Web application version - #4
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… compute resumed control-flow state, and generate token movements
- Initial state estimation and short-term simulation - Resume token-animation
…nts with timestamp == to start_point
…trieving only the necessary rows
Add Docker runtime and VPS migration workflow
PowerMaxZ
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Jul 2, 2026
…, case_type_drift Build the three previously-unimplemented paper scenarios (#2, #4, #6), each validated end-to-end with real Prosimos (state c-index 1.0, baselines 0.5): - #2 parallel_auto (sim-driven): tools/generate_parallel_auto.py (AND block with a long Critical branch + short NonCritical chain on separate pools) + build_branch_automation_params (shrink non-critical durations toward a floor so the activity stays in the trace -> ngd blind). Also proves AND-gateway short-term resume works. - #4 front_back_load (sim-driven): tools/generate_linear_chain.py + build_front_back_load_params (antisymmetric position ramp, per-case total held exactly invariant; --load-direction front|back keeps the level ladder monotone). - #6 case_type_drift (sim-driven oracle): drift_case_types + _run_case_type_drift_levels, reuses synthetic_case_route. Re-sorts a fraction of case_type tags by arrival time, global marginal fixed. Both case_type and activity_type fire (case_type firing is the temporal-drift signature vs label_swap). Wiring: dispatch in _prepare_params_for_level / run_pipeline, DatasetSpec entries (synthetic_parallel_auto, synthetic_linear_chain), CLI choices + --load-direction. 17 new unit tests; full non-e2e suite 233 passed. Docs: PAPER_SCENARIOS.md (table + build status) and standalone EXPERIMENT_SCENARIOS.md (table + plain-English scenario descriptions). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Fj2DDhAhygixvTfJm4Qjpo
PowerMaxZ
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Jul 14, 2026
Scenario #4, redesigned per David from the single-linear-chain front_back_load (which RED detects) to compensating front/back XOR branches that even RED misses. - tools/generate_front_back_xor.py: one 50/50 XOR into two equal-total branches, P front-loaded and Q back-loaded (same activity count, same total duration). - perturb.build_front_back_swap_params: morphs each branch toward the other's time profile (level = percent swapped), holding each branch total exactly invariant. level 0 = GT, 100 = full swap. - Wired as a simulator-driven param family, DatasetSpec synthetic_front_back_xor (swap_p/q_task_ids), CLI --perturbation front_back_swap, 4 unit tests. - Docs updated to mark #4 built & validated. Validated (Scope A, 5 runs, levels 0-100): the cleanest shutout in the set -- ALL baselines blind including RED (cycle_time/red/rtd c-index 0.500, exactly 0 at every level; ngd 0.55), while state detects (activity 0.956, case 0.952, activity_case 0.936). The timeline-localization pillar.
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