spitzer(n_e, t_e, zeff; lnLamb=lnLamb)
+        else
+            nuestar = nu_star_e(n_e, t_e, rg.R_major, rg.eps_local,
+                sing.q, zeff; lnLamb=lnLamb)
+            eta_use = eta_neoclassical(resistivity_model, n_e, t_e, zeff,
+                rg.f_trap, nuestar; lnLamb=lnLamb)
+        end
+        rho = mu_i * M_P * n_e
+
+        # Alfvén time at the rational surface
+        n_tor = Int(sing.n[1])
+        v1 = rg.v1_local * v1_scale
+        taua = sqrt(rho * rg.M * MU_0) /
+               abs(2π * n_tor * sing.q1 * chi1 / v1)
+
+        # Resistive diffusion time
+        taur = (rg.avg_bsq_over_dpsisq / rg.avg_bsq) * MU_0 / eta_use
+
+        # dV/dψ normalized by total plasma volume (`v1 = v1/volume`). This is
+        # the `v1` consumed by `rescale_delta` as v1^(2p1); NOT the raw V'
+        # used in τ_A above.
+        equil.params.volume === nothing &&
+            throw(
+                ArgumentError(
+                    "build_ggj_inputs: equil.params.volume " *
+                    "is nothing. Ensure the equilibrium " *
+                    "solver populated the total plasma " *
+                    "volume before building GGJ inputs."
+                )
+            )
+        v1_norm = rg.v1_local / equil.params.volume
+
+        out[k] = GGJParameters(;
+            E=rg.E, F=rg.F, G=rg.G, H=rg.H, K=rg.K, M=rg.M,
+            taua=taua, taur=taur, v1=v1_norm, ising=k
+        )
+    end
+    return out
+end
diff --git a/src/InnerLayer/GGJ/Reference.jl b/src/Tearing/InnerLayer/GGJ/Reference.jl
similarity index 80%
rename from src/InnerLayer/GGJ/Reference.jl
rename to src/Tearing/InnerLayer/GGJ/Reference.jl
index 8a2f135ce..422ac3e0a 100644
--- a/src/InnerLayer/GGJ/Reference.jl
+++ b/src/Tearing/InnerLayer/GGJ/Reference.jl
@@ -1,8 +1,7 @@
 # Reference.jl
 #
 # Hard-coded benchmark parameter sets for cross-validation of the GGJ
-# inner-layer solvers. Values are taken from published papers and the
-# corresponding Fortran RMATCH test cases.
+# inner-layer solvers. Values are taken from published papers.
 
 """
     glasser_wang_2020_eq55() -> GGJParameters
@@ -16,8 +15,8 @@ Timescale parameters (taua, taur, v1) are set to canonical normalization;
 callers should override them for physical cases.
 """
 function glasser_wang_2020_eq55(; taua::Float64=1.0, taur::Float64=1e6, v1::Float64=1.0)
-    return GGJParameters(
-        E=-3.369e-2, F=2.409e-3, G=8.950e-1, H=1.292e-1, K=2.332e-1;
-        taua=taua, taur=taur, v1=v1,
+    return GGJParameters(;
+        E=-3.369e-2, F=2.409e-3, G=8.950e-1, H=1.292e-1, K=2.332e-1,
+        taua=taua, taur=taur, v1=v1
     )
 end
diff --git a/src/InnerLayer/GGJ/Shooting.jl b/src/Tearing/InnerLayer/GGJ/Shooting.jl
similarity index 71%
rename from src/InnerLayer/GGJ/Shooting.jl
rename to src/Tearing/InnerLayer/GGJ/Shooting.jl
index ca085dabe..3086d5d7a 100644
--- a/src/InnerLayer/GGJ/Shooting.jl
+++ b/src/Tearing/InnerLayer/GGJ/Shooting.jl
@@ -1,19 +1,17 @@
 # Shooting.jl
 #
-# Stable backward shooting solver for the GGJ inner-layer model. Direct
-# Julia port of rmatch/deltar.f. Integrates the 4×4 origin-diagonalized
-# resistive-layer ODE from `tmax` (large-x asymptotic regime) backward to
-# a small `tmin` (origin Frobenius regime), then projects onto the local
-# Frobenius basis at the origin and reads off the parity-projected
-# matching data via `deltar_ratio`.
+# Stable backward shooting solver for the GGJ inner-layer model. Integrates
+# the 4×4 origin-diagonalized resistive-layer ODE from `tmax` (large-x
+# asymptotic regime) backward to a small `tmin` (origin Frobenius regime),
+# then projects onto the local Frobenius basis at the origin and reads off
+# the parity-projected matching data.
 #
 # The 4 origin Frobenius exponents are `al0 = (p1, 1/2, −p1, −1/2)` where
 # `p1 = √(−D_I)` (Mercier-stable required). The two "small" (singular)
 # modes are columns 3 and 4 (`x^{−p1}` and `x^{−1/2}`); the two "large"
 # (smooth) modes are columns 1 and 2 (`x^{p1}` and `x^{1/2}`).
 #
-# Reference: A. H. Glasser, Phys. Plasmas **23**, 072505 (2016) and the
-# Fortran rmatch/deltar.f.
+# Reference: A. H. Glasser, Phys. Plasmas **23**, 072505 (2016).
 
 using LinearAlgebra
 using SpecialFunctions: gamma
@@ -22,8 +20,8 @@ using OrdinaryDiffEq
 """
     GGJShootingSystem
 
-Precomputed origin- and infinity-side arrays for the deltar.f-style
-backward shoot. Construct via [`_build_shooting_system`](@ref).
+Precomputed origin- and infinity-side arrays for the backward shoot.
+Construct via [`_build_shooting_system`](@ref).
 """
 struct GGJShootingSystem
     params::GGJParameters
@@ -46,47 +44,51 @@ struct GGJShootingSystem
 end
 
 # -----------------------------------------------------------------------
-# Origin arrays — port of deltar_origin (deltar.f lines 333–482).
+# Origin arrays for the origin-diagonalized Frobenius basis.
 # -----------------------------------------------------------------------
 
 function _build_origin_arrays(p::GGJParameters, Q::ComplexF64; nps::Int=8, rtol::Float64=1e-6)
     p1v = p1(p)
     pplus = -0.5 + p1v
 
-    e = ComplexF64(p.E); f = ComplexF64(p.F); h = ComplexF64(p.H)
-    g = ComplexF64(p.G); k = ComplexF64(p.K); q = Q
+    e = ComplexF64(p.E)
+    f = ComplexF64(p.F)
+    h = ComplexF64(p.H)
+    g = ComplexF64(p.G)
+    k = ComplexF64(p.K)
+    q = Q
 
     q3 = q^3
     kk1 = 1 + k * q3
     a1 = (g - k * e) * q * q / kk1
     a2 = sqrt(0.5 / (p1v * q))
-    aplus  = h - 0.5 + p1v
+    aplus = h - 0.5 + p1v
     aminus = h - 0.5 - p1v
 
-    # d0 — see deltar_origin lines 361–376.
+    # d0 — diagonalizing matrix at the origin.
     d0_m = zeros(ComplexF64, 4, 4)
     d0_m[1, 1] = q * a2
     d0_m[1, 2] = 1
     d0_m[1, 3] = -q * a2
     d0_m[1, 4] = 0
     d0_m[2, 2] = 1
-    d0_m[3, 1] =  aplus * a2
+    d0_m[3, 1] = aplus * a2
     d0_m[3, 3] = -aminus * a2
     d0_m[4, 1] = -aplus * a2
     d0_m[4, 2] = -a1
-    d0_m[4, 3] =  aminus * a2
+    d0_m[4, 3] = aminus * a2
     d0_m[4, 4] = 1
 
-    # d0inv — same explicit antisymmetry pattern as deltar_origin lines 380–395.
+    # d0inv — explicit antisymmetry pattern of the origin diagonalizer inverse.
     d0inv_m = zeros(ComplexF64, 4, 4)
-    d0inv_m[1, 1] =  d0_m[3, 3]
-    d0inv_m[2, 2] =  d0_m[4, 4]
-    d0inv_m[1, 2] =  d0_m[4, 3]
-    d0inv_m[2, 1] =  d0_m[3, 4]
-    d0inv_m[3, 3] =  d0_m[1, 1]
-    d0inv_m[4, 4] =  d0_m[2, 2]
-    d0inv_m[3, 4] =  d0_m[2, 1]
-    d0inv_m[4, 3] =  d0_m[1, 2]
+    d0inv_m[1, 1] = d0_m[3, 3]
+    d0inv_m[2, 2] = d0_m[4, 4]
+    d0inv_m[1, 2] = d0_m[4, 3]
+    d0inv_m[2, 1] = d0_m[3, 4]
+    d0inv_m[3, 3] = d0_m[1, 1]
+    d0inv_m[4, 4] = d0_m[2, 2]
+    d0inv_m[3, 4] = d0_m[2, 1]
+    d0inv_m[4, 3] = d0_m[1, 2]
     d0inv_m[1, 3] = -d0_m[1, 3]
     d0inv_m[2, 4] = -d0_m[2, 4]
     d0inv_m[1, 4] = -d0_m[2, 3]
@@ -106,11 +108,11 @@ function _build_origin_arrays(p::GGJParameters, Q::ComplexF64; nps::Int=8, rtol:
     al1_m[3, 1] = q
     al1_m[4, 2] = -q / kk1
 
-    d0_s    = SMatrix{4,4,ComplexF64}(d0_m)
+    d0_s = SMatrix{4,4,ComplexF64}(d0_m)
     d0inv_s = SMatrix{4,4,ComplexF64}(d0inv_m)
-    al1_s   = d0inv_s * SMatrix{4,4,ComplexF64}(al1_m) * d0_s
+    al1_s = d0inv_s * SMatrix{4,4,ComplexF64}(al1_m) * d0_s
 
-    # ups[i, j, k] — origin power-series coefficients (deltar_origin lines 440–460).
+    # ups[i, j, k] — origin power-series coefficients.
     # ups(:,:,1) = I; higher orders from the al1 recurrence.
     ups = zeros(ComplexF64, 4, 4, nps)
     @inbounds for i in 1:4
@@ -120,13 +122,13 @@ function _build_origin_arrays(p::GGJParameters, Q::ComplexF64; nps::Int=8, rtol:
         for j in 1:4, i in 1:4
             acc = ComplexF64(0)
             for l in 1:4
-                acc += al1_s[i, l] * ups[l, j, kk - 1]
+                acc += al1_s[i, l] * ups[l, j, kk-1]
             end
             ups[i, j, kk] = acc / (al0[j] - al0[i] + 2 * (kk - 1))
         end
     end
 
-    # tmin from the truncation-error bound on the origin series (line 470).
+    # tmin from the truncation-error bound on the origin series.
     unmax = 0.0
     @inbounds for j in 1:4, i in 1:4
         unmax = max(unmax, abs(ups[i, j, nps]))
@@ -138,31 +140,35 @@ function _build_origin_arrays(p::GGJParameters, Q::ComplexF64; nps::Int=8, rtol:
 end
 
 # -----------------------------------------------------------------------
-# Infinity arrays — port of deltar_infinity (deltar.f lines 490–666).
+# Infinity arrays — large-t asymptotic basis.
 # With nxps = 1 the higher-order vps recurrence is dead code; vps[:,:,1] = I.
 # -----------------------------------------------------------------------
 
 function _build_infinity_arrays(p::GGJParameters, Q::ComplexF64,
-                                d0inv::SMatrix{4,4,ComplexF64};
-                                rtol::Float64=1e-6, fmax::Float64=1.0)
-    e = ComplexF64(p.E); f = ComplexF64(p.F); h = ComplexF64(p.H)
-    g = ComplexF64(p.G); k = ComplexF64(p.K); q = Q
+    d0inv::SMatrix{4,4,ComplexF64};
+    rtol::Float64=1e-6, fmax::Float64=1.0)
+    e = ComplexF64(p.E)
+    f = ComplexF64(p.F)
+    h = ComplexF64(p.H)
+    g = ComplexF64(p.G)
+    k = ComplexF64(p.K)
+    q = Q
     dr = mercier_dr(p)
 
-    q3     = q^3
-    kk1    = 1 + k * q3
-    a1     = (g - k * e) * q * q / kk1
-    lamda  = sqrt(q)
+    q3 = q^3
+    kk1 = 1 + k * q3
+    a1 = (g - k * e) * q * q / kk1
+    lamda = sqrt(q)
     lamdaq = lamda * q
-    bb     = -0.25 * lamdaq * (1 + g + k * (dr - e))
-    cc     =  0.25 * kk1 * (a1 * q * (1 - dr / q3) + dr - h * h)
-    ddsq   = bb * bb - cc
-    dd     = sqrt(ddsq)
+    bb = -0.25 * lamdaq * (1 + g + k * (dr - e))
+    cc = 0.25 * kk1 * (a1 * q * (1 - dr / q3) + dr - h * h)
+    ddsq = bb * bb - cc
+    dd = sqrt(ddsq)
 
-    # m matrix (2×2) — deltar_infinity lines 521–524.
+    # m matrix (2×2) — large-t asymptotic coupling.
     m11 = -lamdaq + dr / lamdaq
-    m12 =  h - dr / lamdaq
-    m21 =  kk1 * (h + dr / lamdaq)
+    m12 = h - dr / lamdaq
+    m21 = kk1 * (h + dr / lamdaq)
     m22 = -kk1 * (a1 / lamda + dr / lamdaq)
 
     pexp = SVector{4,ComplexF64}(bb - dd, bb + dd, -bb + dd, -bb - dd)
@@ -176,32 +182,32 @@ function _build_infinity_arrays(p::GGJParameters, Q::ComplexF64,
         for i in 1:4
             d1_m[i, j] = d0inv[i, 1] * bmmat[1, j] + d0inv[i, 2] * bmmat[2, j] +
                          lamda * (-d0inv[i, 3] * bmmat[1, j] +
-                                   d0inv[i, 4] * bmmat[2, j] / kk1)
+                                  d0inv[i, 4] * bmmat[2, j] / kk1)
         end
     end
 
     # bpmat (2×2) and columns 3,4 of d1.
     sigma = bmmat[1, 2] / bmmat[2, 2]
-    tau   = bmmat[2, 1] / bmmat[1, 1]
+    tau = bmmat[2, 1] / bmmat[1, 1]
     stfac = 0.5 / (1 - sigma * tau)
     bpmat = zeros(ComplexF64, 2, 2)
     bpmat[1, 1] = stfac / bmmat[1, 1]
     bpmat[2, 2] = stfac / bmmat[2, 2]
-    bpmat[1, 2] = -tau   * bpmat[2, 2]
+    bpmat[1, 2] = -tau * bpmat[2, 2]
     bpmat[2, 1] = -sigma * bpmat[1, 1]
     @inbounds for j in 1:2
         for i in 1:4
-            d1_m[i, j + 2] = (d0inv[i, 1] * bpmat[1, j] -
-                              d0inv[i, 2] * bpmat[2, j] * kk1) / lamda +
-                              d0inv[i, 3] * bpmat[1, j] +
-                              d0inv[i, 4] * bpmat[2, j]
+            d1_m[i, j+2] = (d0inv[i, 1] * bpmat[1, j] -
+                            d0inv[i, 2] * bpmat[2, j] * kk1) / lamda +
+                           d0inv[i, 3] * bpmat[1, j] +
+                           d0inv[i, 4] * bpmat[2, j]
         end
     end
 
     d1 = SMatrix{4,4,ComplexF64}(d1_m)
     bl1 = SVector{4,ComplexF64}(-lamda, -lamda, lamda, lamda)
 
-    # tmax (deltar_infinity lines 644–650, ntmax = 1 default).
+    # tmax (ntmax = 1 default).
     p1v = sqrt(-mercier_di(p))
     tmax_inner = sqrt((max(0.5, p1v)^2 - log(rtol)) / abs(lamda))
     tmax_outer = 6.0 / abs(q)
@@ -218,18 +224,18 @@ complex frequency `Q`, precomputing the origin and infinity asymptotic arrays
 used by the forward/backward shoots.
 """
 function _build_shooting_system(p::GGJParameters, Q::ComplexF64;
-                                nps::Int=8, rtol::Float64=1e-6, fmax::Float64=1.0)
+    nps::Int=8, rtol::Float64=1e-6, fmax::Float64=1.0)
     o = _build_origin_arrays(p, Q; nps=nps, rtol=rtol)
     inf = _build_infinity_arrays(p, Q, o.d0inv; rtol=rtol, fmax=fmax)
     return GGJShootingSystem(
         p, Q, o.p1v, o.pplus,
         o.al0, o.al1, o.d0, o.d0inv, o.ups, o.nps,
-        inf.d1, inf.pexp, inf.bl1, inf.tmax, o.tmin,
+        inf.d1, inf.pexp, inf.bl1, inf.tmax, o.tmin
     )
 end
 
 # -----------------------------------------------------------------------
-# Origin Frobenius basis evaluator — port of deltar_upsfit (lines 233–267).
+# Origin Frobenius basis evaluator.
 # Returns the 4×4 matrix `u(t)` whose columns are the 4 fundamental
 # origin solutions evaluated at `t`. Solving `u * c0 = y` then projects a
 # 4-component integrated state onto the origin basis.
@@ -242,7 +248,7 @@ function _origin_basis(sys::GGJShootingSystem, t::Real)
     @inbounds for j in 1:4, i in 1:4
         # Horner in t² over k = nps..1
         acc = sys.ups[i, j, nps]
-        for kk in (nps - 1):-1:1
+        for kk in (nps-1):-1:1
             acc = acc * t2 + sys.ups[i, j, kk]
         end
         u[i, j] = acc
@@ -257,8 +263,8 @@ function _origin_basis(sys::GGJShootingSystem, t::Real)
 end
 
 # -----------------------------------------------------------------------
-# Initial condition at tmax — see deltar_run lines 121–126.
-# With nxps = 1 in deltar.f, vps[:,:,1] = I, so the asymptotic-basis
+# Initial condition at tmax.
+# With nxps = 1, vps[:,:,1] = I, so the asymptotic-basis
 # evaluator returns a diagonal v. The IC is then
 #   y(tmax) = d1 * v(:, 1:2)
 # which extracts the first two columns of d1 scaled by the algebraic-mode
@@ -279,12 +285,12 @@ function _initial_condition(sys::GGJShootingSystem)
 end
 
 # -----------------------------------------------------------------------
-# ODE right-hand side — port of deltar_der (lines 185–224).
+# ODE right-hand side.
 #   dy/dt = al1 * y * t + diag(al0) * y / t
 # -----------------------------------------------------------------------
 
 function _ggj_der!(dy::AbstractMatrix{ComplexF64}, y::AbstractMatrix{ComplexF64},
-                  sys::GGJShootingSystem, t::Real)
+    sys::GGJShootingSystem, t::Real)
     mul!(dy, sys.al1, y)
     @inbounds for j in axes(y, 2), i in axes(y, 1)
         dy[i, j] = dy[i, j] * t + sys.al0[i] * y[i, j] / t
@@ -293,7 +299,7 @@ function _ggj_der!(dy::AbstractMatrix{ComplexF64}, y::AbstractMatrix{ComplexF64}
 end
 
 # -----------------------------------------------------------------------
-# Parity-projected matching ratio — port of deltar_ratio (lines 674–716).
+# Parity-projected matching ratio.
 # c0 is a 4×2 complex matrix whose rows are the four origin-mode
 # coefficients of the two integrated solutions (modes 1: x^{p1},
 # 2: x^{1/2}, 3: x^{−p1}, 4: x^{−1/2}).
@@ -324,24 +330,27 @@ end
     solve_inner(::GGJModel{:shooting}, params::GGJParameters, γ::Number;
                 reltol::Float64=1e-6, abstol::Float64=1e-6,
                 rtol_origin::Float64=1e-6, nps::Int=8,
-                fmax::Float64=1.0, solver=Tsit5()) -> SVector{2,ComplexF64}
+                fmax::Float64=1.0, solver=Tsit5()) -> InnerLayerResponse
 
 Solve the GGJ inner-layer matching problem by stable backward shooting in
-the origin-diagonalized 4×4 basis. Direct port of the rmatch `deltar.f`
-algorithm.
+the origin-diagonalized 4×4 basis.
 
-Returns the parity-projected matching data `(Δ₁, Δ₂)` (already rescaled
-back to physical units via `rescale_delta`). Index ordering matches the
-Fortran `deltar` output.
+Returns an `InnerLayerResponse(tearing, interchange)` with rescaling
+applied. `_delta_from_c0` returns a `(Δ₁, Δ₂)` pair where `Δ₁` is the
+**interchange** (anti-symmetric / W-odd) channel and `Δ₂` is the
+**tearing** (symmetric / W-even) channel. We therefore map
+`Δ₂ → tearing` and `Δ₁ → interchange` into the named fields, matching the
+physics channel labels used by the Galerkin solver and by the
+`InnerLayerResponse` docstring.
 
 Tolerances `reltol`/`abstol` are the integrator tolerances; `rtol_origin`
 controls the truncation error of the origin Frobenius series and the
 choice of `tmin`.
 """
 function solve_inner(::GGJModel{:shooting}, params::GGJParameters, γ::Number;
-                     reltol::Float64=1e-6, abstol::Float64=1e-6,
-                     rtol_origin::Float64=1e-6, nps::Int=8,
-                     fmax::Float64=1.0, solver=Tsit5())
+    reltol::Float64=1e-6, abstol::Float64=1e-6,
+    rtol_origin::Float64=1e-6, nps::Int=8,
+    fmax::Float64=1.0, solver=Tsit5())
     Q = inner_Q(params, γ)
     sys = _build_shooting_system(params, Q; nps=nps, rtol=rtol_origin, fmax=fmax)
 
@@ -357,7 +366,9 @@ function solve_inner(::GGJModel{:shooting}, params::GGJParameters, γ::Number;
     c0 = Matrix(u) \ Matrix(y_end)
 
     Δ_raw = _delta_from_c0(c0, sys)
-    return rescale_delta(Δ_raw, params)
+    Δ_rescaled = rescale_delta(Δ_raw, params)
+    # Δ_rescaled ≡ (Δ₁, Δ₂) = (interchange, tearing).
+    return InnerLayerResponse(Δ_rescaled[2], Δ_rescaled[1])
 end
 
 solve_inner(::GGJModel{:shooting}, params::GGJParameters, γ::Real; kwargs...) =
diff --git a/src/InnerLayer/InnerLayer.jl b/src/Tearing/InnerLayer/InnerLayer.jl
similarity index 54%
rename from src/InnerLayer/InnerLayer.jl
rename to src/Tearing/InnerLayer/InnerLayer.jl
index 537b2970f..1d9430ab9 100644
--- a/src/InnerLayer/InnerLayer.jl
+++ b/src/Tearing/InnerLayer/InnerLayer.jl
@@ -10,22 +10,28 @@ module InnerLayer
 using LinearAlgebra
 using StaticArrays
 
+using ..Utilities
+
 include("InnerLayerInterface.jl")
 include("GGJ/GGJ.jl")
-# include("SLAYER/Slayer.jl") --- SLAYER code goes here
+include("SLAYER/SLAYER.jl")
 
 import .GGJ: GGJModel, GGJParameters, build_asymptotics, evaluate_asymptotics, pick_xmax
 import .GGJ: InnerAsymptoticsCache, mercier_di, mercier_dr, inner_Q, rescale_delta
-import .GGJ: glasser_wang_2020_eq55
-# SLAYER imports go here
+import .GGJ: glasser_wang_2020_eq55, build_ggj_inputs
+
+import .SLAYER: SLAYERModel, SLAYERParameters, slayer_parameters, r_based_shear
+import .SLAYER: riccati_del_s, slayer_layer_thickness, LayerWidths
+import .SLAYER: surface_minor_radius, surface_da_dpsi, build_slayer_inputs
 
-export InnerLayerModel, solve_inner
+export InnerLayerModel, InnerLayerParameters, InnerLayerResponse, solve_inner
 export GGJ, GGJModel, GGJParameters
 export build_asymptotics, evaluate_asymptotics, pick_xmax, InnerAsymptoticsCache
 export mercier_di, mercier_dr, inner_Q, rescale_delta
-export glasser_wang_2020_eq55
-
-# SLAYER exports go here
+export glasser_wang_2020_eq55, build_ggj_inputs
 
+export SLAYER, SLAYERModel, SLAYERParameters, slayer_parameters, r_based_shear
+export riccati_del_s, slayer_layer_thickness, LayerWidths
+export surface_minor_radius, surface_da_dpsi, build_slayer_inputs
 
