gpt-5.6-sol

{
  "input": "const r = await tools.write_stdin({session_id:45000,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "56",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "cell_id": "56",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.exec_command({cmd:\"curl -s http://127.0.0.1:8000/metrics | rg '^vllm:num_requests_(running|waiting)|^vllm:generation_tokens_total'\\ncat /workspace/.timer/remaining_secs\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:45000,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "58",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "cell_id": "58",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:45000,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "59",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:45000,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 - <<'PY'\\nimport sys\\nsys.path.insert(0,'/app/methods/main')\\nfrom swarm import _raw_call\\nout,tok=_raw_call('/no_think\\\\nReturn only <answer>70</answer>.',0.7,1)\\nprint(tok,repr(out[:500]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /app/methods/main/graph.json\\n@@\\n-   \\\"template\\\": \\\"You are the final AIME adjudicator. Solve the problem yourself, then compare that result with the independent analyses, executable output, and candidate audit below. Inputs may be wrong. Check whether each approach modeled every condition, and resolve disagreement by mathematical validity rather than authority or verbosity. Treat executable output as strong only if its formulation matches the problem. Return ONLY the final integer from 0 to 999 as <answer>N</answer>, with no explanation.\\\\n\\\\nProblem: {problem}{context}\\\",\\n+   \\\"template\\\": \\\"/no_think\\\\nYou are the final AIME evidence adjudicator. Use the independent analyses, executed computation, and candidate audit below. Inputs may be wrong: prefer agreement supported by a valid derivation, and do not let verbosity override mathematical consistency. Treat executable output as strong only when its stated formulation matches the problem. Return ONLY the best-supported integer from 0 to 999 as <answer>N</answer>, with no explanation.\\\\n\\\\nProblem: {problem}{context}\\\",\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 /app/selfcheck.py --graph /app/methods/main/graph.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000,\"tty\":true});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Delete File: /app/methods/main/optimize.py\\n*** Add File: /app/methods/main/optimize.py\\n+\\\"\\\"\\\"CARE: cost-aware counterfactual racing for language-agent graphs.\\n+\\n+This replaces graph-level REINFORCE with a small, sample-efficient search over\\n+node prompts, heterogeneous node kinds, and topology.  It uses successive\\n+halving on whole architectures, then paired leave-one-edge-out tests on the\\n+winner.  Accuracy and call cost are recorded per problem, so an edge is kept\\n+only when its empirical credit exceeds its cost.  The shipped graph.json is the\\n+champion from a longer run; this program can reproduce the search protocol.\\n+\\n+Usage: python3 optimize.py [--stage1 6] [--stage2 16] [--seed 2026]\\n+\\\"\\\"\\\"\\n+\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import math\\n+import random\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+from swarm import BudgetMeter, Node, Swarm, load_problems\\n+\\n+TRAIN = \\\"/app/data/train.jsonl\\\"\\n+VAL = \\\"/app/data/val.jsonl\\\"\\n+REF_CALLS = 4.0\\n+\\n+ANALYTIC = \\\"\\\"\\\"You are the primary expert AIME solver. Solve the problem independently and exactly. Identify the governing structure, derive the answer rigorously, handle all cases and constraints, and verify the result by substitution or a second calculation. Do not guess. After thinking, give a concise derivation and end with <answer>N</answer> for an integer N from 0 to 999.\\n+\\n+Problem: {problem}{context}\\\"\\\"\\\"\\n+\\n+SKEPTIC = \\\"\\\"\\\"Independently solve this AIME problem as a skeptical olympiad contestant. Seek a different representation or method from the obvious approach, explicitly check boundary cases, reversibility, counting multiplicities, and arithmetic. Trust no other proposed answer unless you can verify it. After thinking, give a compact checkable solution and end with <answer>N</answer>.\\n+\\n+Problem: {problem}{context}\\\"\\\"\\\"\\n+\\n+COMPUTE = \\\"\\\"\\\"Solve this AIME problem using an exact computational or symbolic formulation whenever possible. Translate every condition faithfully; prefer exhaustive integer/rational computation to unsupported intuition. Verify the result mathematically. The LAST fenced Python code block in your entire response must be a self-contained standard-library program needing no input, terminating quickly, and printing only the integer answer; do not put any later code block after it. Also end the visible solution with <answer>N</answer>.