diff --git a/marc/eval/baselines/README.md b/marc/eval/baselines/README.md
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+# CoT baseline
+
+Token-level chain-of-thought baseline for H1 comparison against MARC's value-diffusion solver.
+
+## Model
+
+`gpt-5` via OpenAI API. Fixed internal temperature — this model does not support
+temperature overrides (raises `BadRequestError` if passed). `max_completion_tokens=4096`;
+reasoning tokens count against this budget, and problems that exhaust it before producing
+a visible answer are scored as unparseable (see Known limitations).
+
+## Prompt
+
+Equations are rendered from `problem.graph.factors` (not the `description` string) so
+the model sees exactly what the checker verifies. Answers are constrained to decimal
+format (`ANSWER: x=<decimal>, y=<decimal>`) to keep parsing unambiguous — fractions
+(e.g. `7/2`) are explicitly disallowed in the instruction.
+
+## Run config
+
+- N = 25 problems per split (in_distribution, held_out_structure)
+- perturb_delta = 0.1
+- n_samples (k) = 1 (pass@1)
+
+## Running
+
+    python -m marc.eval.baselines.cot_baseline
+
+Output: results/p2_main/cot_baseline.json
+
+## Result summary (N=25)
+
+- solve_rate: 1.0 on both splits (generalization_gap = 0.0)
+- perturbation_robustness: 0.0 (in-distribution) vs 0.12 (held-out structure)
+
+Raw solve rate saturates at this problem size/difficulty, so it does not separate
+the two splits — but perturbation_robustness does show a gap, suggesting this suite
+may still be too easy for solve_rate itself to be an informative H1 signal.
+Flagged to Sparsh/Davin: consider a harder suite (e.g. length_extrapolation with
+higher n_vars) if solve_rate saturation persists once MARC's model is compared.
+
+## Known limitations
+
+- gpt-5's reasoning-token usage varies per problem; if it exhausts max_completion_tokens
+  on internal reasoning before emitting an ANSWER line, parse_answer returns None and
+  the Checker correctly scores it as a failure. This is a legitimate baseline failure
+  mode (a token-budget design choice), not a parsing bug.
+- Output JSON can contain NaN/Infinity for unparseable answers — not valid strict JSON;
+  confirm downstream consumers (e.g. summary table merge) handle this or coerce to null.
diff --git a/marc/eval/baselines/__init__.py b/marc/eval/baselines/__init__.py
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diff --git a/marc/eval/baselines/cot_baseline.py b/marc/eval/baselines/cot_baseline.py
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+import re
+
+
+import sympy as sp
+
+def _split_residual(expression: str) -> tuple[str, str]:
+    expr = sp.sympify(expression)
+    const, var_part = expr.as_coeff_Add()
+    rhs = -const
+    return str(var_part), str(rhs)
+
+def render_prompt(problem) -> str:
+    var_names = [v.id for v in problem.graph.variables]
+
+    equations = []
+    for factor in problem.graph.factors:
+        lhs, rhs = _split_residual(factor.expression)
+        equations.append(f"{lhs} = {rhs}")
+
+    system = "\n".join(equations)
+    answer_line = ", ".join(f"{v}=<decimal>" for v in var_names)
+
+    return (
+        f"Solve this system of equations for {', '.join(var_names)}:\n\n"
+        f"{system}\n\n"
+        "Reason step by step, then give your final answer as the very last line "
+        "in exactly this format, using decimal numbers (e.g. 3.5, not 7/2):\n"
+        f"ANSWER: {answer_line}"
+    )
+import openai
+
+_client = openai.OpenAI()  # reads OPENAI_API_KEY from env automatically
+
+
+def call_model(prompt: str, model: str = "gpt-5") -> str:
+    response = _client.chat.completions.create(
+        model=model,
+        max_completion_tokens=4096,
+        messages=[{"role": "user", "content": prompt}],
+    )
+    return response.choices[0].message.content
+
+import re
+
+
+def parse_answer(text: str, n_vars: int) -> list[float] | None:
+    match = re.search(r"ANSWER:\s*(.+)", text)
+    if match is None:
+        return None
+
+    numbers = re.findall(r"-?\d+\.?\d*", match.group(1))
+    if len(numbers) != n_vars:
+        return None
+
+    return [float(n) for n in numbers]
+
+
+class CoTSolver:
+    def __init__(self, model: str = "gpt-5"):
+        self.model = model
+
+    def sample(self, problem, k: int) -> list[list[float]]:
+        prompt = render_prompt(problem)
+        n_vars = len(problem.solution)
+
+        candidates = []
+        for _ in range(k):
+            text = call_model(prompt, self.model)
+            parsed = parse_answer(text, n_vars)
+            if parsed is None:
+                parsed = [float("nan")] * n_vars
+            candidates.append(parsed)
+        return candidates
+    
+import json
+from marc.eval.problems import in_distribution, held_out_structure
+from marc.eval.runner import run_split_eval
+
+import json
+from marc.eval.problems import in_distribution, held_out_structure
+from marc.eval.runner import run_split_eval
+
+if __name__ == "__main__":
+    N = 25              # TODO: confirm with Sparsh
+    PERTURB_DELTA = 0.1  # TODO: confirm with Sparsh
+    K = 1
+
+    solver = CoTSolver(model="gpt-5")
+
+    metrics = run_split_eval(
+        in_distribution(N),
+        held_out_structure(N),
+        solver=solver,
+        perturb_delta=PERTURB_DELTA,
+        n_samples=K,
+        solver_name="cot_baseline",
+    )
+
+    import os
+    os.makedirs("results/p2_main", exist_ok=True)
+    with open("results/p2_main/cot_baseline.json", "w") as f:
+        json.dump(metrics, f, indent=2)
+
+    print(f"solve rate (in-dist): {metrics['splits']['in_distribution']['solve_rate']}")
+    print(f"solve rate (held-out): {metrics['splits']['held_out_structure']['solve_rate']}")
+    print(f"generalization gap: {metrics['generalization_gap']}")
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