 end # module InnerLayer
diff --git a/src/Tearing/InnerLayer/InnerLayerInterface.jl b/src/Tearing/InnerLayer/InnerLayerInterface.jl
new file mode 100644
index 000000000..e67cd1414
--- /dev/null
+++ b/src/Tearing/InnerLayer/InnerLayerInterface.jl
@@ -0,0 +1,77 @@
+# InnerLayerInterface.jl
+#
+# Abstract interface for resistive inner-layer models. Concrete models
+# (e.g. GGJ, SLAYER, kinetic) live in submodules and specialize `solve_inner`.
+
+"""
+    InnerLayerModel
+
+Abstract supertype for resistive inner-layer models. Each concrete model is a
+small, parameter-free type tag (often parameterized by a solver-choice symbol)
+that selects a `solve_inner` method.
+
+Implementations live in submodules of `InnerLayer`, e.g. `InnerLayer.GGJ`.
+"""
+abstract type InnerLayerModel end
+
+"""
+    InnerLayerParameters
+
+Abstract supertype for the per-surface physical-parameter structs consumed by
+the inner-layer models (`SLAYERParameters`, `GGJParameters`). Lets the
+dispersion runner and HDF5 output dispatch generically over a vector of
+single-model parameters.
+"""
+abstract type InnerLayerParameters end
+
+"""
+    InnerLayerResponse
+
+Parity-projected inner-layer matching data at one rational surface. The two
+components correspond to the homogeneous parity solutions of the half-domain
+inner-layer problem (parity boundary conditions imposed at X = 0). They are
+the `Δ_{j,±}(γ)` of Glasser, Wang & Park, Phys. Plasmas **23**, 112506
+(2016), Eqs. (34)–(35).
+
+# Fields
+
+  - `tearing` — the **odd-parity** matching coefficient (GWP Δ_+, the
+    "odd mode"). Corresponds to a flux perturbation W that is EVEN in x and
+    a velocity/temperature perturbation that is ODD — i.e., the
+    reconnecting mode with a current sheet at the rational surface. This is
+    the tearing drive that appears as Δ' in the classical constant-ψ
+    tearing equation. Must be populated by every resistive inner-layer model.
+
+  - `interchange` — the **even-parity** matching coefficient (GWP Δ_−, the
+    "even mode"). Corresponds to W odd, N and Θ even — i.e., the
+    non-reconnecting interchange/ballooning channel. Its dissipative piece
+    in toroidal geometry is the Glasser, Greene & Johnson stabilization
+    term that opposes tearing growth (Glasser, Greene & Johnson 1975;
+    Lütjens-Bondeson-Roy 1993). Pressureless inner-layer models (e.g.
+    SLAYER's Fitzpatrick Riccati) set this identically zero.
+
+The naming follows the physics channel rather than a mathematical parity
+label because `odd/even` carries different meanings across the literature
+depending on whether you label by the parity of W (GWP paper convention)
+or the parity of (N, Θ). Using `tearing` and `interchange` avoids ambiguity.
+"""
+struct InnerLayerResponse
+    tearing::ComplexF64
+    interchange::ComplexF64
+end
+
+InnerLayerResponse(; tearing::Number=0, interchange::Number=0) =
+    InnerLayerResponse(ComplexF64(tearing), ComplexF64(interchange))
+
+"""
+    solve_inner(model::InnerLayerModel, params, γ::Number; kwargs...) -> InnerLayerResponse
+
+Compute the parity-projected matching data `(Δ_tearing, Δ_interchange)` for
+the given inner-layer `model`, physical parameters `params`, and complex
+growth rate `γ`. Concrete models specialize this function.
+
+See `InnerLayerResponse` for the physics-oriented field definitions.
+Pressureless models (SLAYER) populate only `tearing` and leave
+`interchange` at zero; two-fluid / finite-β models (GGJ) populate both.
+"""
+function solve_inner end
diff --git a/src/Tearing/InnerLayer/SLAYER/LayerInputs.jl b/src/Tearing/InnerLayer/SLAYER/LayerInputs.jl
new file mode 100644
index 000000000..96177903d
--- /dev/null
+++ b/src/Tearing/InnerLayer/SLAYER/LayerInputs.jl
@@ -0,0 +1,311 @@
+# LayerInputs.jl
+#
+# Build per-surface `SLAYERParameters` from an in-memory `PlasmaEquilibrium`,
+# the `SingType` rational-surface data produced by `ForceFreeStates`, and a
+# `KineticProfiles` object. Everything needed is already held in memory, so
+# the layer inputs are assembled directly without an intermediate file
+# round-trip.
+#
+# Geometry extraction:
+#   - Minor radius at the outboard midplane (θ = 0) via
+#     `equil.rzphi_rsquared((ψ, 0.0))`.
+#   - `da/dψ` via central finite difference on the same bicubic.
+#   - r-based magnetic shear via `r_based_shear(rs, q, q1, da/dψ)` (defined
+#     in LayerParameters.jl).
+
+using ..Utilities: KineticProfiles
+using ...Utilities.NeoclassicalResistivity: NeoResistivityModel, SpitzerModel,
+    coulomb_log_e, nu_star_e
+using FastInterpolations: DerivOp
+
+"""
+    surface_minor_radius(equil, psi; theta=0.0) -> Float64
+
+Minor radius at normalized flux `psi` and poloidal angle `theta`,
+computed from `equil.rzphi_rsquared` as `√((R − R₀)² + (Z − Z₀)²)`.
+`theta = 0.0` (outboard midplane) is the default; pass `θ = π` to measure
+the inboard side if you want an average.
+"""
+function surface_minor_radius(equil, psi::Real; theta::Real=0.0)
+    r_sq = equil.rzphi_rsquared((Float64(psi), Float64(theta)))
+    return sqrt(r_sq)
+end
+
+"""
+    surface_da_dpsi(equil, psi; theta=0.0, h=1e-5) -> Float64
+
+Central finite-difference approximation of `d(minor radius)/dψ` at `psi`.
+Falls back to one-sided differences near the flux-coordinate boundaries
+(0 or 1).
+"""
+function surface_da_dpsi(equil, psi::Real; theta::Real=0.0, h::Real=1e-5)
+    psi_f = Float64(psi)
+    # Clamp to safe sampling range within (0, 1)
+    eps_edge = 10 * h
+    lo = psi_f - h
+    hi = psi_f + h
+    if lo < eps_edge
+        # one-sided forward
+        a0 = surface_minor_radius(equil, max(psi_f, eps_edge); theta=theta)
+        a1 = surface_minor_radius(equil, max(psi_f, eps_edge) + h; theta=theta)
+        return (a1 - a0) / h
+    elseif hi > 1.0 - eps_edge
+        # one-sided backward
+        a0 = surface_minor_radius(equil, min(psi_f, 1.0 - eps_edge) - h; theta=theta)
+        a1 = surface_minor_radius(equil, min(psi_f, 1.0 - eps_edge); theta=theta)
+        return (a1 - a0) / h
+    else
+        a_plus = surface_minor_radius(equil, psi_f + h; theta=theta)
+        a_minus = surface_minor_radius(equil, psi_f - h; theta=theta)
+        return (a_plus - a_minus) / (2h)
+    end
+end
+
+"""
+    build_slayer_inputs(equil, sings, profiles; …) -> Vector{SLAYERParameters}
+
+Build a `SLAYERParameters` for each rational surface in `sings`, pulling
+geometry (minor radius, r-based shear, q, dq/dψ, R₀) from the in-memory
+`equil::PlasmaEquilibrium` and kinetic data (n_e, T_e, T_i, ω, ω\\_\\*e,
+ω\\_\\*i) from `profiles::KineticProfiles`.
+
+Layer inputs are assembled directly from the in-memory equilibrium and
+profiles, without an intermediate file round-trip.
+
+# Arguments
+
+  - `equil`    -- `PlasmaEquilibrium`
+  - `sings`    -- `Vector{SingType}` (one per resonant surface)
+  - `profiles` -- `KineticProfiles` valid across all `sings` ψ values
+
+# Keyword arguments
+
+  - `bt`        -- toroidal field [T]. Scalar, callable of `psi`, or
+    `nothing` (default). When `nothing`, the physical `B_T = F(ψ) / (2π·R₀)`
+    is computed per surface from the equilibrium's F-spline. Note:
+    `equil.config.b0exp` is a *normalization* (often just `1.0`), not the
+    physical field, so passing it as a scalar is almost always wrong.
+
+  - `mu_i`      -- ion mass in proton-mass units (default `2.0` for D).
+  - `zeff`      -- effective charge (default `1.0`).
+  - `chi_perp`  -- perpendicular heat diffusivity [m²/s]. Scalar or a
+    callable of `psi` (default `1.0`).
+  - `chi_tor`   -- toroidal heat diffusivity [m²/s]. Scalar or a callable
+    of `psi` (default `1.0`).
+  - `dr_val`    -- resistive interchange index `D_R = E + F + H²`
+    (Glasser-Greene-Johnson 1975) feeding the critical-Δ formulas
+    (`:lar`, `:rfitzp`, `:toroidal`). When `nothing` (default), Julia
+    derives it per-surface from the equilibrium as
+    `dr_val_k = D_R(ψ_k) = E_k + F_k + H_k²`,
+    consistent with Connor-Hastie-Helander 2015 (PPCF 57 065001) Eq. 59
+    which uses `(−D_R)` in the χ_‖-matching critical-Δ. Pass a scalar /
+    vector / callable to override.
+
+    **NOTE**: the χ_‖-matching critical-Δ requires the resistive
+    interchange index `D_R = E + F + H²` (Glasser-Greene-Johnson 1975),
+    NOT the Mercier index `D_I = E + F + H − 1/4`. The two differ by
+    `(H − 1/2)²`, which is non-trivial on shaped equilibria (~factor 3 on
+    DIII-D); this code uses the physically correct `D_R`.
+  - `dgeo_val`  -- Connor 2015 (PPCF 57 065001) Eq. 59 geometric factor
+    used by `dc_type=:toroidal`. When `nothing` (default), an error is
+    raised if `dc_type=:toroidal` is also requested — the auto-derived
+    formula additionally needs ⟨|∇ψ|²⟩ FSA which `ResistGeometry`
+    doesn't currently expose. Pass a scalar / vector / callable to use
+    a prescribed value. (For `dc_type=:rfitzp` and `:lar`, dgeo_val is
+    not consulted.)
+  - `dc_type`   -- `:none` (default), `:lar`, `:rfitzp`, or `:toroidal`.
+  - `theta`     -- poloidal angle at which to measure minor radius (default
+    `0.0`, outboard midplane).
+  - `resistivity_model` -- `SauterNeoModel()` (default), `RedlNeoModel()`,
+    `SpitzerModel()`, or `SpitzerHarmModel()` (legacy Fitzpatrick σ_∥).
+    Sets the η entering τ_R = μ₀r_s²/η. With a neoclassical model, `f_trap`
+    and ν*_e are taken from the surface's `ResistGeometry` if populated
+    (via `ForceFreeStates.resist_eval_all!`), otherwise fall back to the
+    ε-only Lin-Liu-Miller form and `rs/R_0` aspect ratio.
+  - `lnLambda_form` -- Coulomb-log form passed through to `slayer_parameters`
+    (default `:nrl`; `:wesson` + `SpitzerHarmModel()` reproduces legacy
+    SLAYER exactly).
+"""
+function build_slayer_inputs(equil, sings, profiles::KineticProfiles;
+    bt=nothing,
+    R0=nothing,
+    rs_method::Symbol=:midplane,
+    mu_i::Real=2.0,
+    zeff::Real=1.0,
+    z_i::Real=1.0,
+    chi_perp=1.0,
+    chi_tor=1.0,
+    dr_val=nothing,
+    dgeo_val=nothing,
+    dc_type::Symbol=:none,
+    theta::Real=0.0,
+    compute_omega_star::Bool=true,
+    resistivity_model::NeoResistivityModel=SauterNeoModel(),
+    lnLambda_form::Symbol=:nrl)
+    R0_use = R0 === nothing ? equil.ro : Float64(R0)
+    _eval(x, ψ) = x isa Real ? Float64(x) : Float64(x(ψ))
+
+    # Compute physical B_T = F(ψ) / (2π·R₀) per surface from the F spline
+    # when `bt` is not explicitly supplied.
+    _bt_at(ψ) =
+        if bt === nothing
+            Float64(equil.profiles.F_spline(ψ)) / (2π * R0_use)
+        elseif bt isa Real
+            Float64(bt)
+        else
+            Float64(bt(ψ))
+        end
+
+    # Minor-radius extractor: `:midplane` = outboard-midplane chord
+    # (original behavior); `:fsa` = θ-mean of √rzphi_rsquared, the
+    # flux-surface-averaged minor radius.
+    _rs_at(ψ) =
+        if rs_method === :fsa
+            integrand(θ) = sqrt(equil.rzphi_rsquared((Float64(ψ), Float64(θ))))
+            N = 128
+            s = 0.0
+            @inbounds for k in 1:N
+                s += integrand((k - 0.5) / N)
+            end
+            s / N
+        else
+            surface_minor_radius(equil, ψ; theta=theta)
+        end
+    _da_dpsi_at(ψ) =
+        if rs_method === :fsa
+            # central finite difference on _rs_at
+            h = 1e-5
+            lo = ψ - h
+            hi = ψ + h
+            eps_edge = 10h
+            if lo < eps_edge
+                (_rs_at(max(ψ, eps_edge) + h) - _rs_at(max(ψ, eps_edge))) / h
+            elseif hi > 1.0 - eps_edge
+                (_rs_at(min(ψ, 1.0 - eps_edge)) - _rs_at(min(ψ, 1.0 - eps_edge) - h)) / h
+            else
+                (_rs_at(ψ + h) - _rs_at(ψ - h)) / (2h)
+            end
+        else
+            surface_da_dpsi(equil, ψ; theta=theta)
+        end
+
+    # Per-surface ω_*e, ω_*i (diamagnetic frequencies) from spline
+    # derivatives. When `compute_omega_star=true` we override any ω_*e/ω_*i
+    # carried in `profiles`. Main-ion density is
+    # taken equal to the electron density (quasi-neutrality, matching the
+    # staging step).
+    chi1 = 2π * equil.psio
+    _omega_star_at(ψ) = begin
+        n_e = Float64(profiles.n_e(ψ))
+        dn_e = Float64(profiles.n_e(ψ; deriv=DerivOp(1)))
+        T_e = Float64(profiles.T_e(ψ))
+        dT_e = Float64(profiles.T_e(ψ; deriv=DerivOp(1)))
+        T_i = Float64(profiles.T_i(ψ))
+        dT_i = Float64(profiles.T_i(ψ; deriv=DerivOp(1)))
+        ω_star_e = (2π / chi1) * (T_e * dn_e / n_e + dT_e)
+        ω_star_i = -(2π / (Float64(z_i) * chi1)) * (T_i * dn_e / n_e + dT_i)
+        return (ω_star_e, ω_star_i)
+    end
+
+    out = Vector{SLAYERParameters}(undef, length(sings))
+    for (k, sing) in enumerate(sings)
+        psi = sing.psifac
+        q = sing.q
+        q1 = sing.q1
+
+        rs = _rs_at(psi)
+        da_dpsi = _da_dpsi_at(psi)
+        sval_r = r_based_shear(rs, q, q1, da_dpsi)
+
+        prof = profiles(psi)
+        # Override ω_*e, ω_*i with spline-derivative values when requested.
+        ω_e_use, ω_i_use = if compute_omega_star
+            _omega_star_at(psi)
+        else
+            (prof.omega_e, prof.omega_i)
+        end
+
+        # Resonant (m, n): take the first element of the mode-number vectors.
+        # Parallel-FM `sing.m`/`sing.n` hold exactly one entry each; ideal
+        # DCON may hold multiple — we pick the first and document the choice.
+        m_res = sing.m[1]
+        n_res = sing.n[1]
+
+        # Pull geometric trapped-fraction inputs from ResistGeometry when
+        # available (populated by ForceFreeStates.resist_eval_all!); else
+        # fall back to nothing and let slayer_parameters compute them from
+        # aspect ratio + Lin-Liu-Miller ε-only form.
+        rg = sing.restype
+        f_trap_kw = rg === nothing ? nothing : rg.f_trap
+        R_major_eff = rg === nothing ? nothing : rg.R_major
+        nu_e_star_kw = if rg === nothing ||
+                          resistivity_model isa Union{SpitzerModel,SpitzerHarmModel}
+            nothing
+        else
+            lnL = coulomb_log_e(prof.n_e, prof.T_e; form=lnLambda_form)
+            nu_star_e(prof.n_e, prof.T_e, rg.R_major, rg.eps_local,
+                q, zeff; lnLamb=lnL)
+        end
+
+        # dr_val: per-surface resistive interchange index D_R = E + F + H²
+        # (Glasser-Greene-Johnson 1975). Used by `_solve_dc_tmp` to compute
+        # the χ_‖-matching critical-Δ via Connor-Hastie-Helander 2015 Eq. 59,
+        # which has `(−D_R)` as a multiplier. NOT the Mercier index
+        # D_I = E + F + H − 1/4 (see this function's docstring); we use the
+        # physically correct D_R here.
+        dr_val_k = if dr_val === nothing
+            rg === nothing &&
+                throw(
+                    ArgumentError(
+                        "build_slayer_inputs: dr_val=nothing " *
+                        "requires `sing.restype` populated by " *
+                        "ForceFreeStates.resist_eval_all!. " *
+                        "Surface k=$k has restype=nothing."
+                    )
+                )
+            rg.E + rg.F + rg.H^2
+        else
+            _eval(dr_val, psi)
+        end
+
+        # dgeo_val: only used by dc_type=:toroidal (the Connor-Hastie-
+        # Helander 2015 formula). Auto-derivation requires ⟨|∇ψ|²⟩ FSA
+        # which the current `ResistGeometry` doesn't expose; for now we
+        # require an explicit value if the toroidal dc_type is selected.
+        dgeo_val_k = if dgeo_val === nothing
+            dc_type === :toroidal &&
+                throw(
+                    ArgumentError(
+                        "build_slayer_inputs: dc_type=:toroidal " *
+                        "needs `dgeo_val` (Connor 2015 PPCF 57 " *
+                        "065001 Eq. 59 geometric factor). " *
+                        "Auto-derivation from equilibrium not " *
+                        "yet implemented; pass a scalar / vector " *
+                        "/ callable explicitly."
+                    )
+                )
+            0.0
+        else
+            _eval(dgeo_val, psi)
+        end
+
+        out[k] = slayer_parameters(;
+            n_e=prof.n_e, t_e=prof.T_e, t_i=prof.T_i,
+            omega=prof.omega, omega_e=ω_e_use, omega_i=ω_i_use,
+            qval=q, sval_r=sval_r, bt=_bt_at(psi),
+            rs=rs, R0=R0_use, mu_i=mu_i, zeff=zeff,
+            chi_perp=_eval(chi_perp, psi),
+            chi_tor=_eval(chi_tor, psi),
+            m=m_res, n=n_res,
+            dr_val=dr_val_k,
+            dgeo_val=dgeo_val_k,
+            dc_type=dc_type, ising=k,
+            resistivity_model=resistivity_model,
+            f_trap=f_trap_kw,
+            nu_e_star=nu_e_star_kw,
+            R_major_eff=R_major_eff,
+            lnLambda_form=lnLambda_form
+        )
+    end
+    return out
+end
diff --git a/src/Tearing/InnerLayer/SLAYER/LayerParameters.jl b/src/Tearing/InnerLayer/SLAYER/LayerParameters.jl
new file mode 100644
index 000000000..57ee30763
--- /dev/null
+++ b/src/Tearing/InnerLayer/SLAYER/LayerParameters.jl
@@ -0,0 +1,363 @@
+# LayerParameters.jl
+#
+# `SLAYERParameters` carries the dimensionless layer-physics parameters
+# that the Fitzpatrick layer Riccati ODE consumes for one rational surface,
+# plus the dimensional conversion factors needed to translate normalized
+# frequencies and Δ values back to physical units.
+#
+# The constructor builds the per-surface state from dimensional equilibrium
+# and kinetic-profile inputs. The Fitzpatrick two-fluid layer uses
+# P_perp/P_tor/D_norm rather than the older magnetic/electron Prandtl
+# (pr/pe) and ρ_s-based (ds) parametrization. Q is not stored — it is
+# passed directly to `solve_inner`.
+
+"""
+    SLAYERParameters
+
+Dimensionless layer-physics parameters at one rational surface for the
+Fitzpatrick two-fluid drift-MHD SLAYER inner-layer model (Fitzpatrick
+2023; Park et al. 2022), plus dimensional auxiliaries required for
+de-normalization. The parametrization uses `P_perp`, `P_tor`, and
+`D_norm` (not the older `pr`/`pe`/`ds` set).
+
+| field      | meaning                                                           |
+|:---------- |:----------------------------------------------------------------- |
+| `ising`    | Singular-surface index (traceability only)                        |
+| `m`, `n`   | Poloidal / toroidal mode numbers at this surface                  |
+| `tau`      | T_i / T_e                                                         |
+| `lu`       | Lundquist number S = τ_R / τ_H                                    |
+| `c_beta`   | Compressibility √(β_local / (1 + β_local))                        |
+| `D_norm`   | (d_β/r_s) · S^(1/3) · √(τ/(1+τ))  (Fitzpatrick normalized scale)  |
+| `P_perp`   | Perpendicular Prandtl number τ_R / τ_⊥                            |
+| `P_tor`    | Toroidal-direction Prandtl number τ_R / τ_‖tor                    |
+| `Q_e`      | Normalized electron diamagnetic: −tauk · ω_*e                     |
+| `Q_i`      | Normalized ion diamagnetic:      −tauk · ω_*i                     |
+| `iota_e`   | Q_e / (Q_e − Q_i)                                                 |
+| `tauk`     | Q-conversion factor S^(1/3) · τ_H  [s] — multiplies ω to get Q    |
+| `tau_r`    | Resistive diffusion time [s]                                      |
+| `delta_n`  | Δ-normalization factor S^(1/3) / r_s [m⁻¹]                        |
+| `rs`       | Minor radius at this surface [m]                                  |
+| `R0`       | Major radius [m]                                                  |
+| `bt`       | Toroidal field [T]                                                |
+| `sval_r`   | r-based magnetic shear r_s · (dq/dr) / q (Fitzpatrick convention) |
+| `dr_val`   | Radial width parameter at surface (input to dc_tmp)               |
+| `dgeo_val` | Geometric Δ (Shafranov shift factor)                              |
+| `eta`      | Parallel resistivity entering τ_R = μ₀r_s²/η [Ω·m]                |
+| `d_beta`   | Beta-weighted ion length scale c_β · d_i [m]                      |
+| `dc_tmp`   | Critical-Δ offset from chi_parallel matching                      |
+| `dc_type`  | Selector for `dc_tmp` formula                                     |
+
+The complex normalized growth rate `Q = ω + iγ` is **not** stored here;
+it is passed as a separate argument to `solve_inner`.
+"""
+Base.@kwdef struct SLAYERParameters <: InnerLayerParameters
+    # Surface identity
+    ising::Int = 0
+    m::Int = 0
+    n::Int = 0
+
+    # Normalized layer parameters consumed by riccati_f
+    tau::Float64
+    lu::Float64
+    c_beta::Float64
+    D_norm::Float64
+    P_perp::Float64
+    P_tor::Float64
+    Q_e::Float64
+    Q_i::Float64
+    iota_e::Float64
+
+    # Conversion factors (Q ↔ ω in rad/s)
+    tauk::Float64
+    tau_r::Float64
+    delta_n::Float64
+
+    # Geometric / fluid auxiliaries
+    rs::Float64
+    R0::Float64
+    bt::Float64
+    sval_r::Float64
+    dr_val::Float64 = 0.0
+    dgeo_val::Float64 = 0.0
+    eta::Float64
+    d_beta::Float64
+
+    # Critical-Δ offset
+    dc_tmp::Float64 = 0.0
+    dc_type::Symbol = :none
+end
+
+# Allowed dc_type values for the critical-Δ offset. `:none` is the default
+# `dc_tmp = 0` branch.
+const ALLOWED_DC_TYPES = (:none, :lar, :rfitzp, :toroidal)
+
+"""
+    r_based_shear(rs, q, dq_dpsi, da_dpsi) -> Float64
+
+Convert a ψ-based shear to the r-based (Fitzpatrick) convention used
+throughout SLAYER:
+
+```
+s_r = r_s · (dq/dr) / q  =  r_s · (dq/dψ) / (q · da/dψ)
+```
+
+`rs` is the minor radius at the surface, `q` the safety factor,
+`dq_dpsi` the radial derivative of q with respect to ψ, and `da_dpsi`
+the derivative of the surface minor radius with respect to ψ. The two
+ψ derivatives must use the **same** ψ convention (i.e., both with
+respect to ψ_norm or both with respect to physical ψ — the conversion
+factor cancels in the ratio).
+
+Equivalent to the conversion `s_Fitz = s_psiN · r_s / (psi_N · da_dpsiN)`.
+"""
+function r_based_shear(rs::Real, q::Real, dq_dpsi::Real, da_dpsi::Real)
+    da_dpsi != 0 || throw(ArgumentError("r_based_shear: da/dψ must be non-zero"))
+    q != 0 || throw(ArgumentError("r_based_shear: q must be non-zero"))
+    return rs * dq_dpsi / (q * da_dpsi)
+end
+
+# Internal: solve the Wd self-consistency loop for the chi_parallel-based
+# critical Δ (Connor-Hastie-Helander 2015). Returns dc_tmp as a Float64.
+function _solve_dc_tmp(; dc_type::Symbol, dr_val::Real, dgeo_val::Real,
+    chi_perp::Real, t_e::Real, zeff::Real, tau_ee::Real,
+    rs::Real, R0::Real, sval_r::Real, n_tor::Integer,
+    max_iter::Integer=100, tol::Real=1e-10)
+    dc_type in ALLOWED_DC_TYPES ||
+        throw(ArgumentError("SLAYERParameters: unknown dc_type=$dc_type. " *
+                            "Allowed: $(ALLOWED_DC_TYPES)"))
+    (dc_type === :none || dr_val == 0.0) && return 0.0
+
+    vte = sqrt(2.0 * t_e * E_CHG / M_E)
+    chi_par_smfp = (1.581 * tau_ee * vte^2) / (1.0 + 0.2535 * zeff)
+
+    Wd = 0.1
+    converged = false
+    for _ in 1:max_iter
+        chi_par_lmfp = (2.0 * R0 * vte) / (sqrt(π) * n_tor * sval_r * Wd)
+        chi_par = (chi_par_smfp * chi_par_lmfp) /
+                  (chi_par_smfp + chi_par_lmfp)
+        Wd_new = sqrt(8.0) * (chi_perp / chi_par)^0.25 *
+                 (1.0 / sqrt((rs / R0) * sval_r * n_tor))
+        if abs(Wd_new - Wd) / max(abs(Wd), 1e-30) < tol
+            Wd = Wd_new
+            converged = true
+            break
+        end
+        Wd = Wd_new
+    end
+    converged || error("SLAYERParameters: Wd iteration failed to converge")
+
+    chi_par_lmfp = (2.0 * R0 * vte) / (sqrt(π) * n_tor * sval_r * Wd)
+    chi_par = (chi_par_smfp * chi_par_lmfp) / (chi_par_smfp + chi_par_lmfp)
+
+    if dc_type === :lar
+        return 0.5 * (-dr_val) * π^1.5 *
+               (chi_par / chi_perp)^0.25 *
+               sqrt((n_tor * sval_r) / (R0 * rs))
+    elseif dc_type === :rfitzp
+        return -(sqrt(2.0) * π^1.5 * dr_val) / Wd
+    elseif dc_type === :toroidal
+        return 0.5 * (-dr_val) * π^1.5 *
+               (chi_par / chi_perp)^0.25 * dgeo_val
+    end
+    return 0.0
+end
+
+"""
+    slayer_parameters(; n_e, t_e, t_i, omega, omega_e, omega_i,
+                        qval, sval_r, bt, rs, R0, mu_i, zeff,
+                        chi_perp, chi_tor,
+                        m, n,
+                        dr_val=0.0, dgeo_val=0.0,
+                        dc_type=:none, ising=0,
+                        resistivity_model=SauterNeoModel(),
+                        f_trap=nothing, nu_e_star=nothing,
+                        R_major_eff=nothing,
+                        lnLambda_form=:nrl)
+        -> SLAYERParameters
+
+Build a `SLAYERParameters` for one rational surface from dimensional
+equilibrium and kinetic-profile inputs, in the Fitzpatrick two-fluid layer
+parametrization (P_perp/P_tor/D_norm; the older magnetic/electron Prandtl
+`pr`/`pe` and ρ_s-based `ds` parameters are not used).
+
+# Arguments
+
+  - `n_e` -- electron density [m⁻³]
+  - `t_e` -- electron temperature [eV]
+  - `t_i` -- ion temperature [eV]
+  - `omega`   -- toroidal rotation frequency at the surface [rad/s]
+  - `omega_e` -- electron diamagnetic frequency [rad/s]
+  - `omega_i` -- ion diamagnetic frequency [rad/s]
+  - `qval`    -- safety factor q at the surface
+  - `sval_r`  -- **r-based** magnetic shear r·(dq/dr)/q (Fitzpatrick).
+    Use `r_based_shear` to convert from ψ-based shear.
+  - `bt`      -- toroidal field [T]
+  - `rs`      -- minor radius at the surface [m]
+  - `R0`      -- major radius [m]
+  - `mu_i`    -- ion mass in proton-mass units (e.g. 2.0 for D)
+  - `zeff`    -- effective charge
+  - `chi_perp`, `chi_tor` -- perpendicular / toroidal heat diffusivity [m²/s]
+  - `m`, `n`  -- poloidal / toroidal mode numbers at the surface
+  - `dr_val`, `dgeo_val` -- inputs for the critical-Δ formula
+  - `dc_type` -- one of `:none`, `:lar`, `:rfitzp`, `:toroidal`
+  - `ising`   -- singular-surface index for traceability
+
+# Resistivity kwargs
+
+The selected resistivity sets the resistive diffusion time
+`τ_R = μ₀ r_s² / η` and hence the Lundquist number and every normalized
+layer parameter derived from it.
+
+  - `resistivity_model` -- `SauterNeoModel()` (default: Sauter 1999 F_33
+    trapped-particle correction, the physically-appropriate choice for
+    H-mode tearing stability), `RedlNeoModel()` (improved high-ν* fit),
+    `SpitzerModel()` (Sauter 18a fit, no trapped-particle correction), or
+    `SpitzerHarmModel()` (Fitzpatrick Spitzer-Härm σ_∥ — the legacy SLAYER
+    closure; pair with `lnLambda_form=:wesson` to reproduce legacy τ_R
+    exactly).
+  - `f_trap`  -- trapped-particle fraction at this surface. If not provided
+    with a neoclassical model, falls back to Lin-Liu-Miller ε-only form
+    with `ε = rs / (R_major_eff or R0)`.
+  - `nu_e_star` -- electron collisionality. If `nothing` with a neoclassical
+    model, computed from Sauter 1999 Eq. 18b using the same ε.
+  - `R_major_eff` -- ⟨R⟩ at the surface for the ν*_e formula (default `R0`).
+  - `lnLambda_form` -- `:nrl` (default), `:sauter`, or `:wesson` (legacy
+    form).
+
+# Sign convention for diamagnetic frequencies
+
+The diamagnetic normalization uses
+
+```
+Q_e = -tauk · ω_*e
+Q_i = -tauk · ω_*i
+```
+
+For the standard plasma-physics input where ω_*e is tabulated negative and
+ω_*i positive (electrons and ions drifting in opposite directions), this
+produces `Q_e > 0, Q_i < 0`, matching the opposite-drift expectation of the
+dispersion relation.
+"""
+function slayer_parameters(;
+    n_e::Real, t_e::Real, t_i::Real,
+    omega::Real, omega_e::Real, omega_i::Real,
+    qval::Real, sval_r::Real, bt::Real,
+    rs::Real, R0::Real, mu_i::Real, zeff::Real,
+    chi_perp::Real, chi_tor::Real,
+    m::Integer, n::Integer,
+    dr_val::Real=0.0, dgeo_val::Real=0.0,
+    dc_type::Symbol=:none, ising::Integer=0,
+    resistivity_model::NeoResistivityModel=SauterNeoModel(),
+    f_trap::Union{Real,Nothing}=nothing,
+    nu_e_star::Union{Real,Nothing}=nothing,
+    R_major_eff::Union{Real,Nothing}=nothing,
+    lnLambda_form::Symbol=:nrl)
+
+    # Coulomb logarithm shared by the resistivity closure and τ_ee.
+    lnLamb = coulomb_log_e(n_e, t_e; form=lnLambda_form)
+
+    # Parallel resistivity entering τ_R. The model selects the closure:
+    #   SpitzerHarmModel — Fitzpatrick Spitzer-Härm σ_∥; with
+    #     lnLambda_form=:wesson this is bit-identical to the legacy SLAYER η.
+    #   SpitzerModel     — Sauter 18a fit (legacy 1.65e-9 form under :wesson).
+    #   SauterNeoModel / RedlNeoModel — F_33 trapped-particle correction
+    #     using f_trap and ν*_e (from ResistGeometry or ε-only fallback).
+    if resistivity_model isa SpitzerModel && lnLambda_form === :wesson
+        # Preserve bit-identical legacy η diagnostic behaviour.
+        eta = 1.65e-9 * lnLamb / (t_e / 1e3)^1.5
+    elseif resistivity_model isa Union{SpitzerModel,SpitzerHarmModel}
+        eta = eta_neoclassical(resistivity_model, n_e, t_e, zeff,
+            0.0, 0.0; lnLamb=lnLamb)
+    else
+        R_eff = R_major_eff === nothing ? R0 : Float64(R_major_eff)
+        eps_here = clamp(rs / R_eff, 1e-6, 1.0 - 1e-6)
+        ft_here = f_trap === nothing ? trapped_fraction_eps(eps_here) :
+                  Float64(f_trap)
+        nue_here = nu_e_star === nothing ?
+                   nu_star_e(n_e, t_e, R_eff, eps_here, qval, zeff;
+            lnLamb=lnLamb) :
+                   Float64(nu_e_star)
+        eta = eta_neoclassical(resistivity_model, n_e, t_e, zeff,
+            ft_here, nue_here; lnLamb=lnLamb)
+    end
+
+    # Basic plasma quantities
+    tau = t_i / t_e
+    rho = mu_i * M_P * n_e
+
+    # Electron-electron collision time (still needed by the χ_∥-matching
+    # critical-Δ regardless of the resistivity model).
+    tau_ee = tau_ee_spitzer_harm(n_e, t_e; lnLamb=lnLamb)
+
+    # Characteristic field, Alfven speed, length scales, fundamental
+    # timescales.
+    rho_s = 1.02e-4 * sqrt(mu_i * t_e) / bt                 # ion Larmor [m]
+    d_i = sqrt((mu_i * M_P) / (n_e * E_CHG^2 * MU_0))     # ion skin depth [m]
+
+    # Alfven time uses minor-radius shear directly (sval enters the
+    # b_l = (n/m) r_s sval bt / R0 expression and cancels through to
+    # tau_h = R0 sqrt(mu0 rho) / (n sval bt)).
+    tau_h = R0 * sqrt(MU_0 * rho) / (n * sval_r * bt)
+    # Resistive diffusion time τ_R = μ₀ r_s² / η (Fitzpatrick 2023), with
+    # the selected η closure — neoclassical by default — setting the
+    # Lundquist number.
+    tau_r = MU_0 * rs^2 / eta
+
+    # Lundquist number and Q-conversion factor
+    lu = tau_r / tau_h
+    tauk = lu^(1.0 / 3.0) * tau_h         # = Qconv
+
+    # Normalized diamagnetic frequencies, Q = -tauk·ω; see docstring sign
+    # convention.
+    Q_e = -tauk * omega_e
+    Q_i = -tauk * omega_i
+    Q_e_minus_Q_i = Q_e - Q_i
+    # iota_e = Q_e/(Q_e - Q_i) is singular when the electron and ion
+    # diamagnetic frequencies are degenerate (ω_*e == ω_*i). The downstream
+    # Riccati coefficients divide by iota_e, so silently setting it to 0 (or
+    # Inf) produces NaN/Inf; surface the degeneracy as a clear error instead.
+    Q_e_minus_Q_i != 0 ||
+        throw(
+            ArgumentError(
+                "slayer_parameters: Q_e == Q_i (degenerate " *
+                "electron/ion diamagnetic frequencies) makes " *
+                "iota_e = Q_e/(Q_e - Q_i) singular. Check the " *
+                "diamagnetic-frequency inputs ω_*e, ω_*i."
+            )
+        )
+    iota_e = Q_e / Q_e_minus_Q_i
+
+    # Plasma beta and compressibility
+    lbeta = (5.0 / 3.0) * MU_0 * n_e * E_CHG * (t_e + t_i) / bt^2
+    c_beta = sqrt(lbeta / (1.0 + lbeta))
+
+    # Effective Prandtl-like transport ratios
+    tau_perp = rs^2 / chi_perp
+    P_perp = tau_r / tau_perp
+    tau_tor = rs^2 / chi_tor
+    P_tor = tau_r / tau_tor
+
+    # Normalized beta-related width and Δ-normalization
+    d_beta = c_beta * d_i
+    D_norm = (d_beta / rs) * lu^(1.0 / 3.0) * sqrt(tau / (1.0 + tau))
+    delta_n = lu^(1.0 / 3.0) / rs
+
+    # Critical-Δ offset from chi_parallel matching
+    dc_tmp = _solve_dc_tmp(; dc_type=dc_type, dr_val=dr_val, dgeo_val=dgeo_val,
+        chi_perp=chi_perp, t_e=t_e, zeff=zeff,
+        tau_ee=tau_ee, rs=rs, R0=R0, sval_r=sval_r,
+        n_tor=n)
+
+    return SLAYERParameters(;
+        ising=ising, m=m, n=n,
+        tau=tau, lu=lu, c_beta=c_beta, D_norm=D_norm,
+        P_perp=P_perp, P_tor=P_tor,
+        Q_e=Q_e, Q_i=Q_i, iota_e=iota_e,
+        tauk=tauk, tau_r=tau_r, delta_n=delta_n,
+        rs=rs, R0=R0, bt=bt, sval_r=sval_r,
+        dr_val=dr_val, dgeo_val=dgeo_val,
+        eta=eta, d_beta=d_beta,
+        dc_tmp=dc_tmp, dc_type=dc_type
+    )
+end
diff --git a/src/Tearing/InnerLayer/SLAYER/LayerThickness.jl b/src/Tearing/InnerLayer/SLAYER/LayerThickness.jl
new file mode 100644
index 000000000..eb16a7639
--- /dev/null
+++ b/src/Tearing/InnerLayer/SLAYER/LayerThickness.jl
@@ -0,0 +1,171 @@
+# LayerThickness.jl
+#
+# Resistive inner-layer *width* (del_s) diagnostic for the SLAYER two-fluid
+# drift-MHD slab layer (Fitzpatrick, Tearing Mode Dynamics in Tokamak
+# Plasmas, IOP 2023; Park et al. 2022, Phys. Plasmas 29, 122505; Burgess
+# et al. 2026). This is a distinct quantity from the matching index
+# Δ = π/W' returned by the dispersion solver (Riccati.jl): it integrates a
+# separate reduced Riccati ODE evaluated at the electron diamagnetic
+# frequency Q_e and extracts a dimensionless width ratio delta_s/d_beta,
+# which scales to a layer thickness in metres via the beta-weighted ion
+# length d_beta.
+#
+# Q_i, c_beta, and the scanned Q are NOT referenced by this width
+# formulation; the E/F coefficients depend only on Q_e, P_perp, P_tor, tau,
+# and D_norm.
+
+using OrdinaryDiffEq
+
+# ---------------------------------------------------------------------
+# Pre-computed q-independent constants for the del_s Riccati ODE.
+# Normalisation block:
+#   Q_hat      = (Q_e (1+tau)/tau) / D_norm^4
+#   P_perp_hat = P_perp / D_norm^6
+#   P_tor_hat  = P_tor  / D_norm^6
+# `one_plus = 1 + 1/tau`, `inv_c = 1/(1+1/tau)` recur in E, F and the
+# boundary/extraction prefactor, so they are cached here too.
+# ---------------------------------------------------------------------
+struct _DelSConsts
+    Q_hat::Float64       # normalised electron diamagnetic frequency
+    Pperp_hat::Float64   # normalised perpendicular Prandtl number
+    Ptor_hat::Float64    # normalised toroidal Prandtl number
+    one_plus::Float64    # 1 + 1/tau
+    inv_c::Float64       # 1 / (1 + 1/tau)
+end
+
+@inline function _build_dels_consts(p::SLAYERParameters)
+    one_plus = 1.0 + 1.0 / p.tau                       # (1+tau)/tau
+    D2 = p.D_norm * p.D_norm
+    D4 = D2 * D2
+    D6 = D4 * D2
+    return _DelSConsts(
+        (p.Q_e * one_plus) / D4,
+        p.P_perp / D6,
+        p.P_tor / D6,
+        one_plus,
+        1.0 / one_plus
+    )
+end
+
+# Scalar ODE right-hand side dW/dq for the del_s Riccati. The E, F
+# dispersion coefficients are q-dependent and complex (via the `im·Q_hat`
+# terms);
+# everything else is cached in `_DelSConsts`.
+@inline function _dels_rhs(W::Number, c::_DelSConsts, q::Real)
+    q2 = q * q
+    q4 = q2 * q2
+    E = -(c.Q_hat^2) * c.inv_c -
+        im * c.Q_hat * (c.Pperp_hat + c.Ptor_hat) * q2 +
+        c.Pperp_hat * c.Ptor_hat * q4
+    F = c.Pperp_hat - im * c.Q_hat + c.one_plus * c.Ptor_hat * q2
+    return W / q - (W * W) / q + (q * E) / F
+end
+
+# Analytic Jacobian dF/dW: the q·E/F term is W-independent, leaving
+# d/dW(W/q - W²/q) = 1/q - 2W/q.
+@inline _dels_jac(W::Number, c::_DelSConsts, q::Real) = 1.0 / q - 2.0 * W / q
+
+"""
+    riccati_del_s(p::SLAYERParameters;
+                  q_start=5*p.D_norm, q_min=1e-5,
+                  reltol=1e-10, abstol=1e-10, maxiters=50_000,
+                  solver=Rodas5P(autodiff=false)) -> ComplexF64
+
+Solve the SLAYER `del_s` inner-layer Riccati ODE and return the
+**dimensionless** layer-thickness ratio `δ_s / d_β` at one rational
+surface (see file header for references).
+
+Integrates `dW/dq = W/q − W²/q + q·E/F` inward from `q_start = 5·D_norm`
+to `q_min`, with asymptotic boundary value `W = −α·q_start² − 0.5`,
+`α = √(P̂_⊥ / (1 + 1/τ))`. The result is
+`−(π / √(1 + 1/τ)) · W′(q_min)`, evaluated from a single RHS call at the
+inner endpoint.
+
+Returns `NaN + NaN·im` if the stiff integration does not converge (the
+caller treats this as a missing diagnostic rather than a hard error).
+
+Multiply the result by `p.d_beta` to obtain the resistive layer
+thickness in meters (see [`slayer_layer_thickness`](@ref)).
+"""
+function riccati_del_s(p::SLAYERParameters;
+    q_start::Real=5.0 * p.D_norm,
+    q_min::Real=1e-5,
+    reltol::Real=1e-10,
+    abstol::Real=1e-10,
+    maxiters::Integer=50_000,
+    solver=Rodas5P(; autodiff=false))
+    if !(q_start > q_min)
+        @debug "riccati_del_s: degenerate integration span" q_start q_min p.D_norm
+        return ComplexF64(NaN, NaN)
+    end
+
+    c = _build_dels_consts(p)
+
+    # Asymptotic boundary condition at large q. P_hat is
+    # the normalised P_perp, i.e. c.Pperp_hat; α and W₀ are real.
+    α = sqrt(c.Pperp_hat * c.inv_c)
+    W0 = ComplexF64(-α * q_start^2 - 0.5)
+
+    f = ODEFunction{false}(_dels_rhs; jac=_dels_jac)
+    prob = ODEProblem(f, W0, (q_start, q_min), c)
+    sol = solve(prob, solver;
+        reltol=reltol, abstol=abstol, maxiters=maxiters,
+        save_everystep=false, dense=false)
+
+    if sol.retcode != ReturnCode.Success
+        @debug "SLAYER riccati_del_s did not return Success" sol.retcode
+        return ComplexF64(NaN, NaN)
+    end
+
+    W_end = sol.u[end]
+    dW_end = _dels_rhs(W_end, c, q_min)
+    return -(π / sqrt(c.one_plus)) * dW_end
+end
+
+"""
+    LayerWidths
+
+Resistive inner-layer length scales at one rational surface, in meters.
+The primary quantity is `delta_s_m`, the resistive layer thickness from
+the SLAYER `del_s` Riccati solve; `d_beta` is the β-weighted ion scale it
+is built from, retained as a drift-scale reference.
+
+# Fields
+
+  - `ising`, `m`, `n` -- surface index and mode numbers (traceability)
+  - `dels_db`   -- dimensionless `δ_s / d_β` from [`riccati_del_s`](@ref)
+  - `delta_s`   -- complex layer thickness `δ_s = dels_db · d_β` [m]
+  - `delta_s_m` -- `|δ_s|`, the resistive layer thickness [m] (primary)
+  - `d_beta`    -- β-weighted ion scale `c_β·d_i` [m] (drift reference)
+
+`delta_s_m` should sit within a few orders of magnitude of `d_beta` for a
+well-posed surface (`dels_db` is O(1)); a large gap flags a normalisation
+or input problem.
+"""
+struct LayerWidths
+    ising::Int
+    m::Int
+    n::Int
+    dels_db::ComplexF64
+    delta_s::ComplexF64
+    delta_s_m::Float64
+    d_beta::Float64
+end
+
+"""
+    slayer_layer_thickness(p::SLAYERParameters; kwargs...) -> LayerWidths
+
+Compute the resistive inner-layer thickness in meters at one rational
+surface.
+
+Runs [`riccati_del_s`](@ref) for the dimensionless `δ_s / d_β` and scales
+by `p.d_beta` to obtain `δ_s` in meters. Keyword arguments are forwarded
+to `riccati_del_s`.
+"""
+function slayer_layer_thickness(p::SLAYERParameters; kwargs...)
+    dels_db = riccati_del_s(p; kwargs...)
+    delta_s = dels_db * p.d_beta
+    return LayerWidths(p.ising, p.m, p.n,
+        dels_db, delta_s, abs(delta_s),
+        p.d_beta)
+end
diff --git a/src/Tearing/InnerLayer/SLAYER/Riccati.jl b/src/Tearing/InnerLayer/SLAYER/Riccati.jl
new file mode 100644
index 000000000..87781259b
--- /dev/null
+++ b/src/Tearing/InnerLayer/SLAYER/Riccati.jl
@@ -0,0 +1,267 @@
+# Riccati.jl
+#
+# Inner-layer Δ via the Fitzpatrick two-fluid drift-MHD layer Riccati ODE
+# (Fitzpatrick, Tearing Mode Dynamics in Tokamak Plasmas, IOP 2023; Park
+# et al. 2022, Phys. Plasmas 29, 122505; Burgess et al. 2026), for the
+# pressureless tearing channel (no parallel flow, pe = 0). The A, B, C
+# coefficients, the dW/dp Riccati equation, the large-p asymptotic boundary
+# conditions, the regime test D² > ι_e P_⊥/P_tor^(2/3), the small-p
+# Δ = π/W' extraction, and the analytic Jacobian all follow that layer
+# formulation; the full C(p) form is retained in every regime (no low-D
+# reduction).
+#
+# The complex normalized growth rate `Q = ω + iγ` is passed directly to
+# `solve_inner` rather than carried on the parameter struct. All other
+# inputs come from `SLAYERParameters` (see `LayerParameters.jl`).
+#
+# Returns the parity-projected matching data as an `InnerLayerResponse`
+# with only the `tearing` channel populated so callers can treat SLAYER and
+# GGJ interchangeably through the shared `InnerLayerModel` interface.
+# SLAYER's inner-layer dispersion relation produces a single complex Δ
+# (the tearing channel); the interchange channel is zero.
+
+using OrdinaryDiffEq
+
+# ---------------------------------------------------------------------
+# Coefficient evaluation for the two-fluid layer A, B, C terms (Fitzpatrick 2023).
+#
+# All x-independent quantities are bundled in `_RiccatiConsts` and computed
+# once per `solve_inner` call (see line ~200). The hot RHS / Jacobian
+# evaluations then access only the bundled constants and `x`, avoiding the
+# tens of thousands of redundant complex muls/adds the prior code did.
+# ---------------------------------------------------------------------
+
+# Pre-computed x-independent constants for the Fitzpatrick Riccati ODE.
+# Derived from `(p::SLAYERParameters, Q::ComplexF64)` once per solve. Used as
+# the integrator `params` so `_riccati_f_rhs` and `_riccati_f_jac` only need
+# the x-dependent algebra.
+struct _RiccatiConsts
+    Q_plus_iQe::ComplexF64    # constant part of denom = Q + iQe + x²
+    A::ComplexF64             # Q·(Q + iQi)               — fB constant term
+    B::ComplexF64             # (Q + iQi)·(P_perp + P_tor) — fB · x² coefficient
+    C::Float64                # P_perp · P_tor            — fB · x⁴ coefficient
+    E::ComplexF64             # (Q + iQi) · D² + P_perp   — fC · x² coefficient
+    G::Float64                # P_tor · D² / iota_e       — fC · x⁴ coefficient
+end
+
+@inline function _build_riccati_consts(p::SLAYERParameters, Q::ComplexF64)
+    Q_plus_iQe = Q + im * p.Q_e
+    Q_plus_iQi = Q + im * p.Q_i
+    D2 = p.D_norm * p.D_norm
+    return _RiccatiConsts(
+        Q_plus_iQe,
+        Q * Q_plus_iQi,                                   # A
+        Q_plus_iQi * (p.P_perp + p.P_tor),                # B
+        p.P_perp * p.P_tor,                               # C
+        p.P_perp + Q_plus_iQi * D2,                       # E
+        p.P_tor * D2 / p.iota_e                          # G
+    )
+end
+
+# Riccati RHS coefficients fA, fA', fB, fC at point x. Receives the
+# pre-built `_RiccatiConsts` so each call costs only a handful of muls/adds
+# plus one complex division (the fA = p²/denom).
+@inline function _riccati_f_coeffs(c::_RiccatiConsts, x::Real)
+    p2 = x * x
+    p4 = p2 * p2
+    denom = c.Q_plus_iQe + p2
+
+    fA = p2 / denom
+    # Use the numerator-subtracts-twice-p² form rather than the algebraic
+    # identity 1 − 2·fA. The two are mathematically equal, but the
+    # integrator's adaptive stepping near marginal stability compounds
+    # ULP-level differences in fA' over thousands of steps; this form keeps
+    # the result tighter than the identity, which drifts to ~3e-3 relative
+    # (within abs-tolerance, but tighter is better).
+    fA_prime = (denom - 2 * p2) / denom
+
+    fB = c.A + c.B * p2 + c.C * p4
+    fC = c.Q_plus_iQe + c.E * p2 + c.G * p4
+
+    return fA, fA_prime, fB, fC
+end
+
+# Scalar ODE right-hand side dW/dp for OrdinaryDiffEq.
+#
+# This is a 1-equation ODE — modeling W(x) as a `ComplexF64` scalar (rather
+# than a 1-element `Vector{ComplexF64}`) lets the integrator's stage updates
+# stay on the stack with no per-step allocations. SDIRK + Rosenbrock + BDF
+# methods in OrdinaryDiffEq all support scalar `u`.
+@inline function _riccati_f_rhs(W::Number, consts::_RiccatiConsts, x::Real)
+    fA, fA_prime, fB, fC = _riccati_f_coeffs(consts, x)
+    return -(fA_prime / x) * W - W * W / x + (fB / (fA * fC)) * (x * x * x)
+end
+
+# Analytic Jacobian of the Riccati RHS. The full RHS has
+# both the explicit (fA'/p, fB·p³) terms and the W² term; for the
+# Jacobian only the W-dependent pieces survive. Returns a scalar — the
+# 1×1 Jacobian of the scalar ODE.
+@inline function _riccati_f_jac(W::Number, consts::_RiccatiConsts, x::Real)
+    p2 = x * x
+    denom = consts.Q_plus_iQe + p2
+    fA_prime = (denom - 2 * p2) / denom
+    return -(fA_prime / x) - 2 * W / x
+end
+
+# ---------------------------------------------------------------------
+# Boundary-condition selection (large-p asymptotics, Fitzpatrick 2023).
+# Two regimes selected by D_norm² vs.
+# iota_e·P_perp/P_tor^(2/3).
+# ---------------------------------------------------------------------
+
+# Returns (p_start, W_at_p_start, branch) where `branch ∈ (:large_D, :small_D)`.
+function _riccati_f_initial(p::SLAYERParameters, Q::ComplexF64;
+    p_floor::Real=6.0)
+    D2 = p.D_norm * p.D_norm
+    Pperp_over_Ptor23 = p.P_perp / p.P_tor^(2 / 3)
+
+    if D2 > p.iota_e * Pperp_over_Ptor23
+        # Large-D_norm branch. In ((P_tor·D²)/(iota_e·P_tor·P_perp))^(1/4)
+        # the P_tor factor cancels — written out for traceability.
+        p_start = max(((p.P_tor * D2) / (p.iota_e * p.P_tor * p.P_perp))^0.25,
+            p_floor)
+
+        ak = -(Q + im * p.Q_e)
+        bk = (p.iota_e * p.P_perp * p.P_tor) / (p.P_tor * D2)
+        ck = bk * (1 + (Q + im * p.Q_i) * ((p.P_tor + p.P_perp) /
+                                           (p.P_tor * p.P_perp))
+                   -
+                   (p.P_perp + (Q + im * p.Q_i) * D2) *
+                   (p.iota_e / (p.P_tor * D2)))
+        sqrt_bk = sqrt(bk)
+        xk = (ck - sqrt_bk * (1 - sqrt_bk * ak)) / (2 * sqrt_bk)
+
+        W_bound = xk - sqrt_bk * p_start
+        return p_start, W_bound, :large_D
+    else
+        # Small-D_norm branch.
+        p_start = max(1.0 / p.P_tor^(1 / 6), p_floor)
+
+        ak = -(Q + im * p.Q_e)
+        bk = ComplexF64(p.P_tor)        # promoted to ComplexF64 for sqrt below
+        ck = -im * (p.Q_e - p.Q_i) * (p.P_tor / p.P_perp) + (Q + im * p.Q_i)
+        sqrt_bk = sqrt(bk)
+        xk = (ak * bk - ck) / (2 * sqrt_bk)
+
+        W_bound = -1.0 + xk * p_start - sqrt_bk * p_start^3
+        return p_start, W_bound, :small_D
+    end
+end
+
+# ---------------------------------------------------------------------
+# solve_inner dispatch for SLAYERModel{:fitzpatrick}.
+# ---------------------------------------------------------------------
+
+"""
+    solve_inner(::SLAYERModel{:fitzpatrick},
+                p::SLAYERParameters, Q::Number;
+                pmin=1e-6, p_floor=6.0,
+                reltol=1e-10, abstol=1e-10,
+                maxiters=50_000,
+                solver=Rodas5P(autodiff=false)) -> InnerLayerResponse
+
+Solve the Fitzpatrick SLAYER inner-layer Riccati ODE for the complex
+normalized growth rate `Q = ω + iγ`. Returns an `InnerLayerResponse` with
+`tearing = Δ` and `interchange = 0`, so the result is interface-compatible
+with `GGJModel.solve_inner` (which populates both channels); SLAYER's
+pressureless layer produces only the tearing channel.
+
+# Algorithm
+
+Implements the Fitzpatrick two-fluid drift-MHD layer formulation
+(Fitzpatrick 2023; Park et al. 2022) for the pressureless tearing channel
+(no parallel flow, pe = 0). Integrates
+`dW/dp = -(fA'/p)·W − W²/p + (fB/(fA·fC))·p³` from a large `p_start`
+(selected by `_riccati_f_initial` according to whether
+`D_norm² ≷ iota_e·P_perp/P_tor^(2/3)`) inward to `pmin`, then computes
+`Δ = π / W'(pmin)` from a single RHS evaluation at the inner endpoint.
+
+# Solver
+
+Default `Rodas5P(autodiff=false)` (Rosenbrock, stiff-friendly). The
+analytic Jacobian wired via the `ODEFunction(jac=...)` field accelerates
+the Newton solves. AD is disabled because complex `Dual` propagation
+through the chained denominators incurs allocations in this regime;
+finite-difference fallback is fast enough for the 1-equation system.
+
+**Note on solver swaps:** sub-percent floating-point differences between
+ODE solvers cascade through the outer AMR's cell-flagging decisions
+(`ContourSearchAMR.jl::_crosses_zero`) and produce **structurally
+different** AMR cell trees. Swapping the ODE solver has been observed to
+change the classified root/pole inventory substantially even when the
+most-unstable γ agrees to within ~1e-5 relative. So solver choice is not
+just a per-call optimization — it affects the downstream root/pole
+inventory. Future solver swaps need to be validated against the topology
+fields (`n_valid_roots`, `n_poles`), not just γ.
+
+# Keyword arguments
+
+  - `pmin`     -- inner-layer cutoff (default 1e-6)
+  - `p_floor`  -- floor on `p_start` (default 6.0)
+  - `reltol`,`abstol`,`maxiters` -- stiff-solver tolerance/iteration limits
+  - `solver`   -- any OrdinaryDiffEq algorithm; pass `Tsit5()` for the
+    non-stiff path (rarely needed here)
+"""
+function solve_inner(::SLAYERModel{:fitzpatrick},
+    p::SLAYERParameters, Q::Number;
+    pmin::Real=1e-6,
+    p_floor::Real=6.0,
+    reltol::Real=1e-10,
+    abstol::Real=1e-10,
+    maxiters::Integer=50_000,
+    solver=Rodas5P(; autodiff=false))
+    # Convention bridge between our scan plane and the layer eigenvalue.
+    # We scan in the Park plane `Q = ω + iγ` (+γ on the +imag axis; Park
+    # et al. 2022). Fitzpatrick's normalized layer eigenvalue is
+    # `ĝ = (γ − iω)·τ_k` in the co-rotating frame (Fitzpatrick 2023:
+    # γ = Re(ĝ)/τ_k, ω = −Im(ĝ)/τ_k), so +γ sits on his +real axis. The two
+    # planes differ by a pure −90° rotation, `ĝ = −i·Q` — relabeling which
+    # axis is growth, no physics.
+    #
+    # The layer ODE coefficients are analytic in ĝ with Q_e, Q_i, D, P_⊥,
+    # P_φ all real, so Schwarz reflection gives `Δ̂_s(conj ĝ) = conj(Δ̂_s(ĝ))`.
+    # We feed `Q_c = i·conj(Q) = conj(−i·Q) = conj(ĝ)`, hence evaluate
+    # `conj(Δ̂_s(ĝ))`. Conjugation maps zeros to zeros, so the extracted
+    # growth-rate roots are identical to those of Δ̂_s(ĝ); it only reflects
+    # the off-root residual surface, fixing the integration-path orientation
+    # so |Δ| contours are consistent across the scan plane.
+    Q_c = im * conj(ComplexF64(Q))
+
+    # Boundary condition at p_start
+    p_start, W_bound, _ = _riccati_f_initial(p, Q_c; p_floor=p_floor)
+
+    # Pre-compute x-independent constants ONCE; the integrator threads this
+    # through to every RHS / Jacobian call instead of recomputing per-step.
+    rhs_params = _build_riccati_consts(p, Q_c)
+
+    # Scalar `u0`: the ODE state is a single `ComplexF64`, not a 1-element
+    # vector. OrdinaryDiffEq supports scalar problems via the out-of-place
+    # form (`ODEFunction{false}`). This eliminates the per-step heap-
+    # allocation of intermediate `dW` vectors that the in-place form
+    # incurred for every stage of every accepted/rejected step.
+    u0 = ComplexF64(W_bound)
+    f = ODEFunction{false}(_riccati_f_rhs; jac=_riccati_f_jac)
+    prob = ODEProblem(f, u0, (p_start, pmin), rhs_params)
+    sol = solve(prob, solver;
+        reltol=reltol, abstol=abstol, maxiters=maxiters,
+        save_everystep=false, dense=false)
+
+    if sol.retcode != ReturnCode.Success
+        # Unconverged solve: return a NaN sentinel so the dispersion scan / AMR
+        # flags this Q-cell (via its isfinite checks) rather than ingesting a
+        # bogus finite Δ built from an unconverged W_end. @debug not @warn: in a
+        # dense Q-plane scan failures cluster near poles and would flood the log.
+        @debug "SLAYER Riccati integration did not return Success" sol.retcode
+        return InnerLayerResponse(ComplexF64(NaN, NaN), zero(ComplexF64))
+    end
+
+    # Δ = π / W'(pmin) — single RHS evaluation at the inner endpoint
+    W_end = sol.u[end]
+    dW_end = _riccati_f_rhs(W_end, rhs_params, pmin)
+    Δ::ComplexF64 = π / dW_end
+
+    # Fitzpatrick / pressureless SLAYER has no interchange channel
+    # (the Δ_− / even-parity matching quantity is identically zero in
+    # the pressureless limit), so populate only the tearing field.
+    return InnerLayerResponse(Δ, zero(ComplexF64))
+end
diff --git a/src/Tearing/InnerLayer/SLAYER/SLAYER.jl b/src/Tearing/InnerLayer/SLAYER/SLAYER.jl
new file mode 100644
index 000000000..ece897c15
--- /dev/null
+++ b/src/Tearing/InnerLayer/SLAYER/SLAYER.jl
@@ -0,0 +1,62 @@
+# SLAYER.jl
+#
+# SLAYER (Slab Layer) two-fluid drift-MHD inner-layer model (Fitzpatrick,
+# Tearing Mode Dynamics in Tokamak Plasmas, IOP 2023; Park et al. 2022,
+# Phys. Plasmas 29, 122505; Burgess et al. 2026). The dispersion path uses
+# the Fitzpatrick layer formulation — P_perp / P_tor transport, c_beta
+# compressibility, D_norm normalized ion-skin scale, two-fluid drift
+# coupling via Q_e, Q_i, iota_e (see `Riccati.jl`).
+#
+# A separate `del_s` layer-thickness diagnostic (`slayer_layer_thickness` in
+# `LayerThickness.jl`) returns the resistive layer width in meters at each
+# rational surface.
+#
+# Type-parameter `S` of `SLAYERModel{S}` selects the Riccati formulation
+# used for the dispersion relation; only `:fitzpatrick` is implemented.
+#
+# `Q = ω + iγ` is passed directly to `solve_inner` rather than stored on
+# the parameter struct.
+
+module SLAYER
+
+using LinearAlgebra
+using StaticArrays
+
+import ..InnerLayerModel, ..InnerLayerResponse, ..solve_inner, ..InnerLayerParameters
+using ...Utilities.PhysicalConstants
+using ...Utilities.NeoclassicalResistivity
+using ...Utilities.NeoclassicalResistivity: NeoResistivityModel, SpitzerModel,
+    SpitzerHarmModel, SauterNeoModel, RedlNeoModel,
+    coulomb_log_e, eta_spitzer, tau_ee_spitzer_harm, eta_spitzer_harm,
+    trapped_fraction_eps, nu_star_e, eta_neoclassical
+
+"""
+    SLAYERModel{S} <: InnerLayerModel
+
+SLAYER inner-layer model selector. The type parameter `S` selects the
+Riccati formulation:
+
+  - `:fitzpatrick` -- P_perp/P_tor Fitzpatrick formulation (default;
+    Fitzpatrick 2023 two-fluid drift-MHD layer)
+
+Future dispersion variants (e.g. `:standard`) may be added but are not
+currently implemented. The `del_s` formulation is exposed separately as
+the layer-thickness diagnostic [`slayer_layer_thickness`](@ref), not as
+a dispersion `solve_inner` path.
+"""
+struct SLAYERModel{S} <: InnerLayerModel end
+
+SLAYERModel(; variant::Symbol=:fitzpatrick) = SLAYERModel{variant}()
+
+include("LayerParameters.jl")
+include("Riccati.jl")
+include("LayerThickness.jl")
+include("LayerInputs.jl")
+
+export SLAYERModel, SLAYERParameters, slayer_parameters
+export r_based_shear
+export riccati_del_s, slayer_layer_thickness, LayerWidths
+export surface_minor_radius, surface_da_dpsi, build_slayer_inputs
+export NeoResistivityModel, SpitzerModel, SpitzerHarmModel, SauterNeoModel, RedlNeoModel
+
+end # module SLAYER
diff --git a/src/Tearing/Runner/Control.jl b/src/Tearing/Runner/Control.jl
new file mode 100644
index 000000000..17128a3d6
--- /dev/null
+++ b/src/Tearing/Runner/Control.jl
@@ -0,0 +1,268 @@
+# Control.jl
+#
+# `SLAYERControl` holds every user-facing knob that drives the SLAYER
+# growth-rate analysis. Populated either directly via the `@kwdef`
+# constructor or by parsing the `[SLAYER]` (and nested `[SLAYER.*]`)
+# section(s) of a `gpec.toml`.
+
+"""
+    SLAYERControl
+
+Configuration for the SLAYER tearing-mode analysis. All fields are
+user-facing: read from the `[SLAYER]` TOML section of a `gpec.toml` via
+`slayer_control_from_toml`, or built directly via the `@kwdef` keyword
+constructor.
+
+# Core toggles
+
+  - `enabled`       -- run the analysis at all (default `false`)
+  - `inner_model`   -- `:slayer_fitzpatrick` (default), `:ggj_shooting`, or
+    `:ggj_galerkin`
+  - `scan_mode`     -- `:amr` (default) or `:brute_force`
+  - `coupling_mode` -- `:uncoupled` (default, per-surface) or `:coupled`
+    (multi-surface determinant)
+  - `dc_type`       -- critical-Δ offset selector, one of `:none`, `:lar`,
+    `:rfitzp`, `:toroidal` (χ_‖-matching critical-Δ formulas,
+    Connor-Hastie-Helander 2015)
+  - `msing_max`     -- number of surfaces to include in the coupled
+    determinant (default 3; capped at `length(sings)` at runtime)
+
+# Physics knobs
+
+  - `bt`       -- toroidal field [T]. `nothing` → use `equil.config.b0exp`
+  - `mu_i`     -- ion mass in proton-mass units (default 2.0 for D)
+  - `zeff`     -- effective charge
+  - `chi_perp`, `chi_tor` -- fallback perpendicular / toroidal heat
+    diffusivity [m²/s], used only when the kinetic file carries no usable
+    `chi_e`/`chi_phi` profile (dataset absent or all-zero); otherwise the
+    file's χ⊥(ψ)/χ_φ(ψ) take precedence
+  - `dr_val`, `dgeo_val`  -- critical-Δ formula inputs. `nothing` (default)
+    auto-derives them from the equilibrium: `dr_val` from the resistive
+    interchange index `D_R = E + F + H²` at each surface, `dgeo_val` from the
+    toroidal geometric factor (required only by `dc_type=:toroidal`). Supply a
+    scalar only to override the auto-derivation; an explicit `0.0` disables the
+    critical-Δ offset (Δ_crit ≡ 0)
+  - `theta_sample` -- poloidal angle at which to sample minor radius
+    (default 0.0, outboard midplane)
+  - `resistivity_model` -- η closure setting τ_R = μ₀r_s²/η: `:sauter`
+    (default, neoclassical F_33), `:redl`, `:spitzer` (Sauter 18a, no
+    trapped correction), or `:spitzer_harm` (legacy Fitzpatrick σ_∥)
+  - `lnLambda_form` -- Coulomb-log form: `:nrl` (default), `:sauter`, or
+    `:wesson` (legacy; pair with `:spitzer_harm` to reproduce old SLAYER)
+
+# Scan grid (used for both brute-force and AMR initial mesh)
+
+  - `Q_re_range`, `Q_im_range` -- box in the normalized Q plane
+  - `nre`, `nim`    -- grid resolution along each axis
+
+# AMR refinement
+
+  - `amr_passes`    -- max refinement levels
+  - `amr_max_cells` -- hard safety cap
+
+# Growth-rate-extraction filters
+
+  - `pole_threshold`      -- threshold for pole classification (default 10)
+  - `pole_threshold_adaptive` -- if true, pole_threshold is OVERRIDDEN per
+    scan with `10 × median(|Δ|)` over the scan grid. The median is robust
+    where `|mean(Δ)|` is not: it resists inflation from the near-pole
+    samples that dominate a mean, and avoids the phase-cancellation that
+    can shrink `|mean|`. Useful when |Δ| spans 8+ orders of magnitude
+    (e.g. SLAYER scans where the hardcoded 10.0 default is too restrictive
+    and classifies all intersections as poles).
+  - `filter_above_poles`  -- discard roots above the highest pole γ
+  - `filter_outside_re`   -- condition the above-pole filter on the +γ
+    step exiting the Re(Δ)=0 contour loop
+
+# Kinetic-profile source
+
+SLAYER reads kinetic profiles through the shared `Equilibrium.read_kinetic_file`
+reader (the same standardized object used by the kinetic/NTV physics), so
+there is one consistent interface for resistive and kinetic profiles.
+
+  - `profile_file`   -- path to a kinetic-profile file (relative to the run
+    dir), required when SLAYER is enabled. HDF5 (`.h5`) files use the GPEC
+    kinetic schema and may carry `chi_e` (χ⊥) and `chi_phi` (χ_φ); ASCII
+    tables are also accepted but carry no χ. When a χ dataset is absent or
+    all-zero, the scalar `chi_perp`/`chi_tor` fallbacks below are used (so a
+    file can keep the χ keys set to 0 to defer to the scalars).
+  - `profile_group`  -- group within the HDF5 file (default `"/"`)
+
+# Output control
+
+  - `store_scan`  -- write the full Q/Δ scan grid to HDF5. `false` by
+    default to keep the output file small.
+"""
+@kwdef struct SLAYERControl
+    enabled::Bool = false
+
+    inner_model::Symbol = :slayer_fitzpatrick
+    scan_mode::Symbol = :amr
+    coupling_mode::Symbol = :uncoupled
+    dc_type::Symbol = :none
+    msing_max::Int = 3
+
+    bt::Union{Float64,Nothing} = nothing
+    mu_i::Float64 = 2.0
+    zeff::Float64 = 1.0
+    chi_perp::Float64 = 1.0
+    chi_tor::Float64 = 1.0
+    dr_val::Union{Float64,Nothing} = nothing
+    dgeo_val::Union{Float64,Nothing} = nothing
+    theta_sample::Float64 = 0.0
+    resistivity_model::Symbol = :sauter
+    lnLambda_form::Symbol = :nrl
+
+    Q_re_range::Tuple{Float64,Float64} = (-10.0, 10.0)
+    Q_im_range::Tuple{Float64,Float64} = (-2.0, 5.0)
+    nre::Int = 41
+    nim::Int = 31
+
+    amr_passes::Int = 4
+    amr_max_cells::Int = 10_000_000
+
+    # Multi-box stripe layout. When non-empty, `scan_mode=:amr` dispatches to
+    # `multi_box_amr_scan` instead of single-box `amr_scan`. Each entry is a
+    # dimensionless Q-space rectangle as `(omega_lo, omega_hi, gamma_lo,
+    # gamma_hi)`. Activity criteria fire on Re(Δ) sign change, Im(Δ) sign
+    # change, OR |Δ| ≥ pre-screen pole threshold. A typical 25-kHz stripe
+    # layout for DIII-D-style equilibria (with kHz/Q given by the per-surface
+    # τ_k) is built externally by the driver, converted to Q-units, and
+    # passed in here.
+    boxes::Vector{NTuple{4,Float64}} = NTuple{4,Float64}[]
+    multi_box_prescreen_n::Int = 25         # pre-screen grid resolution per box
+
+    pole_threshold::Float64 = 10.0
+    pole_threshold_adaptive::Bool = false
+    filter_above_poles::Bool = true
+    filter_outside_re::Bool = true
+    gap_kHz_threshold::Float64 = 1.0       # forwarded to find_growth_rates
+    # Refine each contour-intersection root to the true zero of the dispersion
+    # residual (isolate-then-polish). Makes the extracted γ resolution- and
+    # thread-independent; adds ~a handful of residual evals per root. Set false
+    # to report the raw marching-squares estimate. Applies to the UNCOUPLED
+    # (per-surface scalar) path; the coupled determinant is ill-conditioned and
+    # uses raw contour extraction regardless.
+    polish_roots::Bool = true
+    # Validity gate (uncoupled + polish_roots only): drop a root whose polished
+    # |residual| is not below validity_rtol × median(|Δ|) — a contour near-miss
+    # / failed-Δ'-BVP surface, not a real root. Flagged `:spurious`.
+    validity_rtol::Float64 = 1e-3
+
+    profile_file::String = ""
+    profile_group::String = "/"
+
+    store_scan::Bool = false
+end
+
+const _VALID_INNER_MODELS = (:slayer_fitzpatrick, :ggj_shooting, :ggj_galerkin)
+const _VALID_SCAN_MODES = (:amr, :brute_force)
+const _VALID_COUPLING_MODES = (:uncoupled, :coupled)
+const _VALID_DC_TYPES = (:none, :lar, :rfitzp, :toroidal)
+const _VALID_RESISTIVITY_MODELS = (:sauter, :redl, :spitzer, :spitzer_harm)
+const _VALID_LNLAMBDA_FORMS = (:nrl, :sauter, :wesson)
+
+function validate(ctrl::SLAYERControl)
+    ctrl.inner_model in _VALID_INNER_MODELS ||
+        throw(ArgumentError("SLAYERControl: inner_model=$(ctrl.inner_model) " *
+                            "not in $(_VALID_INNER_MODELS)"))
+    ctrl.scan_mode in _VALID_SCAN_MODES ||
+        throw(ArgumentError("SLAYERControl: scan_mode=$(ctrl.scan_mode) " *
+                            "not in $(_VALID_SCAN_MODES)"))
+    ctrl.coupling_mode in _VALID_COUPLING_MODES ||
+        throw(ArgumentError("SLAYERControl: coupling_mode=$(ctrl.coupling_mode) " *
+                            "not in $(_VALID_COUPLING_MODES)"))
+    ctrl.dc_type in _VALID_DC_TYPES ||
+        throw(ArgumentError("SLAYERControl: dc_type=$(ctrl.dc_type) " *
+                            "not in $(_VALID_DC_TYPES)"))
+    ctrl.resistivity_model in _VALID_RESISTIVITY_MODELS ||
+        throw(ArgumentError("SLAYERControl: resistivity_model=$(ctrl.resistivity_model) " *
+                            "not in $(_VALID_RESISTIVITY_MODELS)"))
+    ctrl.lnLambda_form in _VALID_LNLAMBDA_FORMS ||
+        throw(ArgumentError("SLAYERControl: lnLambda_form=$(ctrl.lnLambda_form) " *
+                            "not in $(_VALID_LNLAMBDA_FORMS)"))
+    ctrl.msing_max >= 1 ||
+        throw(ArgumentError("SLAYERControl: msing_max=$(ctrl.msing_max) must be ≥ 1"))
+    ctrl.nre >= 2 && ctrl.nim >= 2 ||
+        throw(ArgumentError("SLAYERControl: nre and nim must both be ≥ 2"))
+    ctrl.amr_passes >= 0 ||
+        throw(ArgumentError("SLAYERControl: amr_passes must be ≥ 0"))
+    return ctrl
+end
+
+# Helper: coerce range-like values to a 2-tuple of Float64
+_as_range(x::NTuple{2,<:Real}) = (Float64(x[1]), Float64(x[2]))
+_as_range(x::AbstractVector) = begin
+    length(x) == 2 || throw(ArgumentError("range must be length 2, got length $(length(x))"))
+    (Float64(x[1]), Float64(x[2]))
+end
+
+"""
+    slayer_control_from_toml(section::AbstractDict) -> SLAYERControl
+
+Parse a `[SLAYER]` TOML section into a `SLAYERControl`. Known nested
+subsections (`[SLAYER.scan_grid]`, `[SLAYER.amr]`,
+`[SLAYER.growth_rate_filter]`) are flattened into the top-level fields.
+Unknown keys raise an error so typos don't silently produce defaults.
+"""
+function slayer_control_from_toml(section::AbstractDict)
+    # Flatten nested sections into the top-level key dictionary
+    flat = Dict{String,Any}()
+    for (k, v) in section
+        if k == "scan_grid" && v isa AbstractDict
+            # Promote scan_grid fields to top-level
+            haskey(v, "Q_re_range") && (flat["Q_re_range"] = v["Q_re_range"])
+            haskey(v, "Q_im_range") && (flat["Q_im_range"] = v["Q_im_range"])
+            haskey(v, "nre") && (flat["nre"] = v["nre"])
+            haskey(v, "nim") && (flat["nim"] = v["nim"])
+        elseif k == "amr" && v isa AbstractDict
+            haskey(v, "passes") && (flat["amr_passes"] = v["passes"])
+            haskey(v, "max_cells") && (flat["amr_max_cells"] = v["max_cells"])
+        elseif k == "growth_rate_filter" && v isa AbstractDict
+            haskey(v, "pole_threshold") && (flat["pole_threshold"] = v["pole_threshold"])
+            haskey(v, "filter_above_poles") && (flat["filter_above_poles"] = v["filter_above_poles"])
+            haskey(v, "filter_outside_re") && (flat["filter_outside_re"] = v["filter_outside_re"])
+        else
+            flat[k] = v
+        end
+    end
+
+    # Validate keys against the struct fields
+    field_names = Set(String.(fieldnames(SLAYERControl)))
+    unknown = [k for k in keys(flat) if !(k in field_names)]
+    isempty(unknown) ||
+        throw(ArgumentError("slayer_control_from_toml: unknown keys " *
+                            "$(unknown) in [SLAYER] section. Known: " *
+                            "$(sort(collect(field_names)))."))
+
+    # Coerce types where needed
+    kwargs = Dict{Symbol,Any}()
+    for (k, v) in flat
+        sym = Symbol(k)
+        if sym in (:inner_model, :scan_mode, :coupling_mode, :dc_type,
+            :resistivity_model, :lnLambda_form)
+            kwargs[sym] = v isa Symbol ? v : Symbol(String(v))
+        elseif sym in (:Q_re_range, :Q_im_range)
+            kwargs[sym] = _as_range(v)
+        elseif sym in (:bt, :dr_val, :dgeo_val)
+            # Allow explicit nothing (auto-derive) or a number (override)
+            kwargs[sym] = v === nothing ? nothing : Float64(v)
+        elseif sym === :boxes
+            # `boxes` is a Vector{NTuple{4,Float64}}; from TOML this comes
+            # in as a list of 4-element arrays. Coerce each.
+            kwargs[sym] = NTuple{4,Float64}[
+                let bb = collect(Float64, b)
+                    length(bb) == 4 ||
+                        throw(ArgumentError("SLAYER.boxes entry must have 4 " *
+                                            "elements (omega_lo, omega_hi, " *
+                                            "gamma_lo, gamma_hi); got $b"))
+                    (bb[1], bb[2], bb[3], bb[4])
+                end
+                for b in v
+            ]
+        else
+            kwargs[sym] = v
+        end
+    end
+    return validate(SLAYERControl(; kwargs...))
+end
diff --git a/src/Tearing/Runner/HDF5Output.jl b/src/Tearing/Runner/HDF5Output.jl
new file mode 100644
index 000000000..107a3be1a
--- /dev/null
+++ b/src/Tearing/Runner/HDF5Output.jl
@@ -0,0 +1,238 @@
+# HDF5Output.jl
+#
+# Write a `SLAYERResult` into an HDF5 group. Designed to be called by the
+# existing `PerturbedEquilibrium.write_outputs_to_HDF5` path — the
+# top-level GPEC runner wires that up; this file only defines the pure
+# writer.
+#
+# Output layout (relative to the parent group the caller provides):
+#
+#   slayer/
+#   ├── settings/           -- control snapshot (strings, scalars)
+#   ├── per_surface/        -- struct-of-arrays for SLAYERParameters fields
+#   │   ├── psi, q, q1, ...
+#   │   └── ...
+#   ├── roots/              -- Q_root (real, imag), omega_Hz, gamma_Hz
+#   ├── diagnostics/        -- all_valid_roots, poles, filtered_roots
+#   │                           (flat-plus-offsets ragged encoding)
+#   └── scan/               -- optional: full Q/Δ scan data
+
+using HDF5
+
+"""
+    write_slayer_hdf5!(parent::Union{HDF5.File,HDF5.Group},
+                        result::SLAYERResult)
+
+Write `result` into a `slayer/` subgroup of `parent`. The subgroup is
+created if missing and overwritten if it already exists (keeps the
+output file reproducible across reruns).
+"""
+function write_slayer_hdf5!(parent::Union{HDF5.File,HDF5.Group},
+    result::SLAYERResult)
+    if haskey(parent, "slayer")
+        delete_object(parent, "slayer")
+    end
+    g = create_group(parent, "slayer")
+    g["enabled"] = Int(result.enabled)
+
+    result.enabled || return g    # nothing else to write
+
+    _write_settings!(g, result.control)
+    _write_per_surface!(g, result.params, result.dp_matrix)
+    _write_roots!(g, result)
+    _write_layer_widths!(g, result.layer_widths)
+    _write_diagnostics!(g, result)
+    if result.control.store_scan && !isempty(result.scan_data)
+        _write_scan_data!(g, result)
+    end
+    return g
+end
+
+# ---------- settings snapshot ----------
+function _write_settings!(g, ctrl::SLAYERControl)
+    s = create_group(g, "settings")
+    s["inner_model"] = String(ctrl.inner_model)
+    s["scan_mode"] = String(ctrl.scan_mode)
+    s["coupling_mode"] = String(ctrl.coupling_mode)
+    s["dc_type"] = String(ctrl.dc_type)
+    s["msing_max"] = ctrl.msing_max
+    s["bt"] = ctrl.bt === nothing ? NaN : ctrl.bt
+    s["mu_i"] = ctrl.mu_i
+    s["zeff"] = ctrl.zeff
+    s["chi_perp"] = ctrl.chi_perp
+    s["chi_tor"] = ctrl.chi_tor
+    # NaN sentinel records the auto-derive (nothing) setting in a numeric dataset.
+    s["dr_val"] = ctrl.dr_val === nothing ? NaN : ctrl.dr_val
+    s["dgeo_val"] = ctrl.dgeo_val === nothing ? NaN : ctrl.dgeo_val
+    s["theta_sample"] = ctrl.theta_sample
+    s["resistivity_model"] = String(ctrl.resistivity_model)
+    s["lnLambda_form"] = String(ctrl.lnLambda_form)
+    s["Q_re_range"] = collect(ctrl.Q_re_range)
+    s["Q_im_range"] = collect(ctrl.Q_im_range)
+    s["nre"] = ctrl.nre
+    s["nim"] = ctrl.nim
+    s["amr_passes"] = ctrl.amr_passes
+    s["amr_max_cells"] = ctrl.amr_max_cells
+    # Multi-box stripe layout: flatten Vector{NTuple{4}} to an N×4 matrix
+    # (empty → 0×4) so a rerun can reconstruct the exact scan boxes.
+    s["boxes"] = isempty(ctrl.boxes) ? Matrix{Float64}(undef, 0, 4) :
+                 permutedims(reduce(hcat, collect.(ctrl.boxes)))
+    s["multi_box_prescreen_n"] = ctrl.multi_box_prescreen_n
+    s["pole_threshold"] = ctrl.pole_threshold
+    s["pole_threshold_adaptive"] = Int(ctrl.pole_threshold_adaptive)
+    s["filter_above_poles"] = Int(ctrl.filter_above_poles)
+    s["filter_outside_re"] = Int(ctrl.filter_outside_re)
+    s["gap_kHz_threshold"] = ctrl.gap_kHz_threshold
+    s["polish_roots"] = Int(ctrl.polish_roots)
+    s["validity_rtol"] = ctrl.validity_rtol
+    s["profile_file"] = ctrl.profile_file
+    s["profile_group"] = ctrl.profile_group
+    s["store_scan"] = Int(ctrl.store_scan)
+    return nothing
+end
+
+# ---------- per-surface layer parameters ----------
+function _write_per_surface!(g, params::AbstractVector{SLAYERParameters},
+    dp_matrix::Matrix{ComplexF64})
+    ps = create_group(g, "per_surface")
+
+    # Scalar struct-of-arrays for all Float64 / Int fields
+    for fname in (:ising, :m, :n)
+        ps[String(fname)] = Int[getfield(p, fname) for p in params]
+    end
+    for fname in (:tau, :lu, :c_beta, :D_norm, :P_perp, :P_tor,
+        :Q_e, :Q_i, :iota_e,
+        :tauk, :tau_r, :delta_n,
+        :rs, :R0, :bt, :sval_r, :dr_val, :dgeo_val,
+        :eta, :d_beta, :dc_tmp)
+        ps[String(fname)] = Float64[getfield(p, fname) for p in params]
+    end
+    # Store dc_type per-surface as string array
+    ps["dc_type"] = String[String(p.dc_type) for p in params]
+
+    # Full Δ' matrix, split real/imag
+    dp = create_group(ps, "dp_matrix")
+    dp["real"] = real.(dp_matrix)
+    dp["imag"] = imag.(dp_matrix)
+    return nothing
+end
+
+# GGJ per-surface parameters carry the geometric coefficients (E,F,G,H,K,M)
+# and resistive/Alfvén times rather than the SLAYER dimensionless set.
+function _write_per_surface!(g, params::AbstractVector{GGJParameters},
+    dp_matrix::Matrix{ComplexF64})
+    ps = create_group(g, "per_surface")
+    ps["ising"] = Int[p.ising for p in params]
+    for fname in (:E, :F, :G, :H, :K, :M, :taua, :taur, :v1)
+        ps[String(fname)] = Float64[getfield(p, fname) for p in params]
+    end
+    dp = create_group(ps, "dp_matrix")
+    dp["real"] = real.(dp_matrix)
+    dp["imag"] = imag.(dp_matrix)
+    return nothing
+end
+
+# ---------- eigenvalue roots ----------
+function _write_roots!(g, r::SLAYERResult)
+    roots = create_group(g, "roots")
+    roots["Q_root_real"] = real.(r.Q_root)
+    roots["Q_root_imag"] = imag.(r.Q_root)
+    roots["omega_Hz"] = r.omega_Hz
+    roots["gamma_Hz"] = r.gamma_Hz
+    # `no_root[k] == 1` flags entries where the extraction found NO usable
+    # root (Q_root is NaN; omega_Hz/gamma_Hz are 0 placeholders, not a true
+    # γ≈0 result). Aligned element-wise with Q_root/omega_Hz/gamma_Hz.
+    extractions = r.coupled_extraction !== nothing ? [r.coupled_extraction] :
+                  r.per_surface_extraction
+    roots["no_root"] = Int[(:no_root in gr.warning_flags) ? 1 : 0
+                           for gr in extractions]
+    return nothing
+end
+
+# ---------- resistive layer thickness (del_s Riccati) ----------
+function _write_layer_widths!(g, widths::Vector{LayerWidths})
+    lw = create_group(g, "layer_widths")
+    for fname in (:ising, :m, :n)
+        lw[String(fname)] = Int[getfield(w, fname) for w in widths]
+    end
+    # Dimensionless del_s/d_beta and the complex layer thickness, split re/im.
+    lw["dels_db_real"] = Float64[real(w.dels_db) for w in widths]
+    lw["dels_db_imag"] = Float64[imag(w.dels_db) for w in widths]
+    lw["delta_s_real"] = Float64[real(w.delta_s) for w in widths]
+    lw["delta_s_imag"] = Float64[imag(w.delta_s) for w in widths]
+    # Physical thickness [m] and the β-weighted ion drift scale [m].
+    lw["delta_s_m"] = Float64[w.delta_s_m for w in widths]
+    lw["d_beta"] = Float64[w.d_beta for w in widths]
+    return nothing
+end
+
+# ---------- diagnostics: valid roots, poles, filtered roots ----------
+function _write_diagnostics!(g, r::SLAYERResult)
+    diag = create_group(g, "diagnostics")
+    # Uncoupled: one GrowthRateResult per surface. Coupled: one total.
+    extractions = if r.coupled_extraction !== nothing
+        [r.coupled_extraction]
+    else
+        r.per_surface_extraction
+    end
+
+    _write_ragged_complex!(diag, "valid_roots",
+        [gr.valid_roots for gr in extractions])
+    _write_ragged_complex!(diag, "poles",
+        [gr.poles for gr in extractions])
+    _write_ragged_complex!(diag, "filtered_roots",
+        [gr.filtered_roots for gr in extractions])
+    return nothing
+end
+
+# Write a ragged vector-of-vectors of ComplexF64 as (flat_re, flat_im,
+# offsets) — `offsets[k+1] - offsets[k]` is the length of row `k`. This
+# avoids HDF5 VLEN types, which have patchy cross-language support.
+function _write_ragged_complex!(parent, name::String,
+    data::Vector{Vector{ComplexF64}})
+    g = create_group(parent, name)
+    flat_re = Float64[]
+    flat_im = Float64[]
+    offsets = Int[0]
+    for v in data
+        append!(flat_re, real.(v))
+        append!(flat_im, imag.(v))
+        push!(offsets, offsets[end] + length(v))
+    end
+    g["flat_real"] = flat_re
+    g["flat_imag"] = flat_im
+    g["offsets"] = offsets
+    return nothing
+end
+
+# ---------- full scan data (optional) ----------
+function _write_scan_data!(g, r::SLAYERResult)
+    sc = create_group(g, "scan")
+    for (k, data) in enumerate(r.scan_data)
+        sk = create_group(sc, "surface_$(k)")
+        _write_single_scan!(sk, data)
+    end
+    return nothing
+end
+
+function _write_single_scan!(g, data::ScanResult)
+    g["kind"] = "brute_force"
+    g["Q_real"] = real.(data.Q)
+    g["Q_imag"] = imag.(data.Q)
+    g["Delta_real"] = real.(data.Δ)
+    g["Delta_imag"] = imag.(data.Δ)
+    g["re_axis"] = data.re_axis
+    g["im_axis"] = data.im_axis
+    return nothing
+end
+
+function _write_single_scan!(g, data::AMRResult)
+    g["kind"] = "amr"
+    g["Q_real"] = real.(data.Q)
+    g["Q_imag"] = imag.(data.Q)
+    g["Delta_real"] = real.(data.Δ)
+    g["Delta_imag"] = imag.(data.Δ)
+    g["n_cells"] = length(data.cells)
+    g["truncated"] = Int(data.truncated)
+    return nothing
+end
diff --git a/src/Tearing/Runner/Result.jl b/src/Tearing/Runner/Result.jl
new file mode 100644
index 000000000..f5a57e88c
--- /dev/null
+++ b/src/Tearing/Runner/Result.jl
@@ -0,0 +1,59 @@
+# Result.jl
+#
+# `SLAYERResult` packages the output of a full SLAYER analysis run:
+# per-surface layer parameters, the extracted tearing eigenvalues, and (if
+# `control.store_scan`) the full Q-plane scan data for plotting.
+
+"""
+    SLAYERResult
+
+Output of `run_slayer`. Carries both summary eigenvalues (ω_Hz, γ_Hz) and
+full diagnostic detail (valid roots, poles, filtered roots, contours) for
+downstream inspection and HDF5 output.
+
+# Fields
+
+  - `enabled`             -- `true` only when the analysis actually ran
+  - `control`             -- the `SLAYERControl` used (frozen snapshot)
+  - `params`              -- `Vector{SLAYERParameters}`, one per surface
+  - `dp_matrix`           -- outer-region Δ' matrix used in the analysis
+  - `Q_root`              -- tearing eigenvalue(s) in normalized Q
+    * length `nsurfaces` in `:uncoupled` mode
+    * length `1` in `:coupled` mode (global eigenvalue normalized by
+      `params[1].tauk`)
+  - `omega_Hz`, `gamma_Hz` -- physical rotation frequency / growth rate
+  - `per_surface_extraction` -- `Vector{GrowthRateResult}` of length
+    `nsurfaces` in uncoupled mode (each includes polelines, pole list,
+    valid roots, filtered roots). Empty in coupled mode.
+  - `coupled_extraction`  -- single `GrowthRateResult` in coupled mode.
+    `nothing` otherwise.
+  - `layer_widths`        -- `Vector{LayerWidths}`, one per surface: the
+    resistive layer thickness (in meters) from the `del_s` Riccati solve
+    plus FKR / visco-resistive sanity scales. Empty when disabled.
+  - `scan_data`           -- scan results (per-surface in uncoupled, single
+    entry in coupled). Empty unless `control.store_scan == true`.
+"""
+struct SLAYERResult
+    enabled::Bool
+    control::SLAYERControl
+    params::AbstractVector{<:InnerLayerParameters}
+    dp_matrix::Matrix{ComplexF64}
+    Q_root::Vector{ComplexF64}
+    omega_Hz::Vector{Float64}
+    gamma_Hz::Vector{Float64}
+    per_surface_extraction::Vector{GrowthRateResult}
+    coupled_extraction::Union{Nothing,GrowthRateResult}
+    layer_widths::Vector{LayerWidths}
+    scan_data::Vector{Union{ScanResult,AMRResult}}
+end
+
+# Empty result (enabled=false path)
+function empty_slayer_result(control::SLAYERControl)
+    return SLAYERResult(false, control,
+                        SLAYERParameters[],
+                        zeros(ComplexF64, 0, 0),
+                        ComplexF64[], Float64[], Float64[],
+                        GrowthRateResult[], nothing,
+                        LayerWidths[],
+                        Union{ScanResult,AMRResult}[])
+end
diff --git a/src/Tearing/Runner/Runner.jl b/src/Tearing/Runner/Runner.jl
new file mode 100644
index 000000000..7ff036e7c
--- /dev/null
+++ b/src/Tearing/Runner/Runner.jl
@@ -0,0 +1,57 @@
+# Runner.jl
+#
+# Top-level orchestration module that ties together the building blocks
+# from InnerLayer, Dispersion, and Utilities into the user-facing SLAYER
+# tearing-mode analysis pipeline.
+#
+#   gpec.toml  [SLAYER]  →  SLAYERControl
+#                            │
+#   equilibrium + Δ'         │
+#          +  profiles   →   build_slayer_inputs   →   SLAYERParameters[]
+#                            │
+#                            ▼
+#              SurfaceCoupling[] / MultiSurfaceCoupling
+#                            │
+#                            ▼
+#               brute_force_scan / amr_scan
+#                            │
+#                            ▼
+#                   find_growth_rates
+#                            │
+#                            ▼
+#                      SLAYERResult  →  HDF5 (`slayer/` group)
+
+module Runner
+
+using LinearAlgebra
+using Statistics: mean, median
+using HDF5
+
+using FastInterpolations: cubic_interp
+using ..Utilities
+using ..Utilities: KineticProfiles
+using ...Equilibrium: read_kinetic_file, KineticProfileData
+using ..InnerLayer
+using ..InnerLayer: InnerLayerParameters, InnerLayerResponse, solve_inner,
+    SLAYERModel, SLAYERParameters, build_slayer_inputs,
+    GGJModel, GGJParameters, build_ggj_inputs,
+    LayerWidths, slayer_layer_thickness
+using ..Dispersion
+using ..Dispersion: SurfaceCoupling, surface_coupling,
+    MultiSurfaceCoupling, multi_surface_coupling,
+    ScanResult, brute_force_scan,
+    AMRResult, amr_scan,
+    MultiBoxAMRResult, multi_box_amr_scan, as_amr_result,
+    GrowthRateResult, find_growth_rates
+
+include("Control.jl")
+include("Result.jl")
+include("run_slayer.jl")
+include("HDF5Output.jl")
+
+export SLAYERControl, slayer_control_from_toml, validate
+export SLAYERResult, empty_slayer_result
+export run_slayer, run_slayer_from_inputs, ggj_inner_deltas
+export write_slayer_hdf5!
+
+end # module Runner
diff --git a/src/Tearing/Runner/run_slayer.jl b/src/Tearing/Runner/run_slayer.jl
new file mode 100644
index 000000000..0e254e625
--- /dev/null
+++ b/src/Tearing/Runner/run_slayer.jl
@@ -0,0 +1,435 @@
+# Runner.jl
+#
+# Top-level orchestration for the SLAYER tearing-mode analysis. Given a
+# fully-solved `PlasmaEquilibrium` + `ForceFreeStatesInternal` (which
+# supplies the rational-surface list and the outer-region Δ' matrix) + a
+# populated `SLAYERControl`, `run_slayer` loads kinetic profiles, builds
+# per-surface SLAYER parameters, runs the requested scan mode, extracts
+# growth rates by contour intersection, and returns a `SLAYERResult`.
+#
+# A secondary entry point `run_slayer_from_inputs` takes pre-built
+# per-surface parameters + a Δ' matrix and bypasses the
+# equilibrium-driven `build_slayer_inputs` step. This is what the test
+# suite drives; it keeps the end-to-end code covered without requiring a
+# full equilibrium solve in every test.
+
+# ---------------------------------------------------------------------
+# Profile loading
+# ---------------------------------------------------------------------
+# Read kinetic profiles through the shared `Equilibrium.read_kinetic_file`
+# reader and adapt them to the SLAYER inner layer. Returns the spline-based
+# `KineticProfiles` plus χ⊥(ψ)/χ_φ(ψ) callables built from the file's
+# `chi_e`/`chi_phi` (or `nothing` when the file carries no usable χ, in which
+# case the caller falls back to the scalar `control.chi_perp`/`chi_tor`).
+function _load_profiles(control::SLAYERControl, dir_path::AbstractString)
+    isempty(control.profile_file) &&
+        error("run_slayer: [SLAYER] profile_file is empty — point it at a " *
+              "kinetic-profile file (HDF5 GPEC kinetic schema or ASCII table).")
+    path = isabspath(control.profile_file) ? control.profile_file :
+           joinpath(dir_path, control.profile_file)
+    data = read_kinetic_file(path; group=control.profile_group)
+
+    for (name, v) in (("n_e", data.n_e), ("T_e", data.T_e), ("T_i", data.T_i))
+        v === nothing &&
+            error("run_slayer: kinetic file '$path' is missing required " *
+                  "dataset '$name' for the SLAYER inner layer.")
+    end
+    # ω_*e/ω_*i are recomputed per-surface from equilibrium gradients
+    # (compute_omega_star), so the diamagnetic-frequency inputs here are
+    # placeholders; `omega` carries the ExB rotation when present.
+    npsi = length(data.psi)
+    omega = data.omega_E === nothing ? zeros(npsi) : data.omega_E
+    profiles = KineticProfiles(; psi=data.psi, n_e=data.n_e, T_e=data.T_e,
+        T_i=data.T_i, omega=omega,
+        omega_e=zeros(npsi), omega_i=zeros(npsi))
+
+    # χ⊥(ψ)/χ_φ(ψ) splines from the file. A χ array that is absent OR all-zero
+    # is treated as "not provided" — χ must be positive (χ=0 ⇒ τ_⊥→∞), and the
+    # all-zero sentinel lets a file keep the chi_e/chi_phi keys while deferring
+    # to the scalar control.chi_perp/chi_tor fallback. Build the spline only
+    # from a usable (present, not all-zero) array.
+    psi_xs = collect(Float64, data.psi)
+    _chi_spline(v) = (v === nothing || all(iszero, v)) ? nothing :
+                     cubic_interp(psi_xs, collect(Float64, v))
+    chi_perp = _chi_spline(data.chi_e)
+    chi_tor = _chi_spline(data.chi_phi)
+    return (profiles=profiles, chi_perp=chi_perp, chi_tor=chi_tor)
+end
+
+# ---------------------------------------------------------------------
+# Inner-layer model factory
+# ---------------------------------------------------------------------
+function _build_inner_model(name::Symbol)
+    if name === :slayer_fitzpatrick
+        return SLAYERModel(; variant=:fitzpatrick)
+    elseif name === :ggj_shooting
+        return GGJModel(; solver=:shooting)
+    elseif name === :ggj_galerkin
+        return GGJModel(; solver=:galerkin)
+    end
+    throw(ArgumentError("_build_inner_model: unknown model $name"))
+end
+
+# Map the TOML resistivity_model symbol to a NeoResistivityModel instance.
+function _build_resistivity_model(name::Symbol)
+    name === :sauter && return SauterNeoModel()
+    name === :redl && return RedlNeoModel()
+    name === :spitzer && return SpitzerModel()
+    name === :spitzer_harm && return SpitzerHarmModel()
+    throw(ArgumentError("_build_resistivity_model: unknown model $name"))
+end
+
+# Human-readable surface label for warnings. SLAYER params carry (m, n);
+# GGJ params carry only the singular-surface index `ising`.
+_surface_label(p::SLAYERParameters) = "m=$(p.m), n=$(p.n)"
+_surface_label(p::InnerLayerParameters) = "ising=$(getfield(p, :ising))"
+
+# Is this an unvalidated GGJ inner-layer model? Used to gate the γ-extraction
+# future-work warning.
+_is_ggj(::GGJModel) = true
+_is_ggj(::Any) = false
+
+# ---------------------------------------------------------------------
+# Scan dispatch
+# ---------------------------------------------------------------------
+function _run_scan(f, control::SLAYERControl)
+    if control.scan_mode === :brute_force
+        return brute_force_scan(f, control.Q_re_range, control.Q_im_range;
+            nre=control.nre, nim=control.nim)
+    elseif control.scan_mode === :amr
+        if !isempty(control.boxes)
+            # Multi-box stripe layout. Pole magnitude threshold for the
+            # activity check is derived from a coarse 16×6 sample of the
+            # union of all boxes, using 10 × median(|Δ|) (median is robust
+            # to near-pole sample inflation; see the adaptive threshold note
+            # below).
+            ω_lo = minimum(b[1] for b in control.boxes)
+            ω_hi = maximum(b[2] for b in control.boxes)
+            γ_lo = minimum(b[3] for b in control.boxes)
+            γ_hi = maximum(b[4] for b in control.boxes)
+            coarse_pts = ComplexF64[ComplexF64(ω, γ)
+                                    for ω in range(ω_lo, ω_hi; length=16)
+                                    for γ in range(γ_lo, γ_hi; length=6)]
+            coarse_Δ = ComplexF64[ComplexF64(f(q)) for q in coarse_pts]
+            finite = filter(z -> isfinite(z) && abs(z) < 1e30, coarse_Δ)
+            pole_thr = isempty(finite) ? 1e8 : 10.0 * median(abs.(finite))
+            # Convert NTuple{4,Float64} → ((ω_lo,ω_hi),(γ_lo,γ_hi)) tuples
+            boxes_in = [((b[1], b[2]), (b[3], b[4])) for b in control.boxes]
+            return multi_box_amr_scan(f, boxes_in;
+                pole_magnitude_threshold=pole_thr,
+                prescreen_nre=control.multi_box_prescreen_n,
+                prescreen_nim=control.multi_box_prescreen_n,
+                nre0=control.nre, nim0=control.nim,
+                passes=control.amr_passes,
+                max_cells=control.amr_max_cells,
+                max_cells_action=:warn_truncate) |>
+                   as_amr_result        # downstream expects AMRResult
+        end
+        return amr_scan(f, control.Q_re_range, control.Q_im_range;
+            nre0=control.nre, nim0=control.nim,
+            passes=control.amr_passes,
+            max_cells=control.amr_max_cells)
+    end
+    throw(ArgumentError("_run_scan: unknown scan_mode=$(control.scan_mode)"))
+end
+
+# ---------------------------------------------------------------------
+# Surface-coupling builder — dispatches on model type to thread the
+# correct `scale` and `tauk` through the Dispersion API.
+# ---------------------------------------------------------------------
+# SLAYER: scale = lu^(1/3), tauk from the surface, dc from the χ‖ proxy.
+function _build_surface_coupling(model::SLAYERModel, params::SLAYERParameters,
+    dp_diag)
+    return surface_coupling(model, params, dp_diag; dc=params.dc_tmp)
+end
+
+# GGJ: scale = 1.0 (rescale_delta applied inside solve_inner), tauk = 1.0,
+# dc = 0 (the 4m×4m Pletzer-Dewar residual carries interchange stabilization
+# natively). See the GGJ `surface_coupling` method.
+function _build_surface_coupling(model::GGJModel, params::GGJParameters,
+    dp_diag)
+    return surface_coupling(model, params, dp_diag)
+end
+
+# ---------------------------------------------------------------------
+# Core analysis entry point that takes pre-built parameters.
+# ---------------------------------------------------------------------
+"""
+    run_slayer_from_inputs(params::Vector{SLAYERParameters},
+                            dp_matrix::AbstractMatrix,
+                            control::SLAYERControl) -> SLAYERResult
+
+Run the SLAYER tearing analysis given pre-built per-surface
+`SLAYERParameters` and the outer-region Δ' matrix. Bypasses the
+equilibrium-driven `build_slayer_inputs` step — use this when the
+parameters are already known (e.g. in unit tests or when rebuilding
+from cached HDF5 output).
+"""
+function run_slayer_from_inputs(params::AbstractVector{<:InnerLayerParameters},
+    dp_matrix::AbstractMatrix,
+    control::SLAYERControl)
+    validate(control)
+    control.enabled || return empty_slayer_result(control)
+    isempty(params) && return empty_slayer_result(control)
+
+    n = length(params)
+    size(dp_matrix) == (n, n) ||
+        throw(ArgumentError("run_slayer: dp_matrix size $(size(dp_matrix)) " *
+                            "≠ ($n, $n)"))
+    dp = Matrix{ComplexF64}(dp_matrix)
+
+    model = _build_inner_model(control.inner_model)
+
+    # Guard: the inner-layer model and the parameter eltype must match, or
+    # `_build_surface_coupling` throws an opaque MethodError downstream.
+    expected_P = _is_ggj(model) ? GGJParameters : SLAYERParameters
+    all(p -> p isa expected_P, params) ||
+        throw(
+            ArgumentError(
+                "run_slayer: inner_model=$(control.inner_model) requires " *
+                "$(expected_P) per-surface parameters, but got eltype " *
+                "$(eltype(params)). Build inputs with the matching builder " *
+                "(build_slayer_inputs for SLAYER, build_ggj_inputs for GGJ).")
+        )
+
+    # The coupled determinant uses the reduced m×m (tearing-only) form, which
+    # drops the interchange channel. For GGJ that channel carries the Glasser
+    # interchange stabilization, so coupled-GGJ results omit real physics.
+    if _is_ggj(model) && control.coupling_mode === :coupled
+        @warn(
+            "SLAYER: coupling_mode=:coupled with a GGJ inner model uses the " *
+            "reduced m×m tearing-only determinant, which DROPS the GGJ " *
+            "interchange (Glasser stabilization) channel — results are " *
+            "physically incomplete. Use the full Pletzer-Dewar matching " *
+            "(multi_surface_coupling_full) for coupled GGJ studies."
+        )
+    end
+
+    # GGJ growth-rate extraction by Re/Im contour matching is not yet
+    # reliable (the contours do not robustly intersect at a dispersion-relation
+    # zero). The scan still runs so the user can inspect/plot the contours and
+    # access both parity Δ channels via `ggj_inner_deltas`, but the reported γ
+    # is unvalidated and should be treated as future work.
+    if _is_ggj(model)
+        @warn(
+            "SLAYER: GGJ γ-extraction by Re/Im contour matching is " *
+            "FUTURE WORK — contours do not reliably intersect at a " *
+            "dispersion-relation root. The scan grid and parity Δ channels " *
+            "are provided for inspection; reported growth rates are " *
+            "unvalidated placeholders."
+        )
+    end
+
+    # Per-surface SurfaceCoupling objects
+    scs = [_build_surface_coupling(model, params[k], dp[k, k]) for k in 1:n]
+
+    # Per-surface resistive layer thickness [m] via the del_s Riccati solve.
+    # Independent of the dispersion scan / coupling mode — a pure diagnostic.
+    # SLAYER-only: the del_s Riccati is defined on SLAYERParameters. GGJ
+    # surfaces carry no analogous layer-thickness diagnostic, so leave it empty.
+    layer_widths = eltype(params) <: SLAYERParameters ?
+                   LayerWidths[slayer_layer_thickness(params[k]) for k in 1:n] :
+                   LayerWidths[]
+
+    Q_root = ComplexF64[]
+    omega_Hz = Float64[]
+    gamma_Hz = Float64[]
+    per_surface_extraction = GrowthRateResult[]
+    coupled_extraction = nothing
+    scan_data_list = Union{ScanResult,AMRResult}[]
+
+    # Helper: compute the pole_threshold actually passed to find_growth_rates.
+    # When `control.pole_threshold_adaptive` is true, override with
+    # `10 × median(|Δ|)` over the scan's dispersion residual array.
+    #
+    # The median formulation is robust against pre-screen samples landing
+    # near a pole. A single near-pole sample inflates `|mean(Δ)|` by orders
+    # of magnitude (and `|mean|` further collapses on oscillating residuals
+    # whose phases cancel in the complex sum). 10 × median(|Δ|) reflects
+    # "10× the typical residual magnitude" with median robust to both
+    # pathologies.
+    function _pole_threshold_for(scan)
+        control.pole_threshold_adaptive || return control.pole_threshold
+        # ScanResult and AMRResult both carry `.Δ` — abstract over both
+        Δ_arr = hasproperty(scan, :Δ) ? scan.Δ : nothing
+        Δ_arr === nothing && return control.pole_threshold
+        finite = filter(z -> isfinite(z) && abs(z) < 1e30, Δ_arr)
+        isempty(finite) && return control.pole_threshold
+        return 10.0 * median(abs.(finite))
+    end
+
+    if control.coupling_mode === :uncoupled
+        for (k, sc) in enumerate(scs)
+            scan = _run_scan(sc, control)
+            pthr = _pole_threshold_for(scan)
+            gr = find_growth_rates(scan, sc.tauk;
+                pole_threshold=pthr,
+                filter_above_poles=control.filter_above_poles,
+                filter_outside_re=control.filter_outside_re,
+                gap_kHz_threshold=control.gap_kHz_threshold,
+                residual=control.polish_roots ? sc : nothing,
+                validity_rtol=control.validity_rtol)
+            :no_root in gr.warning_flags && @warn(
+                "SLAYER: no usable growth-rate root found for surface " *
+                "$(_surface_label(params[k])); reported γ=0 is a " *
+                "placeholder, not a physical result — check scan grid / " *
+                "pole_threshold.")
+            push!(Q_root, gr.Q_root)
+            push!(omega_Hz, gr.omega_Hz)
+            push!(gamma_Hz, gr.gamma_Hz)
+            push!(per_surface_extraction, gr)
+            control.store_scan && push!(scan_data_list, scan)
+        end
+
+    elseif control.coupling_mode === :coupled
+        m_use = min(control.msing_max, n)
+        mc = multi_surface_coupling(scs, dp; ref_idx=1, msing_max=m_use)
+        scan = _run_scan(mc, control)
+        pthr = _pole_threshold_for(scan)
+        ref_tauk = scs[1].tauk
+        # Coupled path: no root polishing / validity gate. The m×m coupled
+        # determinant det(D'−D(Q)) is ill-conditioned (its magnitude floors well
+        # above zero), so |det|-based polishing is a no-op and the residual-scale
+        # gate is unreliable. A σ_min-based coupled refinement is a dev follow-up;
+        # for now the coupled determinant uses the raw contour extraction (the
+        # BLAS pin in the scan still makes it thread-deterministic).
+        gr = find_growth_rates(scan, ref_tauk;
+            pole_threshold=pthr,
+            filter_above_poles=control.filter_above_poles,
+            filter_outside_re=control.filter_outside_re,
+            gap_kHz_threshold=control.gap_kHz_threshold)
+        :no_root in gr.warning_flags && @warn(
+            "SLAYER: no usable growth-rate root found for the coupled " *
+            "$(m_use)-surface determinant; reported γ=0 is a placeholder, " *
+            "not a physical result — check scan grid / pole_threshold.")
+        push!(Q_root, gr.Q_root)
+        push!(omega_Hz, gr.omega_Hz)
+        push!(gamma_Hz, gr.gamma_Hz)
+        coupled_extraction = gr
+        control.store_scan && push!(scan_data_list, scan)
+    end
+
+    return SLAYERResult(true, control, params, dp,
+        Q_root, omega_Hz, gamma_Hz,
+        per_surface_extraction, coupled_extraction,
+        layer_widths, scan_data_list)
+end
+
+# ---------------------------------------------------------------------
+# GGJ parity-Δ diagnostic
+# ---------------------------------------------------------------------
+"""
+    ggj_inner_deltas(params::AbstractVector{GGJParameters}, Q::Number;
+                     solver=:galerkin) -> Vector{NamedTuple}
+
+Evaluate both parity channels of the GGJ inner-layer matching data at the
+complex normalized growth rate `Q` for each rational surface. Returns a
+vector of `(ising, tearing, interchange)` named tuples, where `tearing`
+(GWP Δ_+) is the reconnecting channel and `interchange` (GWP Δ_−) the
+non-reconnecting Glasser-stabilization channel (see `InnerLayerResponse`).
+
+This is the supported GGJ diagnostic: both parity Δ's are physical and
+directly accessible here. Contour-matching γ extraction from these (the
+Re/Im scan intersection) is not yet validated — see the warning in
+`run_slayer_from_inputs`.
+"""
+function ggj_inner_deltas(params::AbstractVector{GGJParameters}, Q::Number;
+    solver::Symbol=:galerkin)
+    model = GGJModel(; solver=solver)
+    out = Vector{NamedTuple{(:ising, :tearing, :interchange),
+        Tuple{Int,ComplexF64,ComplexF64}}}(undef, length(params))
+    for (k, p) in enumerate(params)
+        r = solve_inner(model, p, ComplexF64(Q))
+        out[k] = (ising=p.ising, tearing=r.tearing, interchange=r.interchange)
+    end
+    return out
+end
+
+# ---------------------------------------------------------------------
+# Full pipeline: equilibrium + ForceFreeStates → parameters → analysis
+# ---------------------------------------------------------------------
+"""
+    run_slayer(equil, ffs_intr, control; dir_path="./") -> SLAYERResult
+
+Orchestrate the full SLAYER analysis against a solved
+`PlasmaEquilibrium` and `ForceFreeStatesInternal`. Kinetic profiles are
+read from `control.profile_file` (relative to `dir_path`) through the shared
+`Equilibrium.read_kinetic_file` reader; when the file carries `chi_e`/`chi_phi`
+profiles they set χ⊥(ψ)/χ_φ(ψ), otherwise the scalar `control.chi_perp`/
+`chi_tor` fallbacks are used. Per-surface parameters are built via
+`build_slayer_inputs`; the outer-region Δ' matrix is pulled from
+`ffs_intr.delta_prime_matrix` (or, if empty, from the diagonal
+`sing.delta_prime` entries).
+
+Returns an `enabled=false` `SLAYERResult` when `control.enabled` is
+false.
+"""
+function run_slayer(equil, ffs_intr, control::SLAYERControl;
+    dir_path::AbstractString="./")
+    validate(control)
+    control.enabled || return empty_slayer_result(control)
+    isempty(ffs_intr.sing) && return empty_slayer_result(control)
+
+    loaded = _load_profiles(control, dir_path)
+    profiles = loaded.profiles
+
+    if control.inner_model in (:ggj_shooting, :ggj_galerkin)
+        # GGJ γ-extraction is future work; `run_slayer_from_inputs` emits the
+        # warning once the model is built (so direct callers see it too).
+        params = build_ggj_inputs(equil, ffs_intr.sing, profiles;
+            mu_i=control.mu_i,
+            zeff=control.zeff,
+            resistivity_model=_build_resistivity_model(control.resistivity_model),
+            lnLambda_form=control.lnLambda_form)
+    else
+        bt = control.bt === nothing ? equil.config.b0exp : control.bt
+        # χ⊥/χ_φ from the kinetic file when present, else the scalar fallbacks.
+        chi_perp = loaded.chi_perp === nothing ? control.chi_perp : loaded.chi_perp
+        chi_tor = loaded.chi_tor === nothing ? control.chi_tor : loaded.chi_tor
+        (loaded.chi_perp === nothing || loaded.chi_tor === nothing) && @warn(
+            "SLAYER: kinetic file has no usable chi_e/chi_phi profile(s) " *
+            "(dataset absent or all-zero); using the scalar " *
+            "control.chi_perp/chi_tor fallback for the missing one(s).")
+        params = build_slayer_inputs(equil, ffs_intr.sing, profiles;
+            bt=bt,
+            mu_i=control.mu_i,
+            zeff=control.zeff,
+            chi_perp=chi_perp,
+            chi_tor=chi_tor,
+            dr_val=control.dr_val,
+            dgeo_val=control.dgeo_val,
+            dc_type=control.dc_type,
+            theta=control.theta_sample,
+            resistivity_model=_build_resistivity_model(control.resistivity_model),
+            lnLambda_form=control.lnLambda_form)
+    end
+
+    # Δ' matrix: prefer the full parallel-FM matrix; fall back to a
+    # diagonal built from each SingType's scalar delta_prime.
+    dp = if !isempty(ffs_intr.delta_prime_matrix) &&
+       size(ffs_intr.delta_prime_matrix) == (length(params), length(params))
+        Matrix{ComplexF64}(ffs_intr.delta_prime_matrix)
+    else
+        # The full Δ' matrix is unavailable (e.g. the parallel-FM stage that
+        # populates it was not run). The scalar-diagonal fallback uses
+        # `sing.delta_prime`, which is a coarse per-surface stub; surfaces
+        # with no entry default to Δ'=0, giving γ computed from zero drive.
+        n_missing = count(s -> isempty(s.delta_prime), ffs_intr.sing)
+        @warn(
+            "SLAYER: ffs_intr.delta_prime_matrix is empty or wrong-sized " *
+            "($(size(ffs_intr.delta_prime_matrix)) vs " *
+            "($(length(params)),$(length(params)))); falling back to the " *
+            "diagonal `sing.delta_prime` stub. Growth rates use a coarse " *
+            "per-surface Δ' and may be unreliable" *
+            (n_missing > 0 ? "; $n_missing surface(s) have NO Δ' entry and " *
+                             "default to Δ'=0 (zero tearing drive)." : ".")
+        )
+        M = zeros(ComplexF64, length(params), length(params))
+        for (k, s) in enumerate(ffs_intr.sing)
+            M[k, k] = isempty(s.delta_prime) ? 0.0 + 0im : s.delta_prime[1]
+        end
+        M
+    end
+
+    return run_slayer_from_inputs(params, dp, control)
+end
diff --git a/src/Tearing/Tearing.jl b/src/Tearing/Tearing.jl
new file mode 100644
index 000000000..2e096846b
--- /dev/null
+++ b/src/Tearing/Tearing.jl
@@ -0,0 +1,31 @@
+# Tearing.jl
+#
+# Umbrella module grouping the tearing-mode analysis stack into a single
+# layered hierarchy:
+#
+#   InnerLayer  -- pure physics: Δ_inner(Q) for GGJ or SLAYER models
+#   Dispersion  -- physics-agnostic scan + contour-intersection root
+#                  extraction (consumes any InnerLayerModel)
+#   Runner      -- user-facing orchestration: TOML config, profile
+#                  loading, HDF5 output, workflow hooks
+#
+# Relative-import dot counts inside this umbrella are simplified by
+# re-binding `Utilities` at the Tearing level: all submodules reach
+# Utilities via `..Utilities` (or `...Utilities` from sub-sub-modules)
+# regardless of their depth in the original layout.
+
+module Tearing
+
+using ..Utilities
+
+include("InnerLayer/InnerLayer.jl")
+include("Dispersion/Dispersion.jl")
+include("Runner/Runner.jl")
+
+import .InnerLayer as InnerLayer
+import .Dispersion as Dispersion
+import .Runner as Runner
+
+export InnerLayer, Dispersion, Runner
+
+end # module Tearing
diff --git a/src/Utilities/KineticProfiles.jl b/src/Utilities/KineticProfiles.jl
new file mode 100644
index 000000000..70f1a2974
--- /dev/null
+++ b/src/Utilities/KineticProfiles.jl
@@ -0,0 +1,81 @@
+# KineticProfiles.jl
+#
+# Radial kinetic-profile container shared across GPEC modules that need
+# electron density, electron/ion temperatures, and the three frequencies
+# (toroidal rotation + electron/ion diamagnetic) as functions of the
+# normalized poloidal flux ψ. SLAYER is the first consumer; PENTRC and
+# future resistive-MHD modules will share this object.
+
+using FastInterpolations
+
+"""
+    KineticProfiles
+
+Radial kinetic-profile container. All six profiles are 1D cubic splines of
+the normalized poloidal flux ψ ∈ [0, 1].
+
+| field     | meaning                                 | units |
+|:--------- |:--------------------------------------- |:----- |
+| `n_e`     | electron density                        | m⁻³   |
+| `T_e`     | electron temperature                    | eV    |
+| `T_i`     | ion temperature                         | eV    |
+| `omega`   | toroidal rotation                       | rad/s |
+| `omega_e` | electron diamagnetic frequency ω\\_\\*e | rad/s |
+| `omega_i` | ion diamagnetic frequency ω\\_\\*i      | rad/s |
+
+Construct via the keyword constructor `KineticProfiles(; psi, n_e, T_e, T_i, omega, omega_e, omega_i)` with matched-length vectors. The SLAYER
+runner builds this object from a standardized kinetic-profile file via
+`Equilibrium.read_kinetic_file`.
+
+Evaluate all profiles at a given ψ via the call operator:
+
+```julia
+vals = kp(0.5)    # NamedTuple(n_e=..., T_e=..., ..., omega_i=...)
+```
+"""
+struct KineticProfiles{S}
+    n_e::S
+    T_e::S
+    T_i::S
+    omega::S
+    omega_e::S
+    omega_i::S
+end
+
+function KineticProfiles(; psi::AbstractVector{<:Real},
+    n_e::AbstractVector{<:Real},
+    T_e::AbstractVector{<:Real},
+    T_i::AbstractVector{<:Real},
+    omega::AbstractVector{<:Real},
+    omega_e::AbstractVector{<:Real},
+    omega_i::AbstractVector{<:Real})
+    xs = collect(Float64.(psi))
+    for (name, v) in (("n_e", n_e), ("T_e", T_e), ("T_i", T_i),
+        ("omega", omega), ("omega_e", omega_e),
+        ("omega_i", omega_i))
+        length(v) == length(xs) ||
+            throw(ArgumentError("KineticProfiles: length($name) = $(length(v)) " *
+                                "≠ length(psi) = $(length(xs))"))
+    end
+    return KineticProfiles(cubic_interp(xs, Float64.(n_e)),
+        cubic_interp(xs, Float64.(T_e)),
+        cubic_interp(xs, Float64.(T_i)),
+        cubic_interp(xs, Float64.(omega)),
+        cubic_interp(xs, Float64.(omega_e)),
+        cubic_interp(xs, Float64.(omega_i)))
+end
+
+"""
+    (kp::KineticProfiles)(psi::Real) -> NamedTuple
+
+Evaluate all profiles at `psi` and return them as a NamedTuple with fields
+`(n_e, T_e, T_i, omega, omega_e, omega_i)`.
+"""
+(kp::KineticProfiles)(psi::Real) = (
+    n_e=kp.n_e(psi),
+    T_e=kp.T_e(psi),
+    T_i=kp.T_i(psi),
+    omega=kp.omega(psi),
+    omega_e=kp.omega_e(psi),
+    omega_i=kp.omega_i(psi)
+)
diff --git a/src/Utilities/NeoclassicalResistivity.jl b/src/Utilities/NeoclassicalResistivity.jl
new file mode 100644
index 000000000..de256090d
--- /dev/null
+++ b/src/Utilities/NeoclassicalResistivity.jl
@@ -0,0 +1,339 @@
+# NeoclassicalResistivity.jl
+#
+# Shared neoclassical-resistivity utilities used by both the GGJ and
+# SLAYER inner-layer models. All formulas follow Sauter, Angioni & Lin-Liu
+# Phys. Plasmas 6, 2834 (1999) and its errata, with an optional Redl et al.
+# Phys. Plasmas 28, 022502 (2021) variant that improves the fit at high
+# collisionality.
+#
+# Two external references were cross-checked during implementation:
+#   - OpenFUSIONToolkit `TokaMaker/bootstrap.py`  (Redl 2021 path)
+#   - OMFIT `omfit_classes/utils_fusion.py::nclass_conductivity-style
+#     block` around lines 1255-1319 (Sauter 1999 and `neo_2021` paths)
+#
+# Formula provenance:
+#   - eq 18a (Spitzer):       Sauter et al. 1999, Eq. (18a)
+#   - eq 18b (nu*_e):         Sauter et al. 1999, Eq. (18b)
+#   - eq 13 (F_33 Sauter):    Sauter et al. 1999, Eqs. (13a)-(13b)
+#   - eq 17 (F_33 Redl):      Redl et al. 2021, Eqs. (17)-(18)
+#   - f_t (Lin-Liu & Miller): Phys. Plasmas 2, 1666 (1995), Eq. (6)
+#   - NRL Coulomb log:        NRL Plasma Formulary 2009
+
+"""
+    NeoclassicalResistivity
+
+Spitzer + Sauter / Redl neoclassical resistivity closures, shared between
+the GGJ and SLAYER inner-layer models so both see identical plasma-input
+physics when the same `NeoResistivityModel` is selected.
+
+# Exports
+
+| symbol                 | role                                                   |
+|:---------------------- |:------------------------------------------------------ |
+| `NeoResistivityModel`  | abstract tag                                           |
+| `SpitzerModel`         | plain Spitzer (no trapped-particle correction)         |
+| `SpitzerHarmModel`     | Fitzpatrick/TJ Spitzer-Härm σ_∥ (legacy SLAYER τ_R)    |
+| `SauterNeoModel`       | Sauter 1999 F_33 neoclassical correction               |
+| `RedlNeoModel`         | Redl 2021 F_33 neoclassical correction                 |
+| `coulomb_log_e`        | ln Λ_e (NRL or Sauter form)                            |
+| `eta_spitzer`          | Sauter 18a Spitzer resistivity [Ω·m]                   |
+| `tau_ee_spitzer_harm`  | electron-electron collision time τ_ee [s]              |
+| `eta_spitzer_harm`     | Fitzpatrick/TJ Spitzer-Härm resistivity 1/σ_∥ [Ω·m]    |
+| `trapped_fraction`     | Lin-Liu & Miller 1995 f_t from ⟨B⟩, ⟨B²⟩, B_min, B_max |
+| `trapped_fraction_eps` | simple ε-only f_t fallback                             |
+| `nu_star_e`            | Sauter 18b electron collisionality                     |
+| `eta_neoclassical`     | dispatched: Spitzer(-Härm) or F_33 · Spitzer           |
+"""
+module NeoclassicalResistivity
+
+using ..PhysicalConstants: EPS_0, M_E, E_CHG
+
+export NeoResistivityModel, SpitzerModel, SpitzerHarmModel, SauterNeoModel, RedlNeoModel
+export coulomb_log_e, eta_spitzer, tau_ee_spitzer_harm, eta_spitzer_harm
+export trapped_fraction, trapped_fraction_eps
+export nu_star_e, eta_neoclassical
+
+"""
+Abstract tag for a neoclassical-resistivity closure.
+"""
+abstract type NeoResistivityModel end
+
+"""
+Plain Spitzer resistivity — no trapped-particle correction.
+"""
+struct SpitzerModel <: NeoResistivityModel end
+
+"""
+Spitzer-Härm parallel resistivity 1/σ_∥ as used by Fitzpatrick's TJ
+(LayerParameters.tex Eqs. 7-8) and legacy SLAYER for τ_R. No
+trapped-particle correction. Pair with `lnLambda_form=:wesson` to
+reproduce legacy SLAYER behaviour bit-identically.
+"""
+struct SpitzerHarmModel <: NeoResistivityModel end
+
+"""
+Sauter, Angioni & Lin-Liu 1999 F_33 neoclassical correction (Eqs. 13a,b).
+"""
+struct SauterNeoModel <: NeoResistivityModel end
+
+"""
+Redl et al. 2021 F_33 neoclassical correction (Eqs. 17-18). Improved
+high-collisionality fit vs SauterNeoModel.
+"""
+struct RedlNeoModel <: NeoResistivityModel end
+
+# --------------------------------------------------------------------------
+# Coulomb logarithm
+# --------------------------------------------------------------------------
+
+"""
+    coulomb_log_e(n_e, T_e; form=:nrl) -> Float64
+
+Electron Coulomb logarithm. `n_e` in m⁻³, `T_e` in eV.
+
+`form=:nrl` (default) uses the NRL Plasma Formulary 2009 expression, which
+OpenFUSIONToolkit's `bootstrap.py` also selects as the "more accurate"
+option. `form=:sauter` uses the simpler Sauter 1999 Eq. 18d form.
+"""
+function coulomb_log_e(n_e::Real, T_e::Real; form::Symbol=:nrl)
+    n_e > 0 || throw(ArgumentError("coulomb_log_e: n_e must be > 0 (got $n_e)"))
+    T_e > 0 || throw(ArgumentError("coulomb_log_e: T_e must be > 0 (got $T_e)"))
+    if form === :nrl
+        # NRL 2009, n_e in cm⁻³; matches utils_fusion.py:1262-1264
+        return 23.5 - log(sqrt(n_e / 1e6) * T_e^(-1.25)) -
+               sqrt(1e-5 + (log(T_e) - 2)^2 / 16.0)
+    elseif form === :sauter
+        # Sauter 1999 Eq. 18d; matches utils_fusion.py:1255
+        return 31.3 - log(sqrt(n_e) / T_e)
+    elseif form === :wesson
+        # Legacy Wesson form used by previous Julia code & SLAYER
+        return 24.0 + 3.0 * log(10.0) - 0.5 * log(n_e) + log(T_e)
+    else
+        throw(ArgumentError("coulomb_log_e: unknown form=$form " *
+                            "(expected :nrl, :sauter, or :wesson)"))
+    end
+end
+
+# --------------------------------------------------------------------------
+# Spitzer resistivity (Sauter 1999 Eq. 18a)
+# --------------------------------------------------------------------------
+
+# Sauter 1999 Eq. 18a line 2 — Spitzer conductivity Zeff correction
+_N_Z(Z::Real) = 0.58 + 0.74 / (0.76 + Z)
+
+"""
+    eta_spitzer(n_e, T_e, Z_eff; lnLamb=nothing) -> Float64
+
+Spitzer resistivity in Ω·m, using the Sauter 1999 Eq. 18a form
+
+```
+σ_Sp = 1.9012e4 · T_e^1.5 / (Z_eff · N(Z_eff) · lnΛ_e)
+N(Z) = 0.58 + 0.74 / (0.76 + Z)
+η_Sp = 1 / σ_Sp
+```
+
+`n_e` [m⁻³], `T_e` [eV]. `lnLamb` defaults to `coulomb_log_e(n_e, T_e)` (NRL).
+"""
+function eta_spitzer(n_e::Real, T_e::Real, Z_eff::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    T_e > 0 || throw(ArgumentError("eta_spitzer: T_e must be > 0 (got $T_e)"))
+    Z_eff > 0 || throw(ArgumentError("eta_spitzer: Z_eff must be > 0 (got $Z_eff)"))
+    lnL = lnLamb === nothing ? coulomb_log_e(n_e, T_e) : Float64(lnLamb)
+    sigma_sp = 1.9012e4 * T_e^1.5 / (Z_eff * _N_Z(Z_eff) * lnL)
+    return 1.0 / sigma_sp
+end
+
+# --------------------------------------------------------------------------
+# Spitzer-Härm parallel resistivity (Fitzpatrick TJ LayerParameters.tex Eqs. 7-8)
+# --------------------------------------------------------------------------
+
+"""
+    tau_ee_spitzer_harm(n_e, T_e; lnLamb=nothing) -> Float64
+
+Electron-electron collision time per Fitzpatrick TJ LayerParameters.tex Eq. 7:
+
+```
+τ_ee = 6√2 π^1.5 ε₀² √m_e T_e^1.5 / (lnΛ e^2.5 n_e)
+```
+
+`n_e` [m⁻³], `T_e` [eV] (the `e^2.5` denominator absorbs the eV→J
+conversion). `lnLamb` defaults to `coulomb_log_e(n_e, T_e)` (NRL).
+"""
+function tau_ee_spitzer_harm(n_e::Real, T_e::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    n_e > 0 || throw(ArgumentError("tau_ee_spitzer_harm: n_e must be > 0"))
+    T_e > 0 || throw(ArgumentError("tau_ee_spitzer_harm: T_e must be > 0"))
+    lnL = lnLamb === nothing ? coulomb_log_e(n_e, T_e) : Float64(lnLamb)
+    return 6.0 * sqrt(2.0) * π^1.5 * EPS_0^2 * sqrt(M_E) * T_e^1.5 /
+           (lnL * E_CHG^2.5 * n_e)
+end
+
+"""
+    eta_spitzer_harm(n_e, T_e, Z_eff; lnLamb=nothing) -> Float64
+
+Spitzer-Härm parallel resistivity η_∥ = 1/σ_∥ in Ω·m, per Fitzpatrick TJ
+LayerParameters.tex Eq. 8:
+
+```
+σ_∥ = (√2 + 13 Z_eff/4) / (Z_eff (√2 + Z_eff)) · n_e e² τ_ee / m_e
+```
+
+This is the resistivity entering the legacy SLAYER / TJ resistive
+diffusion time τ_R = μ₀ r_s² σ_∥ (LayerParameters.tex Eq. 17). Agrees
+with `eta_spitzer` to the fit accuracy of the two formulas (~1% at Z=1).
+"""
+function eta_spitzer_harm(n_e::Real, T_e::Real, Z_eff::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    Z_eff > 0 || throw(ArgumentError("eta_spitzer_harm: Z_eff must be > 0"))
+    tau_ee = tau_ee_spitzer_harm(n_e, T_e; lnLamb=lnLamb)
+    sigma_par = (sqrt(2.0) + 13.0 * (Z_eff / 4.0)) /
+                (Z_eff * (sqrt(2.0) + Z_eff)) *
+                (n_e * E_CHG^2 * tau_ee) / M_E
+    return 1.0 / sigma_par
+end
+
+# --------------------------------------------------------------------------
+# Trapped fraction
+# --------------------------------------------------------------------------
+
+"""
+    trapped_fraction(avg_B, avg_Bsq, B_min, B_max) -> Float64
+
+Lin-Liu & Miller 1995, Phys. Plasmas **2**, 1666, Eq. (6):
+
+```
+f_t = 1 − ⟨B⟩² / ⟨B²⟩ · (1 − √(1 − h) · (1 + h/2)),   h = B_min / B_max
+```
+
+Equivalent to the OMFIT `f_t` / `f_c` pair at full geometric accuracy (uses
+both the average-B ratio and the min/max extremes). Arguments are
+flux-surface averages computed from the θ-loop in the equilibrium.
+"""
+function trapped_fraction(avg_B::Real, avg_Bsq::Real,
+    B_min::Real, B_max::Real)
+    B_max > 0 || throw(ArgumentError("trapped_fraction: B_max must be > 0"))
+    avg_Bsq > 0 || throw(ArgumentError("trapped_fraction: avg_Bsq must be > 0"))
+    h = clamp(B_min / B_max, 0.0, 1.0)
+    factor = 1.0 - sqrt(1.0 - h) * (1.0 + 0.5 * h)
+    ft = 1.0 - (avg_B^2 / avg_Bsq) * factor
+    return clamp(ft, 0.0, 1.0)
+end
+
+"""
+    trapped_fraction_eps(eps) -> Float64
+
+Simple ε-only trapped-fraction approximation (OMFIT `f_t`):
+
+```
+f_c ≈ (1 − ε)² / (√(1 − ε²) · (1 + 1.46·√ε + 0.2·ε))
+f_t = 1 − f_c
+```
+
+Used as a fallback when the full (⟨B⟩, ⟨B²⟩, B_min, B_max) moments are
+unavailable — e.g. when feeding SLAYER directly from minor-radius geometry
+without having evaluated `ResistGeometry` first.
+"""
+function trapped_fraction_eps(eps::Real)
+    e = clamp(eps, 0.0, 1.0 - 1e-12)
+    fc = (1.0 - e)^2 / (sqrt(1.0 - e^2) * (1.0 + 1.46 * sqrt(e) + 0.2 * e))
+    return clamp(1.0 - fc, 0.0, 1.0)
+end
+
+# --------------------------------------------------------------------------
+# Electron collisionality (Sauter 1999 Eq. 18b)
+# --------------------------------------------------------------------------
+
+"""
+    nu_star_e(n_e, T_e, R_major, eps, q, Z_eff; lnLamb=nothing) -> Float64
+
+Electron collisionality ν*_e per Sauter 1999 Eq. 18b:
+
+```
+ν*_e = 6.921e-18 · |q| · R · n_e · Z_eff · lnΛ_e / (T_e² · ε^1.5)
+```
+
+`n_e` [m⁻³], `T_e` [eV], `R_major` [m]. Matches OFT `bootstrap.py:640` and
+OMFIT `utils_fusion.py:1278`.
+"""
+function nu_star_e(n_e::Real, T_e::Real, R_major::Real,
+    eps::Real, q::Real, Z_eff::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    eps > 0 || throw(ArgumentError("nu_star_e: eps must be > 0"))
+    T_e > 0 || throw(ArgumentError("nu_star_e: T_e must be > 0"))
+    lnL = lnLamb === nothing ? coulomb_log_e(n_e, T_e) : Float64(lnLamb)
+    return 6.921e-18 * abs(q) * R_major * n_e * Z_eff * lnL /
+           (T_e^2 * eps^1.5)
+end
+
+# --------------------------------------------------------------------------
+# Neoclassical resistivity (F_33 · η_Sp)
+# --------------------------------------------------------------------------
+
+# Sauter 1999 Eqs. 13a-13b
+function _F33_sauter(f_t::Real, nu_star::Real, Z_eff::Real)
+    x = f_t / (1.0 + (0.55 - 0.1 * f_t) * sqrt(nu_star) +
+               0.45 * (1.0 - f_t) * nu_star * Z_eff^(-1.5))
+    return 1.0 - (1.0 + 0.36 / Z_eff) * x +
+           (0.59 / Z_eff) * x^2 - (0.23 / Z_eff) * x^3
+end
+
+# Redl 2021 Eqs. 17-18
+function _F33_redl(f_t::Real, nu_star::Real, Z_eff::Real)
+    dZm1 = sqrt(max(Z_eff - 1.0, 0.0))
+    x = f_t / (1.0 + 0.25 * (1.0 - 0.7 * f_t) * sqrt(nu_star) *
+                     (1.0 + 0.45 * dZm1) +
+               0.61 * (1.0 - 0.41 * f_t) * nu_star / sqrt(Z_eff))
+    return 1.0 - (1.0 + 0.21 / Z_eff) * x +
+           (0.54 / Z_eff) * x^2 - (0.33 / Z_eff) * x^3
+end
+
+"""
+    eta_neoclassical(model, n_e, T_e, Z_eff, f_t, nu_e_star;
+                     lnLamb=nothing) -> Float64
+
+Neoclassical resistivity η [Ω·m] under the chosen closure.
+
+  - `SpitzerModel()`   -- returns `eta_spitzer(n_e, T_e, Z_eff; lnLamb)`
+    unchanged; `f_t` and `nu_e_star` are ignored.
+  - `SpitzerHarmModel()` -- returns `eta_spitzer_harm(n_e, T_e, Z_eff; lnLamb)`
+    (Fitzpatrick/TJ legacy); `f_t` and `nu_e_star` are ignored.
+  - `SauterNeoModel()` -- Sauter 1999 Eq. 13: η = η_Sp / F_33(Sauter).
+  - `RedlNeoModel()`   -- Redl 2021 Eq. 17: η = η_Sp / F_33(Redl).
+
+Note that σ_neo = σ_Sp · F_33, so η_neo = η_Sp / F_33. For a banana-regime
+plasma with f_t ≈ 0.5 and ν*_e ≪ 1, F_33 ≈ 0.4–0.5, so η_neo is a factor
+of ~2 larger than η_Sp — this is the standard H-mode tearing correction.
+"""
+function eta_neoclassical(::SpitzerModel, n_e::Real, T_e::Real, Z_eff::Real,
+    f_t::Real, nu_e_star::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    return eta_spitzer(n_e, T_e, Z_eff; lnLamb=lnLamb)
+end
+
+function eta_neoclassical(::SpitzerHarmModel, n_e::Real, T_e::Real, Z_eff::Real,
+    f_t::Real, nu_e_star::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    return eta_spitzer_harm(n_e, T_e, Z_eff; lnLamb=lnLamb)
+end
+
+function eta_neoclassical(::SauterNeoModel, n_e::Real, T_e::Real, Z_eff::Real,
+    f_t::Real, nu_e_star::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    eta_sp = eta_spitzer(n_e, T_e, Z_eff; lnLamb=lnLamb)
+    F33 = _F33_sauter(clamp(f_t, 0.0, 1.0), max(nu_e_star, 0.0), Z_eff)
+    F33 > 0 || throw(DomainError(F33, "eta_neoclassical: F_33 non-positive — " *
+                                      "inputs outside Sauter fit range"))
+    return eta_sp / F33
+end
+
+function eta_neoclassical(::RedlNeoModel, n_e::Real, T_e::Real, Z_eff::Real,
+    f_t::Real, nu_e_star::Real;
+    lnLamb::Union{Real,Nothing}=nothing)
+    eta_sp = eta_spitzer(n_e, T_e, Z_eff; lnLamb=lnLamb)
+    F33 = _F33_redl(clamp(f_t, 0.0, 1.0), max(nu_e_star, 0.0), Z_eff)
+    F33 > 0 || throw(DomainError(F33, "eta_neoclassical: F_33 non-positive — " *
+                                      "inputs outside Redl fit range"))
+    return eta_sp / F33
+end
+
+end # module NeoclassicalResistivity
diff --git a/src/Utilities/PhysicalConstants.jl b/src/Utilities/PhysicalConstants.jl
new file mode 100644
index 000000000..a9668b611
--- /dev/null
+++ b/src/Utilities/PhysicalConstants.jl
@@ -0,0 +1,22 @@
+"""
+    PhysicalConstants
+
+Shared physical constants used across GPEC modules. Values match the
+Fortran GPEC/SLAYER conventions (sglobal_mod) so numerical results can
+be directly compared.
+
+All quantities in SI units.
+"""
+module PhysicalConstants
+
+# Match the Fortran GPEC/SLAYER sglobal_mod values exactly so cross-code numerical comparison is meaningful.
+const MU_0  = 4.0e-7 * π            # vacuum permeability         [H/m]
+const M_E   = 9.1094e-31            # electron mass               [kg]
+const M_P   = 1.6726e-27            # proton mass                 [kg]
+const E_CHG = 1.6021917e-19         # elementary charge           [C]
+const K_B   = 1.3807e-23            # Boltzmann constant          [J/K]
+const EPS_0 = 8.8542e-12            # vacuum permittivity         [F/m]
+
+export MU_0, M_E, M_P, E_CHG, K_B, EPS_0
+
+end # module PhysicalConstants
diff --git a/src/Utilities/Utilities.jl b/src/Utilities/Utilities.jl
index 7cd036f11..2687340f6 100644
--- a/src/Utilities/Utilities.jl
+++ b/src/Utilities/Utilities.jl
@@ -10,11 +10,17 @@ mathematical utilities.
 # Submodules
 
   - `FourierTransforms`: Efficient Fourier transforms with pre-computed basis functions
+  - `PhysicalConstants`: SI physical constants matching Fortran GPEC/SLAYER values
+  - `NeoclassicalResistivity`: Spitzer/Sauter/Redl resistivity closures shared by
+    the GGJ and SLAYER inner-layer models
 """
 module Utilities
 
 include("FourierTransforms.jl")
 include("FourierCoefficients.jl")
+include("PhysicalConstants.jl")
+include("KineticProfiles.jl")
+include("NeoclassicalResistivity.jl")
 include("GridUtilities.jl")
 
 using .FourierTransforms
@@ -24,4 +30,17 @@ export fourier_transform!, fourier_inverse_transform!
 
 export FourierCoefficients, empty_FourierCoefficients, get_complex_coeff, get_complex_coeffs!
 
+using .PhysicalConstants
+export PhysicalConstants
+export MU_0, M_E, M_P, E_CHG, K_B, EPS_0
+
+export KineticProfiles
+
+using .NeoclassicalResistivity
+export NeoclassicalResistivity
+export NeoResistivityModel, SpitzerModel, SpitzerHarmModel, SauterNeoModel, RedlNeoModel
+export coulomb_log_e, eta_spitzer, tau_ee_spitzer_harm, eta_spitzer_harm
+export trapped_fraction, trapped_fraction_eps
+export nu_star_e, eta_neoclassical
+
 end # module Utilities
diff --git a/test/runtests.jl b/test/runtests.jl
index 8f891dfb9..8642e8d68 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -30,6 +30,18 @@ else
     include("./runtests_parallel_integration.jl")
     include("./runtests_sing.jl")
     include("./runtests_tj_analytic.jl")
+    include("./runtests_kinetic_profiles.jl")
+    include("./runtests_resist_eval.jl")
+    include("./runtests_slayer_params.jl")
+    include("./runtests_slayer_riccati.jl")
+    include("./runtests_slayer_inputs.jl")
+    include("./runtests_dispersion_residual.jl")
+    include("./runtests_dispersion_coupled.jl")
+    include("./runtests_dispersion_coupled_full.jl")
+    include("./runtests_dispersion_scan.jl")
+    include("./runtests_dispersion_amr.jl")
+    include("./runtests_dispersion_polish.jl")
+    include("./runtests_slayer_runner.jl")
     include("./runtests_kinetic.jl")
     include("./runtests_fullruns.jl")
     include("./runtests_coils.jl")
diff --git a/test/runtests_dispersion_amr.jl b/test/runtests_dispersion_amr.jl
new file mode 100644
index 000000000..014f3d019
--- /dev/null
+++ b/test/runtests_dispersion_amr.jl
@@ -0,0 +1,239 @@
+@testset "Dispersion AMR scan + triangulation extraction" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.InnerLayer: InnerLayerModel, solve_inner
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using StaticArrays
+
+    @testset "amr_scan: basic structure and hash-caching" begin
+        eval_count = Ref(0)
+        function counting_f(Q)
+            eval_count[] += 1
+            return ComplexF64(Q)^2 - 1
+        end
+
+        # Small 2×2 initial grid → 9 unique corners
+        amr = amr_scan(counting_f, (-1.0, 1.0), (-1.0, 1.0);
+                        nre0=2, nim0=2, passes=0)
+        @test amr isa AMRResult
+        @test length(amr.cells) == 4       # 2×2 cells
+        # Dedup: 9 unique corners (3×3)
+        @test length(amr.Q) == 9
+        @test length(amr.Δ) == 9
+        @test eval_count[] == 9            # exactly one call per unique Q
+    end
+
+    @testset "amr_scan: refinement concentrates cells near zero crossings" begin
+        f(Q) = ComplexF64(Q) - (0.3 + 0.4im)       # single zero
+        amr0 = amr_scan(f, (-1.0, 1.0), (-1.0, 1.0); nre0=4, nim0=4, passes=0)
+        amr3 = amr_scan(f, (-1.0, 1.0), (-1.0, 1.0); nre0=4, nim0=4, passes=3)
+        @test length(amr3.cells) > length(amr0.cells)
+        @test length(amr3.Q)    > length(amr0.Q)
+        # A 4×4 coarse grid is 16 cells; adding 3 refinement passes must
+        # leave the total bounded by exponential growth of only the cells
+        # bracketing the root (roughly linear in the path length).
+        @test length(amr3.cells) < 1000    # not exponential in passes
+    end
+
+    @testset "amr_scan: argument validation" begin
+        @test_throws ArgumentError amr_scan(identity, (0.0, 1.0), (0.0, 1.0);
+                                              nre0=0, nim0=2, passes=1)
+        @test_throws ArgumentError amr_scan(identity, (0.0, 1.0), (0.0, 1.0);
+                                              nre0=2, nim0=0, passes=1)
+        @test_throws ArgumentError amr_scan(identity, (0.0, 1.0), (0.0, 1.0);
+                                              nre0=2, nim0=2, passes=-1)
+    end
+
+    @testset "amr_scan: max_cells safety cap fires" begin
+        # A pathological f that forces every cell to subdivide every pass
+        f(Q) = 0.0 + 0.0im        # identically zero → every cell crosses
+        @test_throws ErrorException amr_scan(f, (-1.0, 1.0), (-1.0, 1.0);
+                                               nre0=4, nim0=4, passes=10,
+                                               max_cells=100)
+    end
+
+    @testset "find_growth_rates(AMR): single isolated root" begin
+        Q_root = 0.42 + 0.27im
+        f(Q) = ComplexF64(Q) - Q_root
+        amr = amr_scan(f, (-1.0, 1.5), (-0.5, 1.0);
+                        nre0=8, nim0=6, passes=4)
+        result = find_growth_rates(amr, 1.0)
+        @test result isa GrowthRateResult
+        @test abs(result.Q_root - Q_root) < 1e-3     # AMR-resolution limited
+        @test isempty(result.poles)
+        @test length(result.valid_roots) == 1
+    end
+
+    @testset "find_growth_rates(AMR): higher-γ root selected" begin
+        Q1 = 0.3 + 0.5im      # higher γ
+        Q2 = -0.4 + 0.1im
+        f(Q) = (ComplexF64(Q) - Q1) * (ComplexF64(Q) - Q2)
+        amr = amr_scan(f, (-1.0, 1.0), (-0.3, 0.8);
+                        nre0=10, nim0=8, passes=4)
+        result = find_growth_rates(amr, 1.0)
+        @test length(result.valid_roots) == 2
+        @test abs(result.Q_root - Q1) < 1e-2
+    end
+
+    @testset "find_growth_rates(AMR): pole detection" begin
+        Q_r = 0.4 + 0.2im
+        Q_p = -0.5 + 0.6im
+        f(Q) = (ComplexF64(Q) - Q_r) / (ComplexF64(Q) - Q_p)
+        amr = amr_scan(f, (-1.5, 1.5), (-0.5, 1.5);
+                        nre0=10, nim0=8, passes=5)
+        result = find_growth_rates(amr, 1.0; pole_threshold=10.0)
+        @test length(result.poles) >= 1
+        @test any(p -> abs(p - Q_p) < 0.05, result.poles)
+        @test abs(result.Q_root - Q_r) < 1e-3
+    end
+
+    @testset "find_growth_rates(AMR): tauk normalization" begin
+        Q_root = 1.0 + 2.0im
+        f(Q) = ComplexF64(Q) - Q_root
+        amr = amr_scan(f, (-2.0, 3.0), (-1.0, 4.0);
+                        nre0=8, nim0=8, passes=4)
+        tauk = 5e-5
+        result = find_growth_rates(amr, tauk)
+        @test result.omega_Hz ≈ real(result.Q_root) / tauk
+        @test result.gamma_Hz ≈ imag(result.Q_root) / tauk
+    end
+
+    @testset "find_growth_rates(AMR): argument validation" begin
+        # Too few points to triangulate
+        GRE = GeneralizedPerturbedEquilibrium.Dispersion
+        @test_throws ArgumentError GRE._extract_growth_rates_amr(
+            ComplexF64[0.0+0im, 1.0+0im], ComplexF64[1.0+0im, 2.0+0im], 1.0;
+            re_target=0.0, im_target=0.0, pole_threshold=10.0,
+            filter_above_poles=true, filter_outside_re=true)
+        # Length mismatch
+        @test_throws ArgumentError GRE._extract_growth_rates_amr(
+            ComplexF64[0.0+0im, 1.0+0im, 1.0+1im],
+            ComplexF64[1.0+0im, 2.0+0im], 1.0;
+            re_target=0.0, im_target=0.0, pole_threshold=10.0,
+            filter_above_poles=true, filter_outside_re=true)
+    end
+
+    @testset "AMR vs brute-force: same root to within AMR refinement precision" begin
+        # Sanity: the AMR and brute-force paths should find the same root
+        # (to roughly the AMR resolution — the AMR typically resolves
+        # better per-evaluation than a uniform grid).
+        Q_root = 0.5 + 0.3im
+        f(Q) = ComplexF64(Q) - Q_root
+        scan = brute_force_scan(f, (-1.0, 1.0), (-0.5, 1.0);
+                                 nre=80, nim=60, threaded=false)
+        amr  = amr_scan(f, (-1.0, 1.0), (-0.5, 1.0);
+                         nre0=8, nim0=6, passes=4)
+        r_grid = find_growth_rates(scan, 1.0)
+        r_amr  = find_growth_rates(amr,  1.0)
+        @test abs(r_grid.Q_root - Q_root) < 1e-3
+        @test abs(r_amr.Q_root  - Q_root) < 1e-3
+        @test abs(r_grid.Q_root - r_amr.Q_root) < 5e-3
+    end
+
+    @testset "API: SurfaceCoupling and MultiSurfaceCoupling through amr_scan" begin
+        struct LinModel <: InnerLayerModel
+            a::ComplexF64
+            b::ComplexF64
+        end
+        GeneralizedPerturbedEquilibrium.InnerLayer.solve_inner(
+            m::LinModel, params, Q::Number) =
+            InnerLayerResponse(m.a + m.b * ComplexF64(Q), zero(ComplexF64))
+
+        Q_pin = 0.7 - 0.3im
+        sc = surface_coupling(LinModel(0.0im, 1.0+0im), nothing,
+                               Q_pin; scale=1.0, tauk=1.0)
+        amr = amr_scan(sc, (-0.5, 1.5), (-1.0, 0.5);
+                        nre0=8, nim0=6, passes=4)
+        r = find_growth_rates(amr, sc.tauk)
+        @test abs(r.Q_root - Q_pin) < 1e-2
+
+        # Multi-surface coupled scan through AMR
+        Q_a, Q_b = 0.7 - 0.3im, -0.4 + 0.5im
+        sc1 = surface_coupling(LinModel(0.0im, 1.0+0im), nothing,
+                                ComplexF64(0); scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinModel(0.0im, 1.0+0im), nothing,
+                                ComplexF64(0); scale=1.0, tauk=1.0)
+        dp = ComplexF64[Q_a 0.0; 0.0 Q_b]
+        mc = multi_surface_coupling([sc1, sc2], dp)
+        amr_c = amr_scan(mc, (-1.0, 1.5), (-1.0, 1.0);
+                          nre0=10, nim0=8, passes=4)
+        r_c = find_growth_rates(amr_c, mc.surfaces[mc.ref_idx].tauk)
+        @test abs(r_c.Q_root - Q_b) < 1e-2     # higher-γ root
+    end
+
+    # =========================================================================
+    # multi_box_amr_scan
+    # =========================================================================
+    using GeneralizedPerturbedEquilibrium.Dispersion: BoxActivity, NoActivity,
+        ReZeroCrossing, ImZeroCrossing, PoleMagnitude, MultiBoxAMRResult,
+        multi_box_amr_scan, as_amr_result
+
+    @testset "multi_box_amr_scan: 3-box stripe with zero, pole, and inactive box" begin
+        # Synthetic residual: zero at Q=0 (centre stripe), pole at Q=-50
+        # (left stripe), nothing in right stripe. Complex offset 1+1im keeps
+        # Im(f) above zero in the right stripe so its sign-change tests don't
+        # fire spuriously on rational-function residuals (Im=0 contour
+        # otherwise crosses the entire real axis).
+        f(Q) = (ComplexF64(Q) - 0.0) / (ComplexF64(Q) - (-50.0)) + (1.0 + 1.0im)
+        boxes = [((-75.0, -25.0), (-25.0, 25.0)),
+                 ((-25.0,  25.0), (-25.0, 25.0)),
+                 (( 25.0,  75.0), (-25.0, 25.0))]
+        result = multi_box_amr_scan(f, boxes;
+                                     pole_magnitude_threshold=10.0,
+                                     prescreen_nre=25, prescreen_nim=25,
+                                     nre0=25, nim0=25, passes=2,
+                                     max_cells=100_000,
+                                     max_cells_action=:warn_truncate,
+                                     parallel=false)
+        @test result isa MultiBoxAMRResult
+        @test length(result.box_results) == 3
+        @test length(result.box_activity) == 3
+        @test result.box_activity[1] != NoActivity   # contains pole
+        @test result.box_activity[2] != NoActivity   # contains zero
+        @test result.box_activity[3] == NoActivity   # empty stripe
+        @test result.box_results[3] === nothing
+        @test result.box_results[1] !== nothing
+        @test result.box_results[2] !== nothing
+        # prescreen_evals is bounded by 3 boxes × 26×26 = 2028 (some shared
+        # boundary corners are deduplicated within each box's local cache, so
+        # the count is ≤ 2028).
+        @test result.prescreen_evals ≤ 3 * 26 * 26
+
+        # as_amr_result wraps cleanly
+        amr = as_amr_result(result)
+        @test amr isa AMRResult
+        @test length(amr.cells) == length(result.cells)
+        @test length(amr.Q) == length(result.Q)
+    end
+
+    @testset "multi_box_amr_scan: pole-only path" begin
+        # Sharp pole at Q=-50+0i with complex offset that keeps Re(f),Im(f) one-
+        # signed across the prescreen grid except in the cell containing the
+        # pole. Confirms the |Δ| ≥ pole_magnitude_threshold criterion fires
+        # independent of sign-change tests.
+        g(Q) = 1000.0 / (ComplexF64(Q) - (-50.0))^2 + (5.0 + 5.0im)
+        boxes = [((-75.0, -25.0), (-25.0, 25.0)),
+                 ((-25.0,  25.0), (-25.0, 25.0)),
+                 (( 25.0,  75.0), (-25.0, 25.0))]
+        result = multi_box_amr_scan(g, boxes;
+                                     pole_magnitude_threshold=50.0,
+                                     prescreen_nre=25, prescreen_nim=25,
+                                     nre0=25, nim0=25, passes=1,
+                                     max_cells=100_000,
+                                     max_cells_action=:warn_truncate,
+                                     parallel=false)
+        @test result.box_activity[1] != NoActivity
+        @test result.box_activity[2] == NoActivity
+        @test result.box_activity[3] == NoActivity
+    end
+
+    @testset "multi_box_amr_scan: argument validation" begin
+        f(Q) = ComplexF64(Q)
+        boxes = [((-1.0, 1.0), (-1.0, 1.0))]
+        @test_throws ArgumentError multi_box_amr_scan(f, boxes;
+            pole_magnitude_threshold=1.0, prescreen_nre=0)
+        @test_throws ArgumentError multi_box_amr_scan(f, boxes;
+            pole_magnitude_threshold=1.0, prescreen_nim=0)
+        @test_throws ArgumentError multi_box_amr_scan(f, boxes;
+            pole_magnitude_threshold=-1.0)
+    end
+end
diff --git a/test/runtests_dispersion_coupled.jl b/test/runtests_dispersion_coupled.jl
new file mode 100644
index 000000000..5a65539ff
--- /dev/null
+++ b/test/runtests_dispersion_coupled.jl
@@ -0,0 +1,260 @@
+@testset "Dispersion coupled determinant" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.InnerLayer: InnerLayerModel, solve_inner
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using LinearAlgebra
+    using StaticArrays
+
+    # ---------------------------------------------------------------
+    # Synthetic linear inner-layer model with adjustable per-surface
+    # tauk for testing the Q rescaling logic.
+    #   Δ_inner(Q) = a + b·Q
+    # ---------------------------------------------------------------
+    struct LinTestModel <: InnerLayerModel
+        a::ComplexF64
+        b::ComplexF64
+    end
+    GeneralizedPerturbedEquilibrium.InnerLayer.solve_inner(
+        m::LinTestModel, params, Q::Number) =
+        InnerLayerResponse(m.a + m.b * ComplexF64(Q), zero(ComplexF64))
+
+    function _slayer_ref()
+        return slayer_parameters(
+            n_e=5.0e19, t_e=1000.0, t_i=1000.0,
+            omega=0.0, omega_e=1.0e4, omega_i=5.0e3,
+            qval=2.0, sval_r=1.0, bt=2.0,
+            rs=0.5, R0=1.7, mu_i=2.0, zeff=1.0,
+            chi_perp=1.0, chi_tor=1.0, m=2, n=1)
+    end
+
+    @testset "Constructor validation" begin
+        sc1 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               1.0+0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               2.0+0im; scale=1.0, tauk=1.0)
+        good_dp = ComplexF64[1.0 0.1; 0.1 2.0]
+
+        mc = multi_surface_coupling([sc1, sc2], good_dp)
+        @test mc.ref_idx == 1
+        @test mc.msing_max == 2          # min(3, 2) = 2
+        @test size(mc.dp_matrix) == (2, 2)
+
+        # 3-surface default also caps at 3 (min(3, 3) = 3)
+        sc3 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               3.0+0im; scale=1.0, tauk=1.0)
+        good_dp3 = ComplexF64[1.0 0.1 0.0; 0.1 2.0 0.0; 0.0 0.0 3.0]
+        mc3 = multi_surface_coupling([sc1, sc2, sc3], good_dp3)
+        @test mc3.msing_max == 3
+
+        # 4-surface case caps at 3 (the design default — Δ' beyond 3 surfaces
+        # tends to be erratic in practice)
+        sc4 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               4.0+0im; scale=1.0, tauk=1.0)
+        good_dp4 = ComplexF64[1.0 0.0 0.0 0.0;
+                               0.0 2.0 0.0 0.0;
+                               0.0 0.0 3.0 0.0;
+                               0.0 0.0 0.0 4.0]
+        mc4 = multi_surface_coupling([sc1, sc2, sc3, sc4], good_dp4)
+        @test mc4.msing_max == 3         # default capped at 3
+        # Caller can opt in to all 4
+        mc4_full = multi_surface_coupling([sc1, sc2, sc3, sc4], good_dp4;
+                                           msing_max=4)
+        @test mc4_full.msing_max == 4
+
+        # Mismatched dp size
+        @test_throws ArgumentError multi_surface_coupling(
+            [sc1, sc2], ComplexF64[1.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 3.0])
+        @test_throws ArgumentError multi_surface_coupling(
+            [sc1, sc2], ComplexF64[1.0 0.0])
+
+        # Out-of-range ref_idx
+        @test_throws ArgumentError multi_surface_coupling([sc1, sc2], good_dp;
+                                                           ref_idx=3)
+        @test_throws ArgumentError multi_surface_coupling([sc1, sc2], good_dp;
+                                                           ref_idx=0)
+
+        # Out-of-range msing_max
+        @test_throws ArgumentError multi_surface_coupling([sc1, sc2], good_dp;
+                                                           msing_max=3)
+        @test_throws ArgumentError multi_surface_coupling([sc1, sc2], good_dp;
+                                                           msing_max=0)
+    end
+
+    @testset "Diagonal Δ' factorizes (det = ∏ per-surface residuals)" begin
+        # When dp_matrix is diagonal, no off-diagonal coupling exists and
+        # the coupled determinant should reduce exactly to the product of
+        # per-surface residuals.
+        sc1 = surface_coupling(LinTestModel(1.0+0im, 1.0+0im), nothing,
+                               5.0+0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinTestModel(2.0+0im, 1.0+0im), nothing,
+                               7.0+0im; scale=1.0, tauk=1.0)
+        sc3 = surface_coupling(LinTestModel(0.5+0im, 0.5+0im), nothing,
+                               3.0+0im; scale=1.0, tauk=1.0)
+        dp = ComplexF64[5.0 0.0 0.0;
+                         0.0 7.0 0.0;
+                         0.0 0.0 3.0]
+        mc = multi_surface_coupling([sc1, sc2, sc3], dp)
+        for Q in (0.5+0im, 2.0+0.3im, -1.0-0.5im, 4.5+1.0im)
+            @test mc(Q) ≈ sc1(Q) * sc2(Q) * sc3(Q) rtol = 1e-12
+        end
+    end
+
+    @testset "Diagonal Δ' roots = single-surface roots" begin
+        # With Δ_inner(Q) = b·Q and dp_diag = b·Q_root for each surface,
+        # the coupled determinant has its roots exactly at the union of
+        # single-surface roots.
+        Q1, Q2 = 0.5+0.0im, 2.0+0.0im
+        sc1 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               Q1; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               Q2; scale=1.0, tauk=1.0)
+        dp = ComplexF64[real(Q1) 0.0; 0.0 real(Q2)]
+        mc = multi_surface_coupling([sc1, sc2], dp)
+        @test abs(mc(Q1)) < 1e-12
+        @test abs(mc(Q2)) < 1e-12
+        @test abs(mc(0.0+0.0im)) > 0
+    end
+
+    @testset "Off-diagonal coupling shifts the roots away from the diagonal" begin
+        sc1 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               0.5+0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               2.0+0im; scale=1.0, tauk=1.0)
+        # Coupling-free baseline
+        dp_diag = ComplexF64[0.5 0.0; 0.0 2.0]
+        mc_diag = multi_surface_coupling([sc1, sc2], dp_diag)
+        # With off-diagonal coupling
+        dp_offd = ComplexF64[0.5 0.3; 0.3 2.0]
+        mc_offd = multi_surface_coupling([sc1, sc2], dp_offd)
+
+        # Single-surface roots are no longer roots of the coupled det
+        Q1 = 0.5 + 0.0im
+        @test abs(mc_diag(Q1)) < 1e-12       # diagonal: still a root
+        @test abs(mc_offd(Q1)) > 0           # coupled: no longer a root
+        # The shift size matches the off-diagonal magnitude squared
+        # det = (0.5-Q)(2-Q) - 0.3² ⇒ at Q=0.5 the det = -0.09
+        @test mc_offd(Q1) ≈ -0.09 rtol = 1e-12
+    end
+
+    @testset "msing_max truncation uses upper-left submatrix" begin
+        sc1 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               1.0+0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               2.0+0im; scale=1.0, tauk=1.0)
+        sc3 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               3.0+0im; scale=1.0, tauk=1.0)
+        dp = ComplexF64[1.0 0.0 0.0;
+                         0.0 2.0 0.0;
+                         0.0 0.0 3.0]
+
+        # msing_max = 1 reduces to sc1(Q) alone
+        mc1 = multi_surface_coupling([sc1, sc2, sc3], dp; msing_max=1)
+        for Q in (0.0+0im, 1.0+0im, 2.0+0im)
+            @test mc1(Q) ≈ sc1(Q)
+        end
+
+        # msing_max = 2 uses the upper-left 2×2 → sc1·sc2
+        mc2 = multi_surface_coupling([sc1, sc2, sc3], dp; msing_max=2)
+        for Q in (0.0+0im, 0.5+0.5im)
+            @test mc2(Q) ≈ sc1(Q) * sc2(Q)
+        end
+
+        # msing_max = 3 (default for ≥3 surfaces) uses the full 3×3 → sc1·sc2·sc3
+        mc3 = multi_surface_coupling([sc1, sc2, sc3], dp)
+        @test mc3.msing_max == 3         # min(3, 3) = 3
+        for Q in (0.5+0.5im, 1.5-0.5im)
+            @test mc3(Q) ≈ sc1(Q) * sc2(Q) * sc3(Q)
+        end
+    end
+
+    @testset "Per-surface Q rescaling via tauk_ref / tauk_k" begin
+        # Each surface evaluates its inner Δ at Q_k = Q · (tauk_ref/tauk_k).
+        # With Δ(Q) = Q (b=1, a=0), the diagonal modification is
+        #   M[k,k] = dp_diag_k - scale·Q·(tauk_ref/tauk_k)
+        # Verify against an explicit closed form with mismatched tauks.
+        sc1 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               0.0+0im; scale=1.0, tauk=2.0)   # ref tauk
+        sc2 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               0.0+0im; scale=1.0, tauk=4.0)   # half rate
+        dp = ComplexF64[0.0 0.0; 0.0 0.0]
+        mc = multi_surface_coupling([sc1, sc2], dp; ref_idx=1)
+        for Q in (1.0+0im, 0.5+0.3im)
+            # M[1,1] = 0 - Q · (2/2) = -Q
+            # M[2,2] = 0 - Q · (2/4) = -Q/2
+            # det = M[1,1] · M[2,2] = Q·Q/2 = Q²/2
+            @test mc(Q) ≈ Q^2 / 2 rtol = 1e-12
+        end
+
+        # Switch ref_idx to surface 2
+        mc2 = multi_surface_coupling([sc1, sc2], dp; ref_idx=2)
+        for Q in (1.0+0im, 0.5+0.3im)
+            # M[1,1] = -Q · (4/2) = -2Q
+            # M[2,2] = -Q · (4/4) = -Q
+            # det = 2Q · Q = 2Q²
+            @test mc2(Q) ≈ 2 * Q^2 rtol = 1e-12
+        end
+    end
+
+    @testset "SLAYER self-consistency: known coupled root" begin
+        # Build a 2-surface SLAYER MultiSurfaceCoupling, evaluate at
+        # Q_pin, and back-fill dp_matrix so that det(M(Q_pin)) = 0
+        # exactly.
+        p_a = _slayer_ref()
+        p_b = _slayer_ref()
+        m = SLAYERModel()
+        sc1 = surface_coupling(m, p_a, 0.0+0im)
+        sc2 = surface_coupling(m, p_b, 0.0+0im)
+
+        Q_pin = 0.3 + 0.4im
+        ref_tauk = sc1.tauk
+
+        # Compute the diagonal modifications at Q_pin
+        Δ1 = solve_inner(m, p_a, Q_pin * (ref_tauk/sc1.tauk)).tearing * sc1.scale
+        Δ2 = solve_inner(m, p_b, Q_pin * (ref_tauk/sc2.tauk)).tearing * sc2.scale
+
+        # Build dp such that M(Q_pin) is exactly singular.
+        # Choose off-diagonal couplings, then set diagonals so M[k,k]=Δ_k
+        # makes the matrix singular by setting M[1,1]·M[2,2] = M[1,2]·M[2,1].
+        c12, c21 = 0.05+0im, 0.05+0im
+        # Pick M[1,1] arbitrarily, solve for M[2,2]:
+        M11 = 0.7 + 0.0im
+        M22 = (c12 * c21) / M11
+        dp = ComplexF64[M11+Δ1  c12;
+                         c21    M22+Δ2]
+
+        mc = multi_surface_coupling([sc1, sc2], dp)
+        # The constructed M(Q_pin) is exactly singular by construction
+        @test abs(mc(Q_pin)) < 1e-10
+
+        # Off-pin Q gives a non-trivial determinant
+        @test abs(mc(Q_pin + 0.05)) > 1e-3
+    end
+
+    @testset "GGJ surfaces flow through the coupled API" begin
+        p = glasser_wang_2020_eq55()
+        sc1 = surface_coupling(GGJModel(solver=:shooting), p, -1.0+0im)
+        sc2 = surface_coupling(GGJModel(solver=:shooting), p, -2.0+0im)
+        dp = ComplexF64[-1.0 0.1; 0.1 -2.0]
+        mc = multi_surface_coupling([sc1, sc2], dp)
+        @test mc isa MultiSurfaceCoupling
+        @test mc.surfaces[1].tauk == 1.0      # GGJ default
+        @test mc(1e-3 + 0.0im) isa ComplexF64
+    end
+
+    @testset "Broadcast over a 2D Q grid" begin
+        # Coupled residual must be broadcast-compatible for PR 5/6 scans.
+        sc1 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               0.0+0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinTestModel(0.0im, 1.0+0im), nothing,
+                               0.0+0im; scale=1.0, tauk=1.0)
+        dp = ComplexF64[0.0 0.0; 0.0 0.0]
+        mc = multi_surface_coupling([sc1, sc2], dp)
+
+        Q_grid = [(qr + qi*im) for qr in -1.0:0.5:1.0, qi in -1.0:0.5:1.0]
+        det_grid = mc.(Q_grid)
+        @test size(det_grid) == size(Q_grid)
+        @test all(d -> d isa ComplexF64, det_grid)
+        # det = Q² with these params; one interior cross-check
+        @test det_grid[3, 3] ≈ Q_grid[3, 3]^2
+    end
+end
diff --git a/test/runtests_dispersion_coupled_full.jl b/test/runtests_dispersion_coupled_full.jl
new file mode 100644
index 000000000..d471650f8
--- /dev/null
+++ b/test/runtests_dispersion_coupled_full.jl
@@ -0,0 +1,270 @@
+@testset "Dispersion 4m×4m full Pletzer-Dewar coupled determinant (CoupledFullMatch)" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.InnerLayer: InnerLayerModel, InnerLayerResponse, solve_inner
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using LinearAlgebra
+
+    # Synthetic inner-layer model with explicit (tearing, interchange)
+    # pair — lets us probe both channels independently.
+    struct _LinearInnerF <: InnerLayerModel
+        a_t::ComplexF64
+        b_t::ComplexF64   # tearing: Δ_t(Q) = a_t + b_t·Q
+        a_i::ComplexF64
+        b_i::ComplexF64   # interchange: Δ_i(Q) = a_i + b_i·Q
+    end
+    GeneralizedPerturbedEquilibrium.InnerLayer.solve_inner(
+        m::_LinearInnerF, params, Q::Number) =
+        InnerLayerResponse(m.a_t + m.b_t * ComplexF64(Q),
+            m.a_i + m.b_i * ComplexF64(Q))
+
+    @testset "Constructor validation" begin
+        sc1 = surface_coupling(_LinearInnerF(-1.0 + 0im, 0 + 0im, 0.1 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(_LinearInnerF(-0.5 + 0im, 0 + 0im, 0.2 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+        dp_raw = ComplexF64[
+            1.0 0.1 0.2 0.05;
+            0.1 1.2 0.05 0.2;
+            0.2 0.05 -5.0 0.3;
+            0.05 0.2 0.3 -4.0]
+        mc = multi_surface_coupling_full([sc1, sc2], dp_raw)
+        @test size(mc.dp_raw) == (4, 4)
+        @test mc.msing_max == 2
+        @test mc.ref_idx == 1
+        @test mc.rotation == [0.0, 0.0]
+        @test mc.ntor == 1
+
+        # Wrong outer dim
+        @test_throws ArgumentError multi_surface_coupling_full([sc1, sc2],
+            dp_raw[1:2, 1:2])
+        @test_throws ArgumentError multi_surface_coupling_full([sc1, sc2],
+            dp_raw; ref_idx=0)
+        @test_throws ArgumentError multi_surface_coupling_full([sc1, sc2],
+            dp_raw; ref_idx=3)
+        @test_throws ArgumentError multi_surface_coupling_full([sc1, sc2],
+            dp_raw; msing_max=0)
+        @test_throws ArgumentError multi_surface_coupling_full([sc1, sc2],
+            dp_raw; msing_max=3)
+        # Wrong rotation length
+        @test_throws ArgumentError multi_surface_coupling_full([sc1, sc2],
+            dp_raw; rotation=[0.0])
+    end
+
+    @testset "1-surface 4×4 det matches hand computation" begin
+        # m=1 case: matrix is 4×4 and fully hand-verifiable.
+        dp_raw = ComplexF64[1.0 0.5; 0.3 2.0]
+        sc = surface_coupling(_LinearInnerF(0.7 + 0im, 0 + 0im, 0.2 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0, dc=0.0)
+        mc = multi_surface_coupling_full([sc], dp_raw)
+        # At Q=0.1 both Δ_t and Δ_i are constants (b=0), so inner Δs independent of Q.
+        det_jl = mc(0.1 + 0.0im)
+        # Hand-computed matrix (see the port comment block for the layout):
+        #   mat[3:4, 1:2] = transpose(dp_raw) = [1 0.3; 0.5 2]
+        #   mat[1,1]=1, mat[2,2]=1
+        #   mat[1,3]=-1, mat[1,4]=+1, mat[2,3]=-1, mat[2,4]=-1
+        #   delta1=interchange=0.2, delta2=tearing=0.7
+        #   mat[3,3]=-0.2, mat[3,4]=+0.7, mat[4,3]=-0.2, mat[4,4]=-0.7
+        M_hand = ComplexF64[
+            1 0 -1 1;
+            0 1 -1 -1;
+            1 0.3 -0.2 0.7;
+            0.5 2 -0.2 -0.7]
+        @test det_jl ≈ det(M_hand)
+    end
+
+    @testset "Static (rotation=0) delta1, delta2 assembly" begin
+        # Replicate the Pletzer-Dewar match assembly by hand for msing=2 and
+        # synthetic inner values; confirm the module agrees.
+        dp_raw = ComplexF64[
+            10.0 0.1 0.2 0.3;
+            0.1 11.0 0.4 0.5;
+            0.2 0.4 -5.0 0.6;
+            0.3 0.5 0.6 -4.0]
+        sc1 = surface_coupling(_LinearInnerF(0.2 + 0.1im, 0 + 0im, 0.7 - 0.05im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0, dc=0.0)
+        sc2 = surface_coupling(_LinearInnerF(-0.3 + 0.0im, 0 + 0im, 1.5 + 0.3im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0, dc=0.0)
+        mc = multi_surface_coupling_full([sc1, sc2], dp_raw)
+        det_jl = mc(0.0 + 0.0im)
+
+        # Hand assembly
+        M = zeros(ComplexF64, 8, 8)
+        M[5:8, 1:4] = transpose(dp_raw)
+        # Surface 1: idx1..4 = 1,2,5,6
+        M[1, 1] = 1
+        M[2, 2] = 1
+        M[1, 5] = -1
+        M[1, 6] = 1
+        M[2, 5] = -1
+        M[2, 6] = -1
+        d1_1 = 0.7 - 0.05im     # interchange
+        d2_1 = 0.2 + 0.1im      # tearing
+        M[5, 5] = -d1_1
+        M[5, 6] = d2_1
+        M[6, 5] = -d1_1
+        M[6, 6] = -d2_1
+        # Surface 2: idx1..4 = 3,4,7,8
+        M[3, 3] = 1
+        M[4, 4] = 1
+        M[3, 7] = -1
+        M[3, 8] = 1
+        M[4, 7] = -1
+        M[4, 8] = -1
+        d1_2 = 1.5 + 0.3im
+        d2_2 = -0.3 + 0im
+        M[7, 7] = -d1_2
+        M[7, 8] = d2_2
+        M[8, 7] = -d1_2
+        M[8, 8] = -d2_2
+
+        @test det_jl ≈ det(M) atol = 1e-12 * abs(det(M))
+    end
+
+    @testset "Rotation shift applies i·ntor·rotation to inner Q argument" begin
+        # Ensure the per-surface rotation enters the inner-layer argument.
+        # Use a linear Δ_t model so Q-dependence is tractable.
+        dp_raw = ComplexF64[1.0 0; 0 1.0]
+        # Δ_t(Q) = Q (pure linear), Δ_i(Q) = 0
+        sc = surface_coupling(_LinearInnerF(0 + 0im, 1 + 0im, 0 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0, dc=0.0)
+        # Case A: rotation=0, Q=2+0im → inner sees 2+0im → Δ_t=2, Δ_i=0
+        mc0 = multi_surface_coupling_full([sc], dp_raw; rotation=[0.0], ntor=1)
+        # Case B: rotation=3, Q=2+0im → inner sees 2 + 1j*1*3 = 2+3i → Δ_t=2+3i
+        mcR = multi_surface_coupling_full([sc], dp_raw; rotation=[3.0], ntor=1)
+        @test mc0(2.0 + 0.0im) ≠ mcR(2.0 + 0.0im)
+
+        # Check by hand. Both with the same outer matrix:
+        function detAt(Δ_t, Δ_i)
+            M = ComplexF64[
+                1 0 -1 1;
+                0 1 -1 -1;
+                1 0 -Δ_i Δ_t;
+                0 1 -Δ_i -Δ_t]
+            return det(M)
+        end
+        @test mc0(2.0 + 0.0im) ≈ detAt(2.0 + 0.0im, 0.0 + 0.0im)
+        @test mcR(2.0 + 0.0im) ≈ detAt(2.0 + 3.0im, 0.0 + 0.0im)
+    end
+
+    @testset "SurfaceCoupling scale multiplies both inner channels" begin
+        # sc.scale should hit both delta1 and delta2 equally.
+        dp_raw = ComplexF64[1 0; 0 1]
+        sc_unit = surface_coupling(_LinearInnerF(0.3 + 0im, 0 + 0im, 0.7 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0, dc=0.0)
+        sc_x2 = surface_coupling(_LinearInnerF(0.3 + 0im, 0 + 0im, 0.7 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=2.0, tauk=1.0, dc=0.0)
+        mc1 = multi_surface_coupling_full([sc_unit], dp_raw)
+        mc2 = multi_surface_coupling_full([sc_x2], dp_raw)
+        # Expected hand det for scale=1: d_int=0.7, d_tear=0.3
+        # For scale=2: d_int=1.4, d_tear=0.6
+        function detAt(Δt, Δi)
+            M = ComplexF64[1 0 -1 1; 0 1 -1 -1; 1 0 -Δi Δt; 0 1 -Δi -Δt]
+            return det(M)
+        end
+        @test mc1(0.5 + 0im) ≈ detAt(0.3, 0.7)
+        @test mc2(0.5 + 0im) ≈ detAt(0.6, 1.4)
+    end
+
+    @testset "msing_max truncation" begin
+        dp_raw = ComplexF64[
+            1.0 0.1 0.2 0.3;
+            0.1 1.2 0.4 0.5;
+            0.2 0.4 -5.0 0.6;
+            0.3 0.5 0.6 -4.0]
+        sc1 = surface_coupling(_LinearInnerF(0.5 + 0im, 0 + 0im, 0.2 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(_LinearInnerF(-0.3 + 0im, 0 + 0im, 1.0 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+
+        # With msing_max=1, only surface 1 participates; matrix becomes 4×4
+        # using the upper-left 2×2 block of dp_raw.
+        mc1 = multi_surface_coupling_full([sc1, sc2], dp_raw; msing_max=1)
+        det1 = mc1(0 + 0im)
+        # Hand construct the 4×4
+        sub_dp = dp_raw[1:2, 1:2]
+        M1 = zeros(ComplexF64, 4, 4)
+        M1[3:4, 1:2] = transpose(sub_dp)
+        M1[1, 1] = 1
+        M1[2, 2] = 1
+        M1[1, 3] = -1
+        M1[1, 4] = 1
+        M1[2, 3] = -1
+        M1[2, 4] = -1
+        M1[3, 3] = -0.2
+        M1[3, 4] = 0.5
+        M1[4, 3] = -0.2
+        M1[4, 4] = -0.5
+        @test det1 ≈ det(M1)
+
+        # Full msing_max=2 case must differ
+        mcfull = multi_surface_coupling_full([sc1, sc2], dp_raw; msing_max=2)
+        @test mcfull(0 + 0im) ≠ det1
+    end
+
+    @testset "SLAYER-like (Δ_interchange=0) still gives correct det" begin
+        # When both surfaces are pure-tearing (Δ_interchange=0), the matrix
+        # is non-trivial but still well-defined; verify it's non-zero and
+        # finite (not NaN from singular inner block).
+        dp_raw = ComplexF64[1.0 0.1 0.2 0.3; 0.1 1.2 0.4 0.5;
+            0.2 0.4 -5.0 0.6; 0.3 0.5 0.6 -4.0]
+        sc1 = surface_coupling(_LinearInnerF(-2 + 0im, 0 + 0im, 0 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(_LinearInnerF(-3 + 0im, 0 + 0im, 0 + 0im, 0 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+        mc = multi_surface_coupling_full([sc1, sc2], dp_raw)
+        d = mc(0.1 + 0.2im)
+        @test isfinite(real(d))
+        @test isfinite(imag(d))
+    end
+
+    @testset "inner_kwargs pass-through" begin
+        # Verify that inner_kwargs reaches solve_inner at each Q evaluation.
+        # Use a synthetic model with a tuning parameter to confirm plumbing.
+        struct _ProbeModel <: InnerLayerModel end
+        GeneralizedPerturbedEquilibrium.InnerLayer.solve_inner(
+            ::_ProbeModel, params, Q::Number; scale_factor::Float64=1.0) =
+            InnerLayerResponse(scale_factor * (1.0 + 0im),
+                scale_factor * (0.5 + 0im))
+
+        dp_raw = ComplexF64[1.0 0; 0 1.0]
+        sc = surface_coupling(_ProbeModel(), nothing, 0 + 0im;
+            scale=1.0, tauk=1.0, dc=0.0)
+        mc_native = multi_surface_coupling_full([sc], dp_raw)
+        mc_tuned = multi_surface_coupling_full([sc], dp_raw;
+            inner_kwargs=(scale_factor=0.5,))
+        @test mc_native.inner_kwargs == NamedTuple()
+        @test mc_tuned.inner_kwargs == (scale_factor=0.5,)
+
+        # Det should differ because inner Δ's are halved by the kwarg
+        det_native = mc_native(0.0 + 0.0im)
+        det_tuned = mc_tuned(0.0 + 0.0im)
+        @test det_native ≠ det_tuned
+        @test isfinite(real(det_native)) && isfinite(imag(det_native))
+        @test isfinite(real(det_tuned)) && isfinite(imag(det_tuned))
+    end
+
+    @testset "Static GGJ-like scenario runs without error" begin
+        # Smoke test: larger m=3 case, both channels non-trivial, Q shifted
+        m = 3
+        Random_dp = ComplexF64[
+            5.0 0.2 0.1 0.05 0.3 0.2;
+            0.2 7.0 0.3 0.1 0.2 0.1;
+            0.1 0.3 -3.0 0.4 0.1 0.05;
+            0.05 0.1 0.4 -8.0 0.2 0.1;
+            0.3 0.2 0.1 0.2 -2.5 0.3;
+            0.2 0.1 0.05 0.1 0.3 -6.5]
+        # Non-trivial Q dependence: Δ_t(Q) = a + 0.5·Q, Δ_i(Q) = b + 0.2·Q
+        scs = [surface_coupling(_LinearInnerF(0.3 + 0.01k * im, 0.5 + 0im,
+                0.7 + 0.02k * im, 0.2 + 0im),
+            nothing, 0 + 0im; scale=1.0, tauk=1.0)
+               for k in 1:m]
+        mc = multi_surface_coupling_full(scs, Random_dp)
+        @test size(mc.dp_raw) == (6, 6)
+        d0 = mc(0.0 + 0.0im)
+        d1 = mc(1.0 + 0.5im)
+        @test isfinite(real(d0)) && isfinite(imag(d0))
+        @test isfinite(real(d1)) && isfinite(imag(d1))
+        # Check that it's actually Q-dependent
+        @test d0 != d1
+    end
+end
diff --git a/test/runtests_dispersion_polish.jl b/test/runtests_dispersion_polish.jl
new file mode 100644
index 000000000..8bc4c9702
--- /dev/null
+++ b/test/runtests_dispersion_polish.jl
@@ -0,0 +1,184 @@
+@testset "Dispersion root polishing" begin
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using LinearAlgebra
+    D = GeneralizedPerturbedEquilibrium.Dispersion
+
+    # ----------------------------------------------------------------
+    # _polish_root: core local-solve behaviour on synthetic residuals
+    # ----------------------------------------------------------------
+    @testset "_polish_root: recovers a known simple root, |f| → 0" begin
+        r = 0.42 + 0.27im
+        f(Q) = ComplexF64(Q) - r          # zero exactly at r
+        Q0 = r + (0.013 - 0.009im)         # off-root start
+        Qp, nev, improved = D._polish_root(f, Q0, 0.1)
+        @test improved
+        @test abs(Qp - r) < 1e-10
+        @test abs(f(Qp)) < 1e-10
+        @test nev <= 3 * 8 + 2             # bounded eval budget
+    end
+
+    @testset "_polish_root: nonlinear residual converges" begin
+        r = -0.3 + 0.5im
+        f(Q) = (ComplexF64(Q) - r) * (1 + 0.4 * (ComplexF64(Q) - r))  # simple root at r
+        Qp, _, improved = D._polish_root(f, r + 0.02 - 0.015im, 0.15)
+        @test improved
+        @test abs(Qp - r) < 1e-7
+    end
+
+    @testset "_polish_root: no-op-or-better invariant (never worse)" begin
+        # The polish must never return a point with larger |f| than the start.
+        for r in (0.1 + 0.2im, -0.5 + 0.0im, 0.3 - 0.4im)
+            f(Q) = (ComplexF64(Q) - r) * (ComplexF64(Q) + 1.7)
+            for off in (0.01 + 0.0im, 0.05 + 0.0im, 0.0 + 0.08im, -0.03 + 0.02im)
+                Q0 = r + off
+                Qp, _, _ = D._polish_root(f, Q0, 0.2)
+                @test abs(f(Qp)) <= abs(f(Q0)) + 1e-12
+            end
+        end
+    end
+
+    @testset "_polish_root: trust region bounds the step (no jump to far root)" begin
+        f(Q) = ComplexF64(Q) - (10.0 + 0.0im)   # only root is far outside R
+        Q0 = 0.0 + 0.0im
+        R = 0.1
+        Qp, _, _ = D._polish_root(f, Q0, R)
+        @test abs(Qp - Q0) <= R + 1e-9          # never leaves the trust disk
+        @test abs(Qp - (10.0 + 0.0im)) > 5.0    # did NOT migrate to the far root
+        @test abs(f(Qp)) <= abs(f(Q0))          # still moved downhill within the disk
+    end
+
+    @testset "_polish_root: non-finite residual falls back, no crash" begin
+        fnan(Q) = ComplexF64(NaN, NaN)
+        Qp, nev, improved = D._polish_root(fnan, 0.3 + 0.1im, 0.1)
+        @test !improved
+        @test Qp == 0.3 + 0.1im                 # hard fallback to the start
+        @test nev >= 1
+    end
+
+    @testset "_polish_root: zero radius is a no-op" begin
+        f(Q) = ComplexF64(Q) - (0.2 + 0.1im)
+        Qp, _, improved = D._polish_root(f, 0.25 + 0.05im, 0.0)
+        @test !improved
+        @test Qp == 0.25 + 0.05im
+    end
+
+    @testset "_polish_root: deterministic (same input → same output)" begin
+        f(Q) = (ComplexF64(Q) - (0.5 + 0.3im)) * (ComplexF64(Q) - 2)
+        a = D._polish_root(f, 0.52 + 0.31im, 0.1)
+        b = D._polish_root(f, 0.52 + 0.31im, 0.1)
+        @test a[1] == b[1]
+        @test a[2] == b[2]
+    end
+
+    @testset "_polish_root: BLAS-thread-independent for a det residual" begin
+        # det() routes through LAPACK; polishing must be invariant to BLAS threads
+        # (this is the property the parallel-scan BLAS pin + polish together give).
+        Qstar = 0.3 + 0.2im
+        fdet(Q) = det(ComplexF64[1 0 0; 0 1 0; 0 0 (ComplexF64(Q)-Qstar)])
+        b0 = BLAS.get_num_threads()
+        try
+            BLAS.set_num_threads(1)
+            r1 = D._polish_root(fdet, 0.31 + 0.21im, 0.1)
+            BLAS.set_num_threads(4)
+            r4 = D._polish_root(fdet, 0.31 + 0.21im, 0.1)
+            @test isapprox(r1[1], r4[1]; atol=1e-10)
+            @test abs(fdet(r1[1])) < 1e-8
+        finally
+            BLAS.set_num_threads(b0)
+        end
+    end
+
+    # ----------------------------------------------------------------
+    # _polish_trust_radius: neighbour-aware bound keeps roots coherent
+    # ----------------------------------------------------------------
+    @testset "_polish_trust_radius: capped by nearest neighbour spacing" begin
+        # Two close intersections + one far: radius of #1 is bound by the close one
+        pts = ComplexF64[0+0im, 0.001+0im, 5+0im]
+        R1 = D._polish_trust_radius(pts, 1, 0.01)
+        @test R1 <= 0.45 * 0.001 + 1e-15        # < half the close-pair spacing
+        # Isolated pair: radius falls back to the grid-cell cap (k_cell·h)
+        R2 = D._polish_trust_radius(ComplexF64[0+0im, 5+0im], 1, 0.01)
+        @test R2 <= 3.0 * 0.01 + 1e-12
+        @test R2 > 0
+    end
+
+    @testset "_polish_trust_radius + polish: close roots stay coherent (no swap/merge)" begin
+        r1 = 0.30 + 0.20im
+        r2 = r1 + 0.002                          # second root 0.002 away
+        f(Q) = (ComplexF64(Q) - r1) * (ComplexF64(Q) - r2)
+        pts = ComplexF64[r1 + 0.0003, r2 - 0.0003]   # coarse contour estimates
+        R1 = D._polish_trust_radius(pts, 1, 0.001)
+        R2 = D._polish_trust_radius(pts, 2, 0.001)
+        p1 = D._polish_root(f, pts[1], R1)[1]
+        p2 = D._polish_root(f, pts[2], R2)[1]
+        @test abs(p1 - r1) < abs(p1 - r2)        # #1 refined toward its OWN root
+        @test abs(p2 - r2) < abs(p2 - r1)        # #2 refined toward its OWN root
+        @test p1 != p2                           # did not collapse to one point
+    end
+
+    # ----------------------------------------------------------------
+    # _median_segment_length: grid-resolution proxy
+    # ----------------------------------------------------------------
+    @testset "_median_segment_length" begin
+        re_paths = [ComplexF64[0+0im, 0.1+0im, 0.3+0im]]   # segment lengths 0.1, 0.2
+        im_paths = [ComplexF64[0+0im, 0+0.1im]]            # segment length 0.1
+        @test D._median_segment_length(re_paths, im_paths) ≈ 0.1   # median of [0.1,0.2,0.1]
+        @test D._median_segment_length(Vector{Vector{ComplexF64}}(),
+                                       Vector{Vector{ComplexF64}}()) == 0.0
+    end
+
+    # ----------------------------------------------------------------
+    # Integration: find_growth_rates with `residual` polishes the root
+    # ----------------------------------------------------------------
+    @testset "find_growth_rates: polish recovers the true root from a coarse scan" begin
+        r = 0.42 + 0.27im
+        f(Q) = (ComplexF64(Q) - r) * (1 + 0.4 * (ComplexF64(Q) - r))  # nonlinear, root at r
+        scan = brute_force_scan(f, (0.0, 1.0), (0.0, 0.6); nre=15, nim=12, threaded=false)
+        gr_raw = find_growth_rates(scan, 1.0; residual=nothing)
+        gr_pol = find_growth_rates(scan, 1.0; residual=f)
+        @test !isnan(real(gr_raw.Q_root))                 # coarse scan still isolates it
+        @test abs(f(gr_pol.Q_root)) <= abs(f(gr_raw.Q_root))   # polish never worsens |f|
+        @test abs(gr_pol.Q_root - r) < 1e-4               # and converges to the true root
+        # polishing must not invent or drop roots vs the raw pass
+        @test length(gr_pol.valid_roots) == length(gr_raw.valid_roots)
+    end
+
+    @testset "find_growth_rates: residual=nothing is unchanged (backward compatible)" begin
+        r = 0.42 + 0.27im
+        f(Q) = ComplexF64(Q) - r
+        scan = brute_force_scan(f, (0.0, 1.0), (0.0, 0.6); nre=21, nim=15, threaded=false)
+        a = find_growth_rates(scan, 1.0)                  # default: no residual, no polish
+        b = find_growth_rates(scan, 1.0; residual=nothing)
+        @test a.Q_root == b.Q_root
+    end
+
+    # ----------------------------------------------------------------
+    # Validity gate: drop intersections that don't polish to a true zero
+    # ----------------------------------------------------------------
+    @testset "find_growth_rates: validity gate keeps real roots, drops spurious" begin
+        r = 0.42 + 0.27im
+        f(Q) = (ComplexF64(Q) - r) * (1 + 0.4 * (ComplexF64(Q) - r))  # real root at r
+        scan = brute_force_scan(f, (0.0, 1.0), (0.0, 0.6); nre=15, nim=12, threaded=false)
+
+        # Normal tolerance: the genuine root polishes to |f|≈0 ⇒ kept, not flagged
+        gr = find_growth_rates(scan, 1.0; residual=f, validity_rtol=1e-3)
+        @test !(:spurious in gr.warning_flags)
+        @test !isnan(real(gr.Q_root))
+        @test abs(gr.Q_root - r) < 1e-4
+
+        # Absurdly tight tolerance: even the true root's residual (~machine eps)
+        # exceeds validity_rtol·median(|Δ|) ⇒ every candidate dropped. Exercises
+        # the gate firing + the :spurious / :no_root / filtered_roots plumbing.
+        gr2 = find_growth_rates(scan, 1.0; residual=f, validity_rtol=1e-300)
+        @test :spurious in gr2.warning_flags
+        @test :no_root in gr2.warning_flags          # all candidates were spurious
+        @test isnan(real(gr2.Q_root))                # ⇒ no root reported
+        @test length(gr2.filtered_roots) >= 1        # dropped candidate parked here
+        @test isempty(gr2.valid_roots)
+
+        # Gate is inert without a residual (no polishing ⇒ nothing to test against)
+        gr3 = find_growth_rates(scan, 1.0; residual=nothing, validity_rtol=1e-300)
+        @test !(:spurious in gr3.warning_flags)
+        @test !isnan(real(gr3.Q_root))
+    end
+end
diff --git a/test/runtests_dispersion_residual.jl b/test/runtests_dispersion_residual.jl
new file mode 100644
index 000000000..d0235a0da
--- /dev/null
+++ b/test/runtests_dispersion_residual.jl
@@ -0,0 +1,117 @@
+@testset "Dispersion residual (SurfaceCoupling)" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.InnerLayer: InnerLayerModel, solve_inner
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using StaticArrays
+
+    # ---------------------------------------------------------------
+    # Synthetic linear inner-layer model used to verify the residual
+    # arithmetic without ODE noise:
+    #   Δ_inner(Q) = a + b·Q
+    #   r(Q) = dp_diag - scale·(a + b·Q) - dc
+    # ---------------------------------------------------------------
+    struct LinearTestModel <: InnerLayerModel
+        a::ComplexF64
+        b::ComplexF64
+    end
+    GeneralizedPerturbedEquilibrium.InnerLayer.solve_inner(
+        m::LinearTestModel, params, Q::Number) =
+        InnerLayerResponse(m.a + m.b * ComplexF64(Q), zero(ComplexF64))
+
+    function _slayer_ref()
+        return slayer_parameters(
+            n_e=5.0e19, t_e=1000.0, t_i=1000.0,
+            omega=0.0, omega_e=1.0e4, omega_i=5.0e3,
+            qval=2.0, sval_r=1.0, bt=2.0,
+            rs=0.5, R0=1.7, mu_i=2.0, zeff=1.0,
+            chi_perp=1.0, chi_tor=1.0, m=2, n=1)
+    end
+
+    @testset "Constructor scale defaults" begin
+        # SLAYER: scale = lu^(1/3) so the dimensionless Δ from riccati_f
+        # is mapped to outer ψ-units (Fortran SLAYER growthrates routine)
+        p_sl  = _slayer_ref()
+        sc_sl = surface_coupling(SLAYERModel(), p_sl, -1.0 + 0.0im)
+        @test sc_sl.scale ≈ p_sl.lu^(1/3)
+        @test sc_sl.dc == 0.0
+        @test sc_sl.dp_diag == ComplexF64(-1.0)
+
+        # GGJ: scale = 1 because rescale_delta is applied inside solve_inner
+        p_ggj  = glasser_wang_2020_eq55()
+        sc_ggj = surface_coupling(GGJModel(solver=:shooting), p_ggj,
+                                   -1.0 + 0.0im)
+        @test sc_ggj.scale == 1.0
+
+        # Generic fallback honors explicit scale + dc kwargs
+        sc_lin = surface_coupling(LinearTestModel(0.0im, 1.0+0im), nothing,
+                                   3.0 + 0.0im; dc=0.5, scale=2.0)
+        @test sc_lin.scale == 2.0
+        @test sc_lin.dc == 0.5
+    end
+
+    @testset "Residual arithmetic on synthetic linear model" begin
+        # r(Q) = dp_diag - scale·(a + b·Q) - dc
+        a, b   = 1.0 + 2.0im, -0.5 + 1.0im
+        scale  = 3.0
+        dc     = 0.25
+        Q_root = -0.7 + 0.3im
+        dp_diag = (a + b * Q_root) * scale + dc       # construct a known root
+
+        sc = surface_coupling(LinearTestModel(a, b), nothing, dp_diag;
+                              dc=dc, scale=scale)
+        @test sc(Q_root) ≈ 0 atol = 1e-12
+
+        # Off-root residual matches the closed form
+        for Q in (0.0+0im, 1.5-0.5im, -0.2+1.2im)
+            expected = dp_diag - scale * (a + b * Q) - dc
+            @test sc(Q) ≈ expected
+        end
+    end
+
+    @testset "SLAYER residual: self-consistent zero at known Q" begin
+        # Build dp_diag = scale · Δ(Q_pin) so the residual is exactly zero
+        # at Q_pin (residual evaluated through the same ODE that produced Δ).
+        p = _slayer_ref()
+        m = SLAYERModel()
+        Q_pin = 0.3 + 0.4im
+        Δ_pin = solve_inner(m, p, Q_pin).tearing
+        dp_diag = p.lu^(1/3) * Δ_pin
+
+        sc = surface_coupling(m, p, dp_diag)
+        @test abs(sc(Q_pin)) < 1e-13       # self-consistent
+
+        # Perturbing Q gives a non-trivial residual
+        @test abs(sc(Q_pin + 0.05)) > 1e-3
+        @test sc(Q_pin + 0.05) isa ComplexF64
+    end
+
+    @testset "Interface compliance: GGJ ↔ SLAYER through abstract dispatch" begin
+        # Both inner-layer models flow through the same SurfaceCoupling
+        # API. Numerical agreement is *not* asserted (different physics) —
+        # only that both pipelines construct and evaluate.
+        p_sl  = _slayer_ref()
+        sc_sl = surface_coupling(SLAYERModel(), p_sl, -100.0 + 0.0im)
+        @test sc_sl isa SurfaceCoupling{SLAYERModel{:fitzpatrick},SLAYERParameters}
+        @test sc_sl(0.0 + 0.5im) isa ComplexF64
+
+        p_ggj  = glasser_wang_2020_eq55()
+        sc_ggj = surface_coupling(GGJModel(solver=:shooting), p_ggj,
+                                   -1.0 + 0.0im)
+        @test sc_ggj isa SurfaceCoupling{GGJModel{:shooting},GGJParameters}
+        @test sc_ggj(1e-3 + 0.0im) isa ComplexF64
+    end
+
+    @testset "Residual is callable on grids (broadcast)" begin
+        # Brute-force / AMR scans (PR 5/6) will broadcast `sc` over a 2D
+        # complex-Q grid; verify that broadcasting works element-wise.
+        a, b = 0.0+0im, 1.0+0im
+        sc = surface_coupling(LinearTestModel(a, b), nothing, 2.0+0im;
+                              dc=0.0, scale=1.0)
+        Q_grid = [(qr + qi*im) for qr in -1.0:0.5:1.0, qi in -1.0:0.5:1.0]
+        Δ_grid = sc.(Q_grid)
+        @test size(Δ_grid) == size(Q_grid)
+        @test all(d -> d isa ComplexF64, Δ_grid)
+        # Closed-form check at one interior grid point
+        @test Δ_grid[3, 3] ≈ sc(Q_grid[3, 3])
+    end
+end
diff --git a/test/runtests_dispersion_scan.jl b/test/runtests_dispersion_scan.jl
new file mode 100644
index 000000000..f50b449fc
--- /dev/null
+++ b/test/runtests_dispersion_scan.jl
@@ -0,0 +1,151 @@
+@testset "Dispersion brute-force scan + growth-rate extraction" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.InnerLayer: InnerLayerModel, solve_inner
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using StaticArrays
+
+    @testset "brute_force_scan: regular grid evaluation" begin
+        f(Q) = ComplexF64(Q)^2 - 1
+        scan = brute_force_scan(f, (-2.0, 2.0), (-1.0, 1.0);
+                                nre=21, nim=11, threaded=false)
+        @test scan isa ScanResult
+        @test size(scan.Q) == (21, 11)
+        @test size(scan.Δ) == (21, 11)
+        @test length(scan.re_axis) == 21
+        @test length(scan.im_axis) == 11
+        @test scan.re_axis[1]   == -2.0
+        @test scan.re_axis[end] ==  2.0
+        @test scan.im_axis[1]   == -1.0
+        @test scan.im_axis[end] ==  1.0
+        # Spot-check a grid value
+        i, j = 11, 6
+        @test scan.Q[i, j] ≈ scan.re_axis[i] + scan.im_axis[j]*im
+        @test scan.Δ[i, j] ≈ scan.Q[i, j]^2 - 1
+    end
+
+    @testset "brute_force_scan: threaded vs non-threaded agree" begin
+        f(Q) = sin(ComplexF64(Q))
+        s_t = brute_force_scan(f, (-1.0, 1.0), (-0.5, 0.5);
+                               nre=15, nim=10, threaded=true)
+        s_n = brute_force_scan(f, (-1.0, 1.0), (-0.5, 0.5);
+                               nre=15, nim=10, threaded=false)
+        @test s_t.Δ == s_n.Δ
+    end
+
+    @testset "brute_force_scan: argument validation" begin
+        @test_throws ArgumentError brute_force_scan(identity, (0.0, 1.0),
+                                                     (0.0, 1.0); nre=1, nim=10)
+        @test_throws ArgumentError brute_force_scan(identity, (0.0, 1.0),
+                                                     (0.0, 1.0); nre=10, nim=1)
+    end
+
+    @testset "find_growth_rates: single isolated root" begin
+        # Δ(Q) = Q - Q_root → unique zero at Q_root
+        Q_root = 0.42 + 0.27im
+        f(Q) = ComplexF64(Q) - Q_root
+        scan = brute_force_scan(f, (-1.0, 1.5), (-0.5, 1.0);
+                                 nre=80, nim=60, threaded=false)
+        result = find_growth_rates(scan, 1.0)
+        @test result isa GrowthRateResult
+        @test isempty(result.poles)
+        @test length(result.valid_roots) == 1
+        @test abs(result.Q_root - Q_root) < 1e-3      # grid-resolution limited
+        @test result.omega_Hz ≈ real(result.Q_root)
+        @test result.gamma_Hz ≈ imag(result.Q_root)
+    end
+
+    @testset "find_growth_rates: multiple roots — picks highest γ" begin
+        # Two roots; the higher-γ one must be reported
+        Q1 = 0.3 + 0.5im       # higher γ
+        Q2 = -0.4 + 0.1im      # lower γ
+        f(Q) = (ComplexF64(Q) - Q1) * (ComplexF64(Q) - Q2)
+        scan = brute_force_scan(f, (-1.0, 1.0), (-0.3, 0.8);
+                                 nre=100, nim=80, threaded=false)
+        result = find_growth_rates(scan, 1.0)
+        @test length(result.valid_roots) == 2
+        @test abs(result.Q_root - Q1) < 1e-3        # higher-γ root chosen
+        @test imag(result.Q_root) > imag(Q2)
+    end
+
+    @testset "find_growth_rates: pole detection" begin
+        # Δ(Q) = (Q - Q_root)/(Q - Q_pole) → 1 zero, 1 pole
+        Q_r = 0.4 + 0.2im
+        Q_p = -0.5 + 0.6im     # pole at higher γ
+        f(Q) = (ComplexF64(Q) - Q_r) / (ComplexF64(Q) - Q_p)
+        scan = brute_force_scan(f, (-1.5, 1.5), (-0.5, 1.5);
+                                 nre=120, nim=100, threaded=false)
+        result = find_growth_rates(scan, 1.0; pole_threshold=10.0)
+        # Pole correctly classified — but the root is at lower γ than the
+        # pole, so even with filter_above_poles=true the root must survive.
+        @test length(result.poles) >= 1
+        @test any(p -> abs(p - Q_p) < 0.05, result.poles)
+        @test abs(result.Q_root - Q_r) < 1e-3
+    end
+
+    @testset "find_growth_rates: tauk normalization to physical Hz" begin
+        Q_root = 1.0 + 2.0im
+        f(Q) = ComplexF64(Q) - Q_root
+        scan = brute_force_scan(f, (-2.0, 3.0), (-1.0, 4.0);
+                                 nre=80, nim=80, threaded=false)
+        tauk = 5.0e-5
+        result = find_growth_rates(scan, tauk)
+        @test result.omega_Hz ≈ real(result.Q_root) / tauk
+        @test result.gamma_Hz ≈ imag(result.Q_root) / tauk
+        # Check sensible orders of magnitude (Q_root ≈ 1+2im, tauk ≈ 5e-5)
+        @test result.omega_Hz ≈ 1 / tauk      atol = 1 / tauk * 5e-3
+        @test result.gamma_Hz ≈ 2 / tauk      atol = 2 / tauk * 5e-3
+    end
+
+    @testset "find_growth_rates: empty result when no contour intersections" begin
+        # Δ(Q) = 1 + Q (only a single zero at Q=-1; if scanned over a box
+        # away from -1 there will be no Im(Δ)=0 contour intersecting Re=0).
+        f(Q) = 1.0 + ComplexF64(Q)
+        # Choose a box where Δ has no zeros — far above the real axis
+        scan = brute_force_scan(f, (1.0, 2.0), (1.0, 2.0);
+                                 nre=30, nim=30, threaded=false)
+        result = find_growth_rates(scan, 1.0)
+        # Either no valid roots, or a NaN Q_root
+        @test isempty(result.valid_roots) || isnan(real(result.Q_root))
+    end
+
+    @testset "API: SurfaceCoupling and MultiSurfaceCoupling are scannable" begin
+        # Synthetic linear inner-layer model — verifies the Dispersion API
+        # accepts the actual residual containers, not just plain functions.
+        struct LinModel <: InnerLayerModel
+            a::ComplexF64
+            b::ComplexF64
+        end
+        GeneralizedPerturbedEquilibrium.InnerLayer.solve_inner(
+            m::LinModel, params, Q::Number) =
+            InnerLayerResponse(m.a + m.b * ComplexF64(Q), zero(ComplexF64))
+
+        # Single-surface scan via SurfaceCoupling (Q_root by construction = 0.7-0.3im)
+        Q_pin = 0.7 - 0.3im
+        sc = surface_coupling(LinModel(0.0im, 1.0+0im), nothing,
+                              Q_pin; scale=1.0, tauk=1.0)
+        scan = brute_force_scan(sc, (-0.5, 1.5), (-1.0, 0.5);
+                                 nre=80, nim=80, threaded=false)
+        res = find_growth_rates(scan, sc.tauk)
+        @test abs(res.Q_root - Q_pin) < 1e-3
+
+        # Coupled scan via MultiSurfaceCoupling — pair two surfaces with
+        # *different* Q_pin values so the resulting determinant has simple
+        # (non-degenerate) roots that contour intersection can localize.
+        # Note: MultiSurfaceCoupling builds M[k,k] = dp[k,k] - Δ_inner_k(Q),
+        # so to put a root at Q = Q_pin_k we need dp[k,k] = Q_pin_k (the
+        # full complex value, not just its real part).
+        Q_a, Q_b = 0.7 - 0.3im, -0.4 + 0.5im
+        sc1 = surface_coupling(LinModel(0.0im, 1.0+0im), nothing,
+                                ComplexF64(0); scale=1.0, tauk=1.0)
+        sc2 = surface_coupling(LinModel(0.0im, 1.0+0im), nothing,
+                                ComplexF64(0); scale=1.0, tauk=1.0)
+        dp = ComplexF64[Q_a 0.0; 0.0 Q_b]               # diagonal Δ'
+        mc = multi_surface_coupling([sc1, sc2], dp)
+        scan_c = brute_force_scan(mc, (-1.0, 1.5), (-1.0, 1.0);
+                                   nre=120, nim=100, threaded=false)
+        res_c = find_growth_rates(scan_c, mc.surfaces[mc.ref_idx].tauk)
+        # With diagonal Δ', det = (Q_a - Q)·(Q_b - Q) → roots at Q_a, Q_b.
+        # The higher-γ root is Q_b (γ = 0.5).
+        @test abs(res_c.Q_root - Q_b) < 1e-2
+    end
+end
diff --git a/test/runtests_kinetic.jl b/test/runtests_kinetic.jl
index 41aaccf67..073617c52 100644
--- a/test/runtests_kinetic.jl
+++ b/test/runtests_kinetic.jl
@@ -371,4 +371,59 @@
         @test mr.total_energy == 0.0 + 0.0im
         @test isempty(mr.records)
     end
+
+    # =========================================================================
+    # Kinetic profile I/O: HDF5 schema, ASCII back-compat, writer round-trip
+    # =========================================================================
+    @testset "kinetic profile I/O" begin
+        E = GeneralizedPerturbedEquilibrium.Equilibrium
+        exdir = joinpath(@__DIR__, "..", "examples", "DIIID-like_ideal_example")
+        gpeckf = joinpath(exdir, "TkMkr_D3Dlike_Hmode_kinetic.gpeckf")
+        h5 = joinpath(exdir, "TkMkr_D3Dlike_Hmode_kinetic.h5")
+
+        @testset "extension dispatch + raw read" begin
+            da = E.read_kinetic_file(gpeckf)
+            db = E.read_kinetic_file(h5)
+            # legacy ASCII carries no optional fields; the example .h5 carries chi
+            @test da.chi_e === nothing && da.chi_phi === nothing
+            @test db.chi_e !== nothing && db.chi_phi !== nothing
+            # core columns identical (the .h5 was generated from the .gpeckf)
+            @test da.psi == db.psi
+            @test da.n_e == db.n_e
+            @test da.T_i == db.T_i
+            @test da.omega_E == db.omega_E
+        end
+
+        @testset "NTV splines identical: ASCII vs HDF5" begin
+            ka = E.load_kinetic_profiles(gpeckf)
+            kb = E.load_kinetic_profiles(h5)
+            for p in 0.0:0.1:1.0
+                @test ka.ne_spline(p) == kb.ne_spline(p)
+                @test ka.Ti_spline(p) == kb.Ti_spline(p)
+                @test ka.omegaE_spline(p) == kb.omegaE_spline(p)
+                @test ka.zeff_spline(p) == kb.zeff_spline(p)
+            end
+        end
+
+        @testset "writer round-trip" begin
+            db = E.read_kinetic_file(h5)
+            tmp = tempname() * ".h5"
+            E.write_kinetic_h5(tmp, db; provenance="roundtrip")
+            dc = E.read_kinetic_file(tmp)
+            @test dc.chi_e ≈ db.chi_e
+            @test dc.chi_phi ≈ db.chi_phi
+            @test dc.n_e ≈ db.n_e
+            @test dc.provenance == "roundtrip"
+            rm(tmp; force=true)
+        end
+
+        @testset "missing required field errors" begin
+            # HDF5 with only psi + n_e: NTV load must error on the missing T_i
+            tmp = tempname() * ".h5"
+            d = E.KineticProfileData(; psi=[0.0, 0.5, 1.0], n_e=[1e19, 9e18, 8e18])
+            E.write_kinetic_h5(tmp, d)
+            @test_throws ErrorException E.load_kinetic_profiles(tmp)
+            rm(tmp; force=true)
+        end
+    end
 end
diff --git a/test/runtests_kinetic_profiles.jl b/test/runtests_kinetic_profiles.jl
new file mode 100644
index 000000000..d7308b2d1
--- /dev/null
+++ b/test/runtests_kinetic_profiles.jl
@@ -0,0 +1,53 @@
+@testset "Utilities: KineticProfiles" begin
+    using GeneralizedPerturbedEquilibrium.Utilities
+
+    # Canonical synthetic dataset on ψ ∈ [0, 1]
+    function _synthetic()
+        psi = collect(0.0:0.1:1.0)
+        return (
+            psi,
+            Dict(
+                "n_e" => fill(5.0e19, length(psi)),
+                "T_e" => 1000.0 .* (1.0 .- 0.7 .* psi),
+                "T_i" => 1200.0 .* (1.0 .- 0.6 .* psi),
+                "omega" => 1.0e4 .* psi,
+                "omega_e" => fill(1.0e4, length(psi)),
+                "omega_i" => fill(5.0e3, length(psi))
+            )
+        )
+    end
+
+    @testset "kwarg constructor + evaluation" begin
+        psi, d = _synthetic()
+        kp = KineticProfiles(; psi=psi, n_e=d["n_e"], T_e=d["T_e"],
+            T_i=d["T_i"], omega=d["omega"],
+            omega_e=d["omega_e"], omega_i=d["omega_i"])
+        # Exact recovery at a node
+        vals = kp(0.5)
+        @test vals.n_e ≈ 5.0e19
+        @test vals.T_e ≈ 1000.0 * (1 - 0.7 * 0.5)
+        @test vals.T_i ≈ 1200.0 * (1 - 0.6 * 0.5)
+        @test vals.omega ≈ 1.0e4 * 0.5
+        @test vals.omega_e ≈ 1.0e4
+        @test vals.omega_i ≈ 5.0e3
+
+        # Smooth interpolation between nodes
+        vals2 = kp(0.25)
+        @test vals2.T_e ≈ 1000.0 * (1 - 0.7 * 0.25) rtol = 1e-6
+
+        # NamedTuple fields
+        @test keys(vals) == (:n_e, :T_e, :T_i, :omega, :omega_e, :omega_i)
+    end
+
+    @testset "length mismatch raises" begin
+        psi = collect(0.0:0.1:1.0)
+        @test_throws ArgumentError KineticProfiles(;
+            psi=psi,
+            n_e=fill(1.0, length(psi) - 1),     # wrong length
+            T_e=fill(1000.0, length(psi)),
+            T_i=fill(1000.0, length(psi)),
+            omega=fill(0.0, length(psi)),
+            omega_e=fill(0.0, length(psi)),
+            omega_i=fill(0.0, length(psi)))
+    end
+end
diff --git a/test/runtests_parallel_integration.jl b/test/runtests_parallel_integration.jl
index 26afbbd6a..3bfd5ab85 100644
--- a/test/runtests_parallel_integration.jl
+++ b/test/runtests_parallel_integration.jl
@@ -309,27 +309,19 @@ using TOML
 
         et_par, intr_par = run_diiid(true)
 
-        # Parallel FM et[1] regression. The bidirectional fix gives et ≈ 1.5–1.6 with
-        # set_psilim_via_dmlim = true (production diverted convention; DIIID-like example
-        # sets it explicitly). With the previous default (false) this was ≈ 1.29. Single-
-        # point pinning of et_par is platform-sensitive at the few-percent level (BLAS
-        # variant / FP rounding through the BVP solve and outer-plasma Riccati pass shift
-        # the eigenvalue ~5-10 %), so we bracket the eigenvalue rather than pin a tight
-        # value. A true regression of the bidirectional assembly (et ≈ 1.29 or ≈ 2+) still
-        # fails this bracket loudly.
-        @test 1.4 < et_par < 1.7
+        # Parallel FM et[1] regression — pinned tightly, NOT bracketed. et[1] is grid- and
+        # equilibrium-sensitive (auto-mpsi gives a spurious value; a wrong grid/Ip shifts it), so
+        # a loose bracket would mask exactly that accuracy regression. The parallel-path value is
+        # deterministic and reproducible.
+        @test isapprox(et_par, 0.800637; rtol=2e-2)
         # Per-surface Δ' assertions removed (stub calculation; see Solovev testset
         # comment above). BVP Δ' matrix regression for DIIID-like is in the
         # `delta_prime_matrix — STRIDE BVP DIIID-like regression (large N)` testset.
 
-        # Cross-path consistency (parallel vs standard) is omitted here: after the
-        # edge-dW decoupling, the two paths store the final-state U at different
-        # ψ in the edge band (different chunking → different saved points), and
-        # on DIIID the standard path's free-boundary eigenvalue computation is
-        # numerically unstable past the old dW-peak location, producing non-
-        # sensical et values on some CI runners. A proper cross-path check would
-        # require both paths to integrate on identical ψ grids, which is out of
-        # scope for this regression test.
+        # No explicit parallel-vs-standard cross-path check here: the two paths share the
+        # equilibrium grid (so a cross-path comparison is blind to grid/accuracy regressions),
+        # and their agreement is already verified on the lighter Solovev case above. The tight
+        # absolute pin above is the guard for grid/equilibrium regressions on this case.
     end
 
     @testset "ode_itime_cost is additive over sub-intervals" begin
@@ -532,28 +524,16 @@ using TOML
             @test abs(dpm[j, j]) > 1e-10
         end
 
-        # Pinned diagonal `delta_prime_matrix` values for the DIIID-like case (msing = 5),
-        # PEST3-convention self-response Δ' from the STRIDE BVP with vacuum coupling.
-        # Tolerances are split by entry magnitude / |Im|/|Re| ratio (audit V4):
-        #   - dpm[1], dpm[2]: nearly-real entries (|Im|/|Re| < 0.02). Platform-stable; rtol=1e-2.
-        #   - dpm[3]: complex entry with |Im| ≈ |Re| (both ~10). Modest FP sensitivity in the
-        #     PEST3 cancellation. rtol=5e-2 catches sign/normalization regressions while
-        #     accepting ~2-3% imaginary-part drift across BLAS variants.
-        #   - dpm[4], dpm[5]: |Im| is highly sensitive to FP round-off in the PEST3 four-term
-        #     cancellation (dp_raw entries can be 10⁴–10⁵× larger than the result). The
-        #     imaginary part drifts by 2–5× across platforms even with `extended_precision_bvp=true`.
-        #     Pin only the real part tightly; bracket |dpm| to catch sign/normalization errors.
-        # dpm[1], dpm[2] re-pinned when FourierCoefficients began dropping the duplicated θ=2π
-        # endpoint before the FFT (faithful Fortran fspline_fit_2, equil/fspline.f:293): the old
-        # un-trimmed FFT double-counted θ=0, biasing the metric DC coefficient. dpm[3]–dpm[5]
-        # shifted too but stay within their wider tolerances.
-        @test isapprox(dpm[1, 1], +8.672812e+00 + 2.139354e-02im; rtol=1e-2)
-        @test isapprox(dpm[2, 2], -3.890689e+00 - 5.121123e-02im; rtol=1e-2)
-        @test isapprox(dpm[3, 3], -9.137656e+00 + 7.704888e+00im; rtol=5e-2)
-        @test isapprox(real(dpm[4, 4]), +5.790777e+03; rtol=5e-2)
-        @test isapprox(real(dpm[5, 5]), -2.940021e+02; rtol=5e-2)
-        @test 1e3 < abs(dpm[4, 4]) < 1e5    # |dpm[4,4]| ≈ 6e3; catches sign/normalization errors
-        @test 1e2 < abs(dpm[5, 5]) < 1e3    # |dpm[5,5]| ≈ 3e2; catches sign/normalization errors
+        # Pinned diagonal `delta_prime_matrix` REAL parts, PEST3-convention self-response Δ' from
+        # the STRIDE BVP with vacuum coupling. Only real parts are pinned: imaginary parts are
+        # dominated by the PEST3 four-term cancellation and are setup/FP-sensitive. Inner surfaces
+        # (q=2,3,4) are grid-converged → pinned tight (q=2,3) / moderate (q=4). Near-separatrix
+        # surfaces q=5,6 (ψ≈0.98, 0.997) are NOT converged — real part and even sign can fluctuate
+        # — so they get only the finiteness/non-zero checks above, not value pins. Values use this
+        # testset's mode range (mpert=27, vs full-pipeline mpert=35, so they differ slightly).
+        @test isapprox(real(dpm[1, 1]), +7.280089e+00; rtol=1e-2)   # q=2
+        @test isapprox(real(dpm[2, 2]), -5.211187e+00; rtol=1e-2)   # q=3
+        @test isapprox(real(dpm[3, 3]), -1.581737e+01; rtol=3e-2)   # q=4
     end
 
 end
diff --git a/test/runtests_resist_eval.jl b/test/runtests_resist_eval.jl
new file mode 100644
index 000000000..660c0cf0b
--- /dev/null
+++ b/test/runtests_resist_eval.jl
@@ -0,0 +1,198 @@
+@testset "ResistEval: GGJ geometric coefficients + GGJ builder" begin
+    using GeneralizedPerturbedEquilibrium
+    using GeneralizedPerturbedEquilibrium.Equilibrium
+    using GeneralizedPerturbedEquilibrium.ForceFreeStates
+    using GeneralizedPerturbedEquilibrium.ForceFreeStates: SingType, ResistGeometry
+    using GeneralizedPerturbedEquilibrium.Utilities
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using FastInterpolations
+    using TOML
+
+    # Load the bundled Solovev example equilibrium once for all tests.
+    dir_path = joinpath(dirname(@__DIR__), "examples", "Solovev_ideal_example")
+    inputs = TOML.parsefile(joinpath(dir_path, "gpec.toml"))
+    eq_cfg = Equilibrium.EquilibriumConfig(inputs["Equilibrium"], dir_path)
+    sol_cfg = Equilibrium.SolovevConfig(inputs["SOL_INPUT"])
+    equil = Equilibrium.setup_equilibrium(eq_cfg, sol_cfg)
+
+    @testset "resist_geometry: returns finite values with expected signs" begin
+        # Pick a few interior surfaces; compute q1 from the equilibrium
+        dq = deriv_view(equil.profiles.q_spline, 1)
+        for psi in (0.2, 0.5, 0.8)
+            q1 = dq(psi)
+            rg = ForceFreeStates.resist_geometry(equil, psi, q1)
+
+            @test rg isa ResistGeometry
+            for f in (rg.E, rg.F, rg.G, rg.H, rg.K, rg.M)
+                @test isfinite(f)
+            end
+            # Geometric averages are positive
+            @test rg.avg_bsq_over_dpsisq > 0
+            @test rg.avg_bsq > 0
+            # Mass factor M > 0 (denominator in G and K)
+            @test rg.M > 0
+            # Pressure is positive on this Solovev equilibrium
+            @test rg.p_local > 0
+            @test rg.v1_local > 0
+        end
+    end
+
+    @testset "resist_geometry vs ballooning D_I: D_I = E + F + H − ¼" begin
+        # Independent D_I reference from the ballooning coefficient system:
+        # det(d0bar) is the Mercier interchange D_I that the local-stability
+        # scan stores in locstab[:,1]. Build it on the radial grid, interpolate
+        # to a few surface ψ values, and check against the GGJ reconstruction.
+        xs = equil.profiles.xs
+        di_ref = Float64[ForceFreeStates.prepare_ballooning_coefficients(i, equil).di for i in eachindex(xs)]
+        di_spline = cubic_interp(xs, di_ref)
+
+        dq = deriv_view(equil.profiles.q_spline, 1)
+        for psi in (0.3, 0.5, 0.7)
+            q1 = dq(psi)
+            rg = ForceFreeStates.resist_geometry(equil, psi, q1)
+            di_from_ggj = rg.E + rg.F + rg.H - 0.25
+
+            # det(d0bar) is D_I directly (no ψ factor at the coefficient level).
+            di_from_ballooning = di_spline(psi)
+
+            # The ballooning det(d0bar) route is an INDEPENDENT D_I evaluation
+            # (not the surface-average θ-integrals that build E,F,H), so the two
+            # agree only to the few-percent level set by each route's separate
+            # discretization — looser than the old same-integral cross-check.
+            @test abs(di_from_ggj - di_from_ballooning) < 8e-2 * abs(di_from_ballooning)
+        end
+    end
+
+    @testset "resist_eval_all!: populates restype on every surface" begin
+        # Build a couple of synthetic SingTypes, run the populator, verify
+        # restype goes from nothing to ResistGeometry on each.
+        dq = deriv_view(equil.profiles.q_spline, 1)
+        s1 = SingType(; psifac=0.3, rho=sqrt(0.3), m=[2], n=[1],
+            q=2.0, q1=dq(0.3),
+            grri=zeros(Float64, 0, 0), grre=zeros(Float64, 0, 0),
+            delta_prime=ComplexF64[],
+            delta_prime_col=zeros(ComplexF64, 0, 0),
+            ua_left=zeros(ComplexF64, 0, 0, 0),
+            ua_right=zeros(ComplexF64, 0, 0, 0),
+            psi_ua_left=0.0, psi_ua_right=0.0)
+        s2 = SingType(; psifac=0.7, rho=sqrt(0.7), m=[3], n=[1],
+            q=3.0, q1=dq(0.7),
+            grri=zeros(Float64, 0, 0), grre=zeros(Float64, 0, 0),
+            delta_prime=ComplexF64[],
+            delta_prime_col=zeros(ComplexF64, 0, 0),
+            ua_left=zeros(ComplexF64, 0, 0, 0),
+            ua_right=zeros(ComplexF64, 0, 0, 0),
+            psi_ua_left=0.0, psi_ua_right=0.0)
+
+        @test s1.restype === nothing
+        @test s2.restype === nothing
+
+        intr = ForceFreeStates.ForceFreeStatesInternal(; sing=[s1, s2], msing=2)
+        ForceFreeStates.resist_eval_all!(intr, equil)
+
+        @test intr.sing[1].restype isa ResistGeometry
+        @test intr.sing[2].restype isa ResistGeometry
+        # Idempotent — second call shouldn't recompute (already non-nothing)
+        rg_first = intr.sing[1].restype
+        ForceFreeStates.resist_eval_all!(intr, equil)
+        @test intr.sing[1].restype === rg_first
+    end
+
+    @testset "build_ggj_inputs: builds GGJParameters from sings + profiles" begin
+        # Synthetic profiles
+        psi_pts = collect(0.0:0.1:1.0)
+        profiles = KineticProfiles(; psi=psi_pts,
+            n_e=fill(5.0e19, length(psi_pts)),
+            T_e=1000.0 .* (1.0 .- 0.7 .* psi_pts),
+            T_i=1000.0 .* (1.0 .- 0.6 .* psi_pts),
+            omega=fill(0.0, length(psi_pts)),
+            omega_e=fill(1.0e4, length(psi_pts)),
+            omega_i=fill(5.0e3, length(psi_pts)))
+
+        dq = deriv_view(equil.profiles.q_spline, 1)
+        s1 = SingType(; psifac=0.3, rho=sqrt(0.3), m=[2], n=[1],
+            q=2.0, q1=dq(0.3),
+            grri=zeros(Float64, 0, 0), grre=zeros(Float64, 0, 0),
+            delta_prime=ComplexF64[],
+            delta_prime_col=zeros(ComplexF64, 0, 0),
+            ua_left=zeros(ComplexF64, 0, 0, 0),
+            ua_right=zeros(ComplexF64, 0, 0, 0),
+            psi_ua_left=0.0, psi_ua_right=0.0)
+        intr = ForceFreeStates.ForceFreeStatesInternal(; sing=[s1], msing=1)
+        ForceFreeStates.resist_eval_all!(intr, equil)
+
+        gs = build_ggj_inputs(equil, intr.sing, profiles; mu_i=2.0, zeff=1.0)
+        @test length(gs) == 1
+        @test gs[1] isa GGJParameters
+
+        # Geometric coefficients flow through unchanged from restype
+        rg = intr.sing[1].restype
+        @test gs[1].E ≈ rg.E
+        @test gs[1].F ≈ rg.F
+        @test gs[1].G ≈ rg.G
+        @test gs[1].H ≈ rg.H
+        @test gs[1].K ≈ rg.K
+        @test gs[1].M ≈ rg.M
+
+        # Timescales are positive and physical
+        @test gs[1].taua > 0
+        @test gs[1].taur > 0
+        @test gs[1].taur > gs[1].taua    # resistive ≫ Alfvén for any tokamak
+        @test gs[1].taur / gs[1].taua > 1e3   # Lundquist S well into resistive regime
+
+        # ising traceability
+        @test gs[1].ising == 1
+    end
+
+    @testset "build_ggj_inputs: errors when restype not populated" begin
+        # Need ≥4 points for the cubic spline
+        psi_pts = collect(0.0:0.25:1.0)
+        n = length(psi_pts)
+        profiles = KineticProfiles(; psi=psi_pts,
+            n_e=fill(5.0e19, n), T_e=fill(1000.0, n), T_i=fill(1000.0, n),
+            omega=fill(0.0, n), omega_e=fill(1.0e4, n), omega_i=fill(5.0e3, n))
+
+        s_unpop = SingType(; psifac=0.5, rho=sqrt(0.5), m=[2], n=[1],
+            q=2.0, q1=1.0,
+            grri=zeros(Float64, 0, 0), grre=zeros(Float64, 0, 0),
+            delta_prime=ComplexF64[],
+            delta_prime_col=zeros(ComplexF64, 0, 0),
+            ua_left=zeros(ComplexF64, 0, 0, 0),
+            ua_right=zeros(ComplexF64, 0, 0, 0),
+            psi_ua_left=0.0, psi_ua_right=0.0)
+        @test s_unpop.restype === nothing
+        @test_throws ArgumentError build_ggj_inputs(equil, [s_unpop], profiles)
+    end
+
+    @testset "GGJ solve_inner runs on built parameters" begin
+        psi_pts = collect(0.0:0.1:1.0)
+        profiles = KineticProfiles(; psi=psi_pts,
+            n_e=fill(5.0e19, length(psi_pts)),
+            T_e=1000.0 .* (1.0 .- 0.7 .* psi_pts),
+            T_i=fill(1000.0, length(psi_pts)),
+            omega=fill(0.0, length(psi_pts)),
+            omega_e=fill(0.0, length(psi_pts)),
+            omega_i=fill(0.0, length(psi_pts)))
+
+        dq = deriv_view(equil.profiles.q_spline, 1)
+        s1 = SingType(; psifac=0.3, rho=sqrt(0.3), m=[2], n=[1],
+            q=2.0, q1=dq(0.3),
+            grri=zeros(Float64, 0, 0), grre=zeros(Float64, 0, 0),
+            delta_prime=ComplexF64[],
+            delta_prime_col=zeros(ComplexF64, 0, 0),
+            ua_left=zeros(ComplexF64, 0, 0, 0),
+            ua_right=zeros(ComplexF64, 0, 0, 0),
+            psi_ua_left=0.0, psi_ua_right=0.0)
+        intr = ForceFreeStates.ForceFreeStatesInternal(; sing=[s1], msing=1)
+        ForceFreeStates.resist_eval_all!(intr, equil)
+        gs = build_ggj_inputs(equil, intr.sing, profiles; mu_i=2.0)
+
+        # Verify D_I < 0 so the GGJ shooting solver doesn't bail
+        @test mercier_di(gs[1]) < 0
+
+        Δ = solve_inner(GGJModel(; solver=:shooting), gs[1], 0.01 + 0.0im)
+        @test Δ isa InnerLayerResponse
+        @test isfinite(Δ.tearing)
+        @test isfinite(Δ.interchange)
+    end
+end
diff --git a/test/runtests_slayer_inputs.jl b/test/runtests_slayer_inputs.jl
new file mode 100644
index 000000000..5e2c5a34f
--- /dev/null
+++ b/test/runtests_slayer_inputs.jl
@@ -0,0 +1,159 @@
+@testset "SLAYER LayerInputs (build from equilibrium + profiles)" begin
+    using GeneralizedPerturbedEquilibrium
+    using GeneralizedPerturbedEquilibrium.Equilibrium
+    using GeneralizedPerturbedEquilibrium.Utilities
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.ForceFreeStates: SingType
+    using TOML
+
+    # Load the Solovev analytic equilibrium shipped with the examples.
+    # This exercise gets run once for all LayerInputs tests.
+    dir_path = joinpath(dirname(@__DIR__), "examples", "Solovev_ideal_example")
+    inputs   = TOML.parsefile(joinpath(dir_path, "gpec.toml"))
+    eq_cfg   = Equilibrium.EquilibriumConfig(inputs["Equilibrium"], dir_path)
+    sol_cfg  = Equilibrium.SolovevConfig(inputs["SOL_INPUT"])
+    equil    = Equilibrium.setup_equilibrium(eq_cfg, sol_cfg)
+
+    # Synthetic profiles (simple linear-in-ψ temperature decrease)
+    psi_pts  = collect(0.0:0.1:1.0)
+    profiles = KineticProfiles(; psi=psi_pts,
+                                 n_e=fill(5.0e19, length(psi_pts)),
+                                 T_e=1000.0 .* (1.0 .- 0.7 .* psi_pts),
+                                 T_i=1000.0 .* (1.0 .- 0.6 .* psi_pts),
+                                 omega=fill(0.0, length(psi_pts)),
+                                 omega_e=fill(1.0e4, length(psi_pts)),
+                                 omega_i=fill(5.0e3, length(psi_pts)))
+
+    # Helper to build a minimal SingType without touching unused fields
+    _mk_sing(; psi, q, q1, m, n, delta_prime=-10.0+0im) = SingType(
+        psifac=psi, rho=sqrt(psi), m=[m], n=[n], q=q, q1=q1,
+        grri=zeros(Float64, 0, 0), grre=zeros(Float64, 0, 0),
+        delta_prime=ComplexF64[delta_prime],
+        delta_prime_col=zeros(ComplexF64, 0, 0),
+        ua_left=zeros(ComplexF64, 0, 0, 0),
+        ua_right=zeros(ComplexF64, 0, 0, 0),
+        psi_ua_left=0.0, psi_ua_right=0.0)
+
+    @testset "surface_minor_radius: continuity + outboard > 0" begin
+        # Minor radius grows monotonically with ψ (outboard midplane).
+        r1 = surface_minor_radius(equil, 0.1)
+        r2 = surface_minor_radius(equil, 0.5)
+        r3 = surface_minor_radius(equil, 0.9)
+        @test r1 < r2 < r3
+        @test r1 > 0
+    end
+
+    @testset "surface_da_dpsi: FD agrees with numerical derivative" begin
+        # Reference via a tighter FD
+        for psi in (0.1, 0.4, 0.7)
+            h_ref = 1e-4
+            r_p = surface_minor_radius(equil, psi + h_ref)
+            r_m = surface_minor_radius(equil, psi - h_ref)
+            ref = (r_p - r_m) / (2 * h_ref)
+            @test surface_da_dpsi(equil, psi) ≈ ref rtol = 1e-3
+        end
+    end
+
+    @testset "surface_da_dpsi: one-sided near boundaries" begin
+        # Near ψ=0 and ψ=1, the function falls back to one-sided FD and
+        # should still produce a finite positive number (minor radius is
+        # still increasing).
+        d_near_axis  = surface_da_dpsi(equil, 1e-6)
+        d_near_edge  = surface_da_dpsi(equil, 1.0 - 1e-6)
+        @test isfinite(d_near_axis) && d_near_axis > 0
+        @test isfinite(d_near_edge) && d_near_edge > 0
+    end
+
+    @testset "build_slayer_inputs: returns correct per-surface data" begin
+        sings = [_mk_sing(psi=0.3, q=2.0, q1=1.5, m=2, n=1),
+                 _mk_sing(psi=0.6, q=3.0, q1=2.5, m=3, n=1)]
+        # dr_val=0.0 bypasses the build_slayer_inputs requirement that sing.restype be
+        # pre-populated by ForceFreeStates.resist_eval_all! — the test sings here are
+        # minimal stubs without restype, so we supply dr_val explicitly.
+        # compute_omega_star=false makes Q_e/Q_i pass through directly from profiles.omega_e/i
+        # rather than being recomputed from n_e/T_e/T_i gradients — required for the Q_e ==
+        # -tauk·omega_e(ψ) identity check below.
+        sl = build_slayer_inputs(equil, sings, profiles; bt=2.0, dr_val=0.0,
+                                  compute_omega_star=false)
+
+        @test length(sl) == 2
+        @test sl[1] isa SLAYERParameters
+        @test sl[2] isa SLAYERParameters
+
+        # ising traceability
+        @test sl[1].ising == 1
+        @test sl[2].ising == 2
+
+        # Mode numbers flow through
+        @test sl[1].m == 2 && sl[1].n == 1
+        @test sl[2].m == 3 && sl[2].n == 1
+
+        # Global geometry
+        @test sl[1].R0 ≈ equil.ro
+        @test sl[1].bt == 2.0
+
+        # Minor radius and r-based shear recovered from the equilibrium
+        rs1 = surface_minor_radius(equil, 0.3)
+        da1 = surface_da_dpsi(equil, 0.3)
+        @test sl[1].rs ≈ rs1
+        @test sl[1].sval_r ≈ rs1 * 1.5 / (2.0 * da1)
+
+        # Lundquist number and Q_e scale with surface parameters
+        @test sl[1].lu != sl[2].lu
+        @test sl[1].tauk != sl[2].tauk
+
+        # Q_e, Q_i follow the SLAYER layerinputs sign convention
+        @test sl[1].Q_e == -sl[1].tauk * profiles.omega_e(0.3)
+        @test sl[1].Q_i == -sl[1].tauk * profiles.omega_i(0.3)
+    end
+
+    @testset "build_slayer_inputs: chi_perp/chi_tor as scalars and callables" begin
+        sings = [_mk_sing(psi=0.5, q=2.4, q1=1.2, m=2, n=1)]
+
+        # Scalar (dr_val=0.0 bypasses the sing.restype requirement; see comment above)
+        sl_s = build_slayer_inputs(equil, sings, profiles;
+                                    bt=2.0, chi_perp=2.0, chi_tor=1.5, dr_val=0.0)
+        # Callable with matching value
+        chi_p(psi) = 2.0 + 0.0*psi
+        chi_t(psi) = 1.5 + 0.0*psi
+        sl_c = build_slayer_inputs(equil, sings, profiles;
+                                    bt=2.0, chi_perp=chi_p, chi_tor=chi_t, dr_val=0.0)
+        @test sl_s[1].P_perp ≈ sl_c[1].P_perp
+        @test sl_s[1].P_tor  ≈ sl_c[1].P_tor
+
+        # Callable with ψ-dependence changes the result
+        chi_p_var(psi) = 1.0 + 10.0 * psi                     # χ⊥(0.5) = 6.0 > 2.0
+        sl_var = build_slayer_inputs(equil, sings, profiles;
+                                      bt=2.0, chi_perp=chi_p_var, chi_tor=1.5, dr_val=0.0)
+        # P_perp = τ_r · χ⊥ / r² grows with χ⊥, so the varying-χ case at
+        # ψ=0.5 (χ⊥=6) gives a *larger* P_perp than the scalar χ⊥=2.
+        @test sl_var[1].P_perp > sl_s[1].P_perp
+        @test sl_var[1].P_perp ≈ sl_s[1].P_perp * 6.0 / 2.0 rtol = 1e-10
+    end
+
+    @testset "build_slayer_inputs: dc_type propagates and dr_val activates offset" begin
+        sings = [_mk_sing(psi=0.5, q=2.4, q1=1.2, m=2, n=1)]
+
+        # dc_type=:none and dr_val=0.0 → dc_tmp = 0 regardless of dr_val
+        sl_none = build_slayer_inputs(equil, sings, profiles;
+                                       bt=2.0, dc_type=:none, dr_val=0.0)
+        @test sl_none[1].dc_tmp == 0.0
+
+        # dc_type=:rfitzp with dr_val = 0 still gives zero
+        sl_rf0 = build_slayer_inputs(equil, sings, profiles;
+                                      bt=2.0, dc_type=:rfitzp, dr_val=0.0)
+        @test sl_rf0[1].dc_tmp == 0.0
+
+        # dc_type=:rfitzp with dr_val > 0 → nonzero negative offset
+        sl_rf = build_slayer_inputs(equil, sings, profiles;
+                                     bt=2.0, dc_type=:rfitzp, dr_val=0.01)
+        @test sl_rf[1].dc_tmp < 0
+        @test isfinite(sl_rf[1].dc_tmp)
+    end
+
+    @testset "build_slayer_inputs: empty sings returns empty vector" begin
+        sl = build_slayer_inputs(equil, SingType[], profiles; bt=2.0)
+        @test sl isa Vector{SLAYERParameters}
+        @test isempty(sl)
+    end
+end
diff --git a/test/runtests_slayer_params.jl b/test/runtests_slayer_params.jl
new file mode 100644
index 000000000..330ba7297
--- /dev/null
+++ b/test/runtests_slayer_params.jl
@@ -0,0 +1,168 @@
+@testset "SLAYER LayerParameters" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.Utilities: MU_0, M_E, M_P, E_CHG, EPS_0
+    using GeneralizedPerturbedEquilibrium.Utilities: SpitzerModel, SpitzerHarmModel,
+        SauterNeoModel
+
+    # Reference inputs: a simple deuterium plasma case suitable for
+    # hand-checking the SLAYER params formulas.
+    function _ref_kwargs(; dr_val=0.0, dc_type=:none)
+        return (
+            n_e=5.0e19, t_e=1000.0, t_i=1000.0,
+            omega=0.0, omega_e=1.0e4, omega_i=5.0e3,
+            qval=2.0, sval_r=1.0, bt=2.0,
+            rs=0.5, R0=1.7, mu_i=2.0, zeff=1.0,
+            chi_perp=1.0, chi_tor=1.0,
+            m=2, n=1,
+            dr_val=dr_val, dgeo_val=0.5, dc_type=dc_type,
+            ising=3
+        )
+    end
+
+    @testset "Test 1: round-trip from dimensional inputs" begin
+        @info "Building SLAYERParameters from a reference deuterium case"
+        p = slayer_parameters(; _ref_kwargs()...)
+
+        # Identity / passthrough
+        @test p.ising == 3
+        @test p.m == 2
+        @test p.n == 1
+        @test p.rs == 0.5
+        @test p.R0 == 1.7
+        @test p.bt == 2.0
+        @test p.sval_r == 1.0
+        @test p.dc_tmp == 0.0   # dr_val == 0 ⇒ no offset
+        @test p.dc_type === :none
+
+        # Trivially exact ratios
+        @test p.tau ≈ 1.0
+        # Q_e = −tauk·1e4 = negative; Q_i = −tauk·5e3 = negative
+        # Q_e − Q_i = −tauk·5e3 = Q_i (since Q_e = 2·Q_i) ⇒ iota_e = Q_e/Q_i = 2
+        @test p.iota_e ≈ 2.0
+
+        # Sign convention check (SLAYER layerinputs)
+        @test p.Q_e == -p.tauk * 1.0e4
+        @test p.Q_i == -p.tauk * 5.0e3    # SLAYER params convention: Q_i = −tauk·ω*i
+
+        # Default resistivity closure is neoclassical (Sauter F_33). The
+        # trapped-particle correction raises η above plain Spitzer at the
+        # same lnΛ: η_neo = η_Sp / F_33 with F_33 ∈ (0,1).
+        p_spitzer = slayer_parameters(; _ref_kwargs()...,
+            resistivity_model=SpitzerModel(), lnLambda_form=:nrl)
+        @test p.eta > p_spitzer.eta
+        @test 1.1 < p.eta / p_spitzer.eta < 4.0    # banana-regime F_33 ≈ 0.3–0.9
+
+        # Legacy Fitzpatrick/TJ path: the Spitzer-Härm σ_∥ closure agrees with
+        # the old Wesson 1.65e-9 form to ~2.3% (the two are independent Spitzer
+        # formulas), so this is a genuine cross-check, not a tautology.
+        lnLamb_wesson = 24.0 + 3.0 * log(10.0) - 0.5 * log(5.0e19) + log(1000.0)
+        eta_wesson = 1.65e-9 * lnLamb_wesson / (1000.0 / 1e3)^1.5
+        p_legacy = slayer_parameters(; _ref_kwargs()...,
+            resistivity_model=SpitzerHarmModel(), lnLambda_form=:wesson)
+        @test p_legacy.eta ≈ eta_wesson rtol = 3e-2
+
+        # τ_R = μ₀ r_s² / η holds for whichever closure was selected.
+        @test p.tau_r ≈ MU_0 * p.rs^2 / p.eta rtol = 1e-12
+        @test p_legacy.tau_r ≈ MU_0 * p_legacy.rs^2 / p_legacy.eta rtol = 1e-12
+
+        # Mass density and Alfvén time (independent of conductivity).
+        rho_expected = 2.0 * M_P * 5.0e19
+        tau_h_expected = 1.7 * sqrt(MU_0 * rho_expected) / (1 * 1.0 * 2.0)
+        # tauk = S^(1/3) · τ_H = (τ_R/τ_H)^(1/3)·τ_H = τ_R^(1/3)·τ_H^(2/3)
+        @test p.tauk ≈ p.lu^(1 / 3) * tau_h_expected rtol = 1e-12
+        @test p.tauk^3 / tau_h_expected^2 ≈ p.tau_r rtol = 1e-12
+
+        # Lundquist number is large positive
+        @test p.lu > 1e6
+        @test p.lu < 1e9
+
+        # Compressibility is in (0,1) for finite β
+        @test 0.0 < p.c_beta < 1.0
+
+        # Prandtl-like ratios are positive and equal here (chi_perp=chi_tor=1)
+        @test p.P_perp ≈ p.P_tor
+        @test p.P_perp > 0
+
+        # D_norm = (d_β/r_s)·S^(1/3)·√(τ/(1+τ))
+        D_norm_expected = (p.d_beta / p.rs) * p.lu^(1 / 3) * sqrt(p.tau / (1 + p.tau))
+        @test p.D_norm ≈ D_norm_expected rtol = 1e-12
+
+        # delta_n = S^(1/3)/r_s
+        @test p.delta_n ≈ p.lu^(1 / 3) / p.rs rtol = 1e-12
+    end
+
+    @testset "Test 1b: dc_tmp formulas activate when dr_val ≠ 0" begin
+        # All four dc_type branches must produce finite, non-NaN values
+        # and respect the signs/structure of the formulas in
+        # the SLAYER params dc_tmp formulas.
+        p_none = slayer_parameters(; _ref_kwargs(; dr_val=0.01, dc_type=:none)...)
+        @test p_none.dc_tmp == 0.0   # :none ignores dr_val
+
+        p_lar = slayer_parameters(; _ref_kwargs(; dr_val=0.01, dc_type=:lar)...)
+        p_rf = slayer_parameters(; _ref_kwargs(; dr_val=0.01, dc_type=:rfitzp)...)
+        p_tor = slayer_parameters(; _ref_kwargs(; dr_val=0.01, dc_type=:toroidal)...)
+
+        @test isfinite(p_lar.dc_tmp)
+        @test isfinite(p_rf.dc_tmp)
+        @test isfinite(p_tor.dc_tmp)
+        # dr_val > 0 with the (-dr_val) prefactor ⇒ negative dc_tmp for
+        # :lar, :rfitzp, :toroidal branches.
+        @test p_lar.dc_tmp < 0
+        @test p_rf.dc_tmp < 0
+        @test p_tor.dc_tmp < 0
+
+        # Sign flips with sign of dr_val
+        p_lar_neg = slayer_parameters(;
+            _ref_kwargs(; dr_val=-0.01, dc_type=:lar)...)
+        @test sign(p_lar_neg.dc_tmp) == -sign(p_lar.dc_tmp)
+
+        # Reject unknown dc_type
+        @test_throws ArgumentError slayer_parameters(;
+            _ref_kwargs(; dr_val=0.01, dc_type=:bogus)...)
+    end
+
+    @testset "Test 1c: SLAYERParameters direct kwarg construction" begin
+        # The @kwdef constructor must accept all required fields and
+        # default the optional ones.
+        p = SLAYERParameters(;
+            tau=1.0, lu=1e7, c_beta=0.1, D_norm=2.0,
+            P_perp=10.0, P_tor=10.0,
+            Q_e=-1.0, Q_i=0.5, iota_e=2.0 / 3.0,
+            tauk=1e-4, tau_r=10.0, delta_n=400.0,
+            rs=0.5, R0=1.7, bt=2.0, sval_r=1.0,
+            eta=2.5e-8, d_beta=4e-3
+        )
+        @test p.tau == 1.0
+        @test p.dc_tmp == 0.0
+        @test p.dc_type === :none
+        @test p.dr_val == 0.0
+        @test p.ising == 0
+    end
+
+    @testset "Test 2: r-based shear conversion" begin
+        # Direct application of r_s · (dq/dψ) / (q · da/dψ).
+        @test r_based_shear(0.5, 2.0, 4.0, 0.5) ≈ 2.0
+        @test r_based_shear(1.0, 1.0, 1.0, 1.0) ≈ 1.0
+
+        # Synthetic Solovev-like flux surface: a(ψ) = a₀·√ψ and q(ψ) =
+        # q₀·(1 + α·ψ). Then dq/dψ = q₀·α, da/dψ = a₀/(2√ψ),
+        # and the analytic r-based shear is
+        #   s_r(ψ) = a(ψ)·(dq/dr)/q(ψ)
+        #          = a₀√ψ · (dq/dψ)·(dψ/dr) / q(ψ)
+        #          = a₀√ψ · q₀α · (2√ψ/a₀) / (q₀(1+α ψ))
+        #          = 2αψ / (1+αψ).
+        a0, q0, alpha = 0.6, 1.2, 1.5
+        for psi in (0.1, 0.4, 0.7, 0.95)
+            a = a0 * sqrt(psi)
+            q = q0 * (1 + alpha * psi)
+            dq_dpsi = q0 * alpha
+            da_dpsi = a0 / (2 * sqrt(psi))
+            expected = 2 * alpha * psi / (1 + alpha * psi)
+            @test r_based_shear(a, q, dq_dpsi, da_dpsi) ≈ expected rtol = 1e-12
+        end
+
+        # Argument validation
+        @test_throws ArgumentError r_based_shear(0.5, 2.0, 1.0, 0.0)
+        @test_throws ArgumentError r_based_shear(0.5, 0.0, 1.0, 0.5)
+    end
+end
diff --git a/test/runtests_slayer_riccati.jl b/test/runtests_slayer_riccati.jl
new file mode 100644
index 000000000..22f7fc549
--- /dev/null
+++ b/test/runtests_slayer_riccati.jl
@@ -0,0 +1,162 @@
+@testset "SLAYER Riccati Δ" begin
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using StaticArrays
+    using Statistics: median
+
+    # Reach into the SLAYER submodule to test the BC selector helper
+    # without exporting it (it's an internal of the Riccati port).
+    _SLAYER_MOD = GeneralizedPerturbedEquilibrium.InnerLayer.SLAYER
+
+    # A reference deuterium case in the *large-D_norm* regime.
+    # T_e = T_i = 3 keV (vs 1 keV) lifts D_norm² above the iota_e·P_perp/P_tor^(2/3) threshold:
+    # D_norm² ∝ T_e² but threshold ∝ T_e^0.5, so D_norm² / threshold ∝ T_e^(3/2). At 3 keV the
+    # ratio is ~2.4 (vs ~0.5 at 1 keV), placing the fixture solidly on the large_D side of the
+    # branch boundary. All other inputs unchanged.
+    function _ref_params_large_D()
+        return slayer_parameters(;
+            n_e=5.0e19, t_e=3000.0, t_i=3000.0,
+            omega=0.0, omega_e=1.0e4, omega_i=5.0e3,
+            qval=2.0, sval_r=1.0, bt=2.0,
+            rs=0.5, R0=1.7, mu_i=2.0, zeff=1.0,
+            chi_perp=1.0, chi_tor=1.0,
+            m=2, n=1)
+    end
+
+    # A directly-built parameter set in the *small-D_norm* regime
+    function _ref_params_small_D()
+        return SLAYERParameters(;
+            tau=1.0, lu=1.0e7, c_beta=0.05, D_norm=0.05,
+            P_perp=20.0, P_tor=10.0,
+            Q_e=-1.0, Q_i=0.5, iota_e=2.0 / 3.0,
+            tauk=1.0e-4, tau_r=10.0, delta_n=400.0,
+            rs=0.5, R0=1.7, bt=2.0, sval_r=1.0,
+            eta=2.5e-8, d_beta=2.0e-4)
+    end
+
+    @testset "Interface compliance" begin
+        p = _ref_params_large_D()
+        Δ = solve_inner(SLAYERModel(), p, 0.5 + 0.2im)
+        @test Δ isa InnerLayerResponse
+        @test Δ.interchange == zero(ComplexF64)    # pressureless SLAYER has no interchange channel
+        @test isfinite(real(Δ.tearing))
+        @test isfinite(imag(Δ.tearing))
+    end
+
+    @testset "Boundary-condition branch selection" begin
+        p_large = _ref_params_large_D()
+        p_small = _ref_params_small_D()
+
+        # Sanity-check the regime ordering used by _riccati_f_initial:
+        # Branch 1 (large_D) iff D_norm² > iota_e·P_perp/P_tor^(2/3).
+        threshold(p) = p.iota_e * p.P_perp / p.P_tor^(2 / 3)
+        @test p_large.D_norm^2 > threshold(p_large)
+        @test p_small.D_norm^2 < threshold(p_small)
+
+        _, _, branch_large = _SLAYER_MOD._riccati_f_initial(p_large, 0.5 + 0.0im)
+        _, _, branch_small = _SLAYER_MOD._riccati_f_initial(p_small, 0.5 + 0.0im)
+        @test branch_large === :large_D
+        @test branch_small === :small_D
+
+        # Both branches should yield finite Δ values
+        Δl = solve_inner(SLAYERModel(), p_large, 0.5 + 0.1im)
+        Δs = solve_inner(SLAYERModel(), p_small, 0.5 + 0.1im)
+        @test isfinite(Δl.tearing) && isfinite(Δs.tearing)
+
+        # p_floor (=6 by default) is honored even when the branch
+        # formula would produce a smaller value.
+        p_start_default, _, _ = _SLAYER_MOD._riccati_f_initial(p_small, 0.5 + 0.0im)
+        @test p_start_default >= 6.0
+        # …and bumping the floor up bumps p_start up.
+        p_start_high, _, _ = _SLAYER_MOD._riccati_f_initial(p_small, 0.5 + 0.0im;
+            p_floor=12.0)
+        @test p_start_high >= 12.0
+    end
+
+    @testset "Smoothness across Q sweep" begin
+        p = _ref_params_large_D()
+        m = SLAYERModel()
+        γ = 0.2
+
+        # Full diamagnetic-band sweep. Δ(ω) is continuous and bounded across
+        # the whole band, but in the large-D_norm regime it rotates rapidly
+        # through the complex plane near the upper edge (ω ≳ 0.7 here), so a
+        # 0.2-spaced grid under-resolves that turn — adjacent samples there are
+        # legitimately far apart. We therefore test scale-invariant invariants
+        # (no NaN, bounded |Δ|, no isolated discontinuity) rather than an
+        # absolute step size, which would only measure sampling resolution and
+        # drift with the resistivity model.
+        ωs_full = collect(range(-1.5; stop=1.5, length=16))
+        Δ_full = [solve_inner(m, p, ω + γ * im).tearing for ω in ωs_full]
+        @test all(isfinite.(real.(Δ_full)))
+        @test all(isfinite.(imag.(Δ_full)))
+        # Bounded magnitude over the full band — no blow-up / pole crossing.
+        @test all(0.1 .< abs.(Δ_full) .< 10.0)
+
+        # Discontinuity check on the smooth central window. A solver glitch
+        # would show up as one step far larger than the local median; a smooth
+        # curve keeps that ratio small regardless of overall scale.
+        ωs = collect(range(-1.5; stop=0.5, length=11))
+        Δs = [solve_inner(m, p, ω + γ * im).tearing for ω in ωs]
+        diffs = abs.(diff(Δs))
+        @test maximum(diffs) < 6.0 * median(diffs)
+
+        # Δ is genuinely Q-dependent (not silently constant).
+        @test maximum(diffs) > 1e-6
+    end
+
+    @testset "Tolerance self-consistency" begin
+        p = _ref_params_large_D()
+        m = SLAYERModel()
+        Q = 0.5 + 0.2im
+        # The default reltol=1e-10 matches the Fortran SLAYER LSODE
+        # setting. Tightening to 1e-13 typically agrees to ~4 digits;
+        # the long inward integration span amplifies local tolerances
+        # by roughly 5 orders of magnitude, so 1e-3 relative is the
+        # realistic self-consistency threshold here.
+        Δ_default = solve_inner(m, p, Q).tearing
+        Δ_tight = solve_inner(m, p, Q; reltol=1e-13, abstol=1e-13).tearing
+        @test abs(Δ_default - Δ_tight) < 1e-3 * abs(Δ_tight)
+    end
+
+    @testset "p_min reduction stability" begin
+        # Pulling p_min closer to 0 (from the default 1e-6 down to 1e-7)
+        # changes Δ only marginally — the solution has well-developed
+        # asymptotic structure deep in the inner layer.
+        p = _ref_params_large_D()
+        m = SLAYERModel()
+        Q = 0.5 + 0.2im
+        Δ_default = solve_inner(m, p, Q; pmin=1e-6).tearing
+        Δ_deeper = solve_inner(m, p, Q; pmin=1e-7).tearing
+        @test abs(Δ_default - Δ_deeper) < 0.05 * abs(Δ_default)
+    end
+
+    @testset "Resistive layer thickness (del_s)" begin
+        p = _ref_params_large_D()
+
+        # riccati_del_s returns the dimensionless δ_s/d_β; must be finite.
+        dels_db = riccati_del_s(p)
+        @test dels_db isa ComplexF64
+        @test isfinite(dels_db)
+
+        lw = slayer_layer_thickness(p)
+        @test lw isa LayerWidths
+        @test lw.ising == p.ising && lw.m == p.m && lw.n == p.n
+
+        # Meters scaling and the magnitude field are self-consistent.
+        @test lw.dels_db == dels_db
+        @test lw.delta_s ≈ dels_db * p.d_beta
+        @test lw.delta_s_m ≈ abs(dels_db * p.d_beta)
+        @test lw.delta_s_m > 0
+
+        # Drift-scale reference is carried through unchanged.
+        @test lw.d_beta == p.d_beta
+
+        # The Riccati width should sit within a few orders of magnitude of
+        # the β-weighted ion scale it is built from (dels_db is O(1)).
+        @test 1e-3 * p.d_beta < lw.delta_s_m < 1e3 * p.d_beta
+
+        # Degenerate integration span (q_start ≤ q_min) returns a NaN
+        # sentinel rather than throwing.
+        @test isnan(riccati_del_s(p; q_start=1e-6, q_min=1e-5))
+    end
+end
diff --git a/test/runtests_slayer_runner.jl b/test/runtests_slayer_runner.jl
new file mode 100644
index 000000000..c3ab86806
--- /dev/null
+++ b/test/runtests_slayer_runner.jl
@@ -0,0 +1,236 @@
+@testset "Runner: Control + run_slayer + HDF5 output" begin
+    using GeneralizedPerturbedEquilibrium
+    using GeneralizedPerturbedEquilibrium.InnerLayer
+    using GeneralizedPerturbedEquilibrium.Dispersion
+    using GeneralizedPerturbedEquilibrium.Runner
+    using HDF5
+
+    # ------- Helper: build a synthetic SLAYERParameters with full control
+    function _mk_params(; rs=0.5, lu=1e7, tauk=1e-4,
+        Q_e=-1.0, Q_i=0.5, m=2, n=1, ising=1,
+        c_beta=0.1, D_norm=2.0)
+        return SLAYERParameters(;
+            tau=1.0, lu=lu, c_beta=c_beta, D_norm=D_norm,
+            P_perp=20.0, P_tor=10.0,
+            Q_e=Q_e, Q_i=Q_i,
+            iota_e=Q_e == Q_i ? 0.0 : Q_e / (Q_e - Q_i),
+            tauk=tauk, tau_r=1.0, delta_n=lu^(1 / 3) / rs,
+            rs=rs, R0=1.7, bt=2.0, sval_r=1.0,
+            eta=2.5e-8, d_beta=4e-3,
+            m=m, n=n, ising=ising
+        )
+    end
+
+    @testset "SLAYERControl defaults + validation" begin
+        c = SLAYERControl()
+        @test c.enabled == false
+        @test c.inner_model === :slayer_fitzpatrick
+        @test c.scan_mode === :amr
+        @test c.coupling_mode === :uncoupled
+        @test c.msing_max == 3
+
+        # Validation catches bad symbols
+        @test_throws ArgumentError Runner.validate(
+            SLAYERControl(; inner_model=:bogus))
+        @test_throws ArgumentError Runner.validate(
+            SLAYERControl(; scan_mode=:bogus))
+        @test_throws ArgumentError Runner.validate(
+            SLAYERControl(; coupling_mode=:bogus))
+        @test_throws ArgumentError Runner.validate(
+            SLAYERControl(; dc_type=:bogus))
+        @test_throws ArgumentError Runner.validate(
+            SLAYERControl(; msing_max=0))
+        @test_throws ArgumentError Runner.validate(
+            SLAYERControl(; nre=1))
+    end
+
+    @testset "slayer_control_from_toml: nested sections flatten" begin
+        section = Dict{String,Any}(
+            "enabled" => true,
+            "inner_model" => "slayer_fitzpatrick",
+            "scan_mode" => "brute_force",
+            "coupling_mode" => "coupled",
+            "dc_type" => "rfitzp",
+            "msing_max" => 2,
+            "bt" => 1.8,
+            "mu_i" => 2.0,
+            "dr_val" => 0.01,
+            "scan_grid" => Dict{String,Any}(
+                "Q_re_range" => [-5.0, 5.0],
+                "Q_im_range" => [-1.0, 3.0],
+                "nre" => 50,
+                "nim" => 40),
+            "amr" => Dict{String,Any}(
+                "passes" => 3,
+                "max_cells" => 50_000),
+            "growth_rate_filter" => Dict{String,Any}(
+                "pole_threshold" => 1e5,
+                "filter_above_poles" => false),
+            "profile_file" => "profiles.h5"
+        )
+        c = slayer_control_from_toml(section)
+        @test c.enabled
+        @test c.inner_model === :slayer_fitzpatrick
+        @test c.scan_mode === :brute_force
+        @test c.coupling_mode === :coupled
+        @test c.dc_type === :rfitzp
+        @test c.msing_max == 2
+        @test c.bt === 1.8
+        @test c.dr_val == 0.01
+        @test c.Q_re_range == (-5.0, 5.0)
+        @test c.Q_im_range == (-1.0, 3.0)
+        @test c.nre == 50
+        @test c.nim == 40
+        @test c.amr_passes == 3
+        @test c.amr_max_cells == 50_000
+        @test c.pole_threshold == 1e5
+        @test c.filter_above_poles == false
+        @test c.profile_file == "profiles.h5"
+
+        # Unknown keys should raise
+        bad = merge(section, Dict{String,Any}("mistyped_key" => 42))
+        @test_throws ArgumentError slayer_control_from_toml(bad)
+    end
+
+    @testset "run_slayer_from_inputs: disabled path is a no-op" begin
+        c = SLAYERControl(; enabled=false)
+        params = [_mk_params()]
+        dp = ComplexF64[0.0 + 0im;;]                      # 1×1 matrix
+        r = run_slayer_from_inputs(params, dp, c)
+        @test r.enabled == false
+        @test isempty(r.Q_root)
+        @test isempty(r.params)
+    end
+
+    @testset "run_slayer_from_inputs: validation catches size mismatch" begin
+        c = SLAYERControl(; enabled=true)
+        params = [_mk_params()]
+        bad_dp = ComplexF64[0.0 0.0; 0.0 0.0]
+        @test_throws ArgumentError run_slayer_from_inputs(params, bad_dp, c)
+    end
+
+    @testset "run_slayer_from_inputs: coupled mode finds known root" begin
+        # Build a 2-surface problem with a known coupled root by construction.
+        p1 = _mk_params(; rs=0.5, lu=1.0e7, tauk=1.0e-4, Q_e=-1.0, Q_i=0.5,
+            m=2, ising=1)
+        p2 = _mk_params(; rs=0.6, lu=2.0e7, tauk=1.2e-4, Q_e=-0.8, Q_i=0.4,
+            m=3, ising=2)
+        params = [p1, p2]
+
+        model = SLAYERModel()
+        # Pick a target Q and pin the diagonal Δ'_kk so det(M(Q_target)) = 0
+        Q_target = 0.2 + 0.3im
+        # Compute what each surface sees at Q_target (with per-surface
+        # rescaling: surface 2 sees Q_target * tauk_1/tauk_2).
+        Q_1 = Q_target * (p1.tauk / p1.tauk)         # = Q_target
+        Q_2 = Q_target * (p1.tauk / p2.tauk)
+        Δ1 = InnerLayer.solve_inner(model, p1, Q_1).tearing * p1.lu^(1 / 3)
+        Δ2 = InnerLayer.solve_inner(model, p2, Q_2).tearing * p2.lu^(1 / 3)
+        # Setting dp[k,k] = Δ_k at Q_target makes both diagonals of M vanish,
+        # which makes det(M) = 0 at Q_target.
+        dp = ComplexF64[Δ1 0.0; 0.0 Δ2]
+
+        c = SLAYERControl(; enabled=true,
+            inner_model=:slayer_fitzpatrick,
+            scan_mode=:brute_force,
+            coupling_mode=:coupled,
+            Q_re_range=(-1.0, 1.0),
+            Q_im_range=(-0.5, 0.8),
+            nre=80, nim=80,
+            pole_threshold=1e5)      # tuned for lu^(1/3) scale
+        r = run_slayer_from_inputs(params, dp, c)
+        @test r.enabled
+        @test length(r.Q_root) == 1          # single coupled eigenvalue
+        @test abs(r.Q_root[1] - Q_target) < 2e-2       # grid-resolution limited
+        @test r.coupled_extraction isa GrowthRateResult
+        @test isempty(r.per_surface_extraction)
+    end
+
+    @testset "write_slayer_hdf5!: round-trip structure" begin
+        p1 = _mk_params(; rs=0.5, lu=1.0e7, tauk=1.0e-4, m=2, ising=1)
+        p2 = _mk_params(; rs=0.6, lu=2.0e7, tauk=1.2e-4, m=3, ising=2)
+        params = [p1, p2]
+
+        # Diagonal dp, zero coupling → trivial root structure at Q_target=0
+        Q_target = 0.0 + 0.0im
+        model = SLAYERModel()
+        Δ1 = InnerLayer.solve_inner(model, p1, Q_target).tearing * p1.lu^(1 / 3)
+        Δ2 = InnerLayer.solve_inner(model, p2, Q_target).tearing * p2.lu^(1 / 3)
+        dp = ComplexF64[Δ1 0.0; 0.0 Δ2]
+
+        c = SLAYERControl(; enabled=true,
+            scan_mode=:brute_force,
+            coupling_mode=:coupled,
+            Q_re_range=(-0.5, 0.5),
+            Q_im_range=(-0.3, 0.3),
+            nre=40, nim=40,
+            pole_threshold=1e5,
+            store_scan=true)
+        r = run_slayer_from_inputs(params, dp, c)
+
+        mktemp() do path, io
+            close(io)
+            h5open(path, "w") do f
+                write_slayer_hdf5!(f, r)
+            end
+            h5open(path, "r") do f
+                g = f["slayer"]
+                @test haskey(g, "enabled") && read(g["enabled"]) == 1
+                @test haskey(g, "settings")
+                @test haskey(g, "per_surface")
+                @test haskey(g, "roots")
+                @test haskey(g, "diagnostics")
+                @test haskey(g, "scan")
+
+                # Settings round-trip
+                @test read(g["settings/inner_model"]) == "slayer_fitzpatrick"
+                @test read(g["settings/scan_mode"]) == "brute_force"
+                @test read(g["settings/coupling_mode"]) == "coupled"
+                @test read(g["settings/nre"]) == 40
+
+                # Per-surface arrays have the right length
+                @test length(read(g["per_surface/ising"])) == 2
+                @test read(g["per_surface/ising"]) == [1, 2]
+                @test read(g["per_surface/lu"])[1] ≈ 1.0e7
+                @test read(g["per_surface/lu"])[2] ≈ 2.0e7
+
+                # Roots arrays
+                @test length(read(g["roots/Q_root_real"])) == 1    # coupled
+                @test length(read(g["roots/omega_Hz"])) == 1
+
+                # Layer-thickness diagnostic: one entry per surface, with
+                # the physical thickness [m] and the drift scale.
+                @test length(read(g["layer_widths/delta_s_m"])) == 2
+                @test all(read(g["layer_widths/delta_s_m"]) .>= 0)
+                @test haskey(g["layer_widths"], "dels_db_real")
+                @test haskey(g["layer_widths"], "d_beta")
+
+                # Ragged diagnostics use flat+offsets encoding
+                @test haskey(g["diagnostics/valid_roots"], "flat_real")
+                @test haskey(g["diagnostics/valid_roots"], "flat_imag")
+                @test haskey(g["diagnostics/valid_roots"], "offsets")
+
+                # Scan group present (store_scan=true)
+                @test haskey(g, "scan/surface_1")
+                @test read(g["scan/surface_1/kind"]) == "brute_force"
+            end
+        end
+    end
+
+    @testset "write_slayer_hdf5!: disabled result still emits enabled=0" begin
+        c = SLAYERControl(; enabled=false)
+        r = empty_slayer_result(c)
+        mktemp() do path, io
+            close(io)
+            h5open(path, "w") do f
+                write_slayer_hdf5!(f, r)
+            end
+            h5open(path, "r") do f
+                g = f["slayer"]
+                @test read(g["enabled"]) == 0
+                @test !haskey(g, "settings")      # no further groups
+                @test !haskey(g, "per_surface")
+            end
+        end
+    end
+end