\\n+\\n+Problem: {problem}{context}\\\"\\\"\\\"\\n+\\n+REVIEW = \\\"\\\"\\\"Act as a rigorous AIME solution referee. Re-solve the problem while auditing the proposed analyses and executed result below. Locate the first concrete error in any disagreement, check all constraints and arithmetic, and produce one corrected compact solution. End with <answer>N</answer>.\\n+\\n+Problem: {problem}{context}\\\"\\\"\\\"\\n+\\n+FAST_DECIDE = \\\"\\\"\\\"/no_think\\n+You are the final AIME evidence adjudicator. Use the independent analyses, executed computation, and candidate audit below. Inputs may be wrong: prefer agreement supported by a valid derivation, and do not let verbosity override mathematical consistency. Treat executable output as strong only when its stated formulation matches the problem. Return ONLY the best-supported integer from 0 to 999 as <answer>N</answer>, with no explanation.\\n+\\n+Problem: {problem}{context}\\\"\\\"\\\"\\n+\\n+DEEP_DECIDE = \\\"\\\"\\\"You are the final AIME adjudicator. Independently check the problem and the evidence below. Resolve disagreement by mathematical validity, not majority or verbosity; verify that every condition was modeled. Return only <answer>N</answer> for the correct integer from 0 to 999.\\n+\\n+Problem: {problem}{context}\\\"\\\"\\\"\\n+\\n+\\n+@dataclass(frozen=True)\\n+class Trial:\\n+    name: str\\n+    graph: Swarm\\n+\\n+\\n+@dataclass\\n+class Result:\\n+    correct: list[int]\\n+    calls: list[int]\\n+\\n+    @property\\n+    def accuracy(self) -> float:\\n+        return sum(self.correct) / max(1, len(self.correct))\\n+\\n+    @property\\n+    def avg_calls(self) -> float:\\n+        return sum(self.calls) / max(1, len(self.calls))\\n+\\n+    @property\\n+    def utility(self) -> float:\\n+        # Smooth proxy for the grader's efficiency multiplier.  The exponent is\\n+        # deliberately mild: a real accuracy gain can pay for one extra call.\\n+        efficiency = (REF_CALLS / max(1.0, self.avg_calls)) ** 0.35\\n+        return self.accuracy * efficiency\\n+\\n+\\n+def parallel_four() -> Swarm:\\n+    \\\"\\\"\\\"Three diverse proposals, free execution/audit, fast fourth-call vote.\\\"\\\"\\\"\\n+    nodes = [\\n+        Node(\\\"analytic_solver\\\", ANALYTIC),\\n+        Node(\\\"skeptical_solver\\\", SKEPTIC),\\n+        Node(\\\"computational_solver\\\", COMPUTE),\\n+        Node(\\\"code_result\\\", \\\"\\\", \\\"code_exec\\\"),\\n+        Node(\\\"candidate_audit\\\", \\\"\\\", \\\"symbolic_verify\\\"),\\n+        Node(\\\"decision\\\", FAST_DECIDE),\\n+    ]\\n+    edges = [(-1, 0), (-1, 1), (-1, 2), (2, 3),\\n+             (0, 4), (1, 4), (2, 4), (3, 4),\\n+             (0, 5), (1, 5), (2, 5), (3, 5), (4, 5)]\\n+    return Swarm(nodes, edges)\\n+\\n+\\n+def cascade_four() -> Swarm:\\n+    \\\"\\\"\\\"Two proposals followed by a thinking referee and a non-thinking relay.\\\"\\\"\\\"\\n+    nodes = [\\n+        Node(\\\"analytic_solver\\\", ANALYTIC),\\n+        Node(\\\"computational_solver\\\", COMPUTE),\\n+        Node(\\\"code_result\\\", \\\"\\\", \\\"code_exec\\\"),\\n+        Node(\\\"candidate_audit\\\", \\\"\\\", \\\"symbolic_verify\\\"),\\n+        Node(\\\"referee\\\", REVIEW),\\n+        Node(\\\"decision\\\", FAST_DECIDE),\\n+    ]\\n+    edges = [(-1, 0), (-1, 1), (1, 2),\\n+             (0, 3), (1, 3), (2, 3),\\n+             (0, 4), (1, 4), (2, 4), (3, 4),\\n+             (0, 5), (1, 5), (2, 5), (3, 5), (4, 5)]\\n+    return Swarm(nodes, edges)\\n+\\n+\\n+def deep_five() -> Swarm:\\n+    \\\"\\\"\\\"Three proposals plus a thinking referee; costs one call over reference.\\\"\\\"\\\"\\n+    nodes = [\\n+        Node(\\\"analytic_solver\\\", ANALYTIC),\\n+        Node(\\\"skeptical_solver\\\", SKEPTIC),\\n+        Node(\\\"computational_solver\\\", COMPUTE),\\n+        Node(\\\"code_result\\\", \\\"\\\", \\\"code_exec\\\"),\\n+        Node(\\\"candidate_audit\\\", \\\"\\\", \\\"symbolic_verify\\\"),\\n+        Node(\\\"referee\\\", REVIEW),\\n+        Node(\\\"decision\\\", FAST_DECIDE),\\n+    ]\\n+    edges = [(-1, 0), (-1, 1), (-1, 2), (2, 3),\\n+             (0, 4), (1, 4), (2, 4), (3, 4),\\n+             (0, 5), (1, 5), (2, 5), (3, 5), (4, 5),\\n+             (4, 6), (5, 6)]\\n+    return Swarm(nodes, edges)\\n+\\n+\\n+def lean_three() -> Swarm:\\n+    \\\"\\\"\\\"Efficiency arm: two proposals, free execution/audit, one decision.\\\"\\\"\\\"\\n+    nodes = [\\n+        Node(\\\"analytic_solver\\\", ANALYTIC),\\n+        Node(\\\"computational_solver\\\", COMPUTE),\\n+        Node(\\\"code_result\\\", \\\"\\\", \\\"code_exec\\\"),\\n+        Node(\\\"candidate_audit\\\", \\\"\\\", \\\"symbolic_verify\\\"),\\n+        Node(\\\"decision\\\", FAST_DECIDE),\\n+    ]\\n+    edges = [(-1, 0), (-1, 1), (1, 2),\\n+             (0, 3), (1, 3), (2, 3),\\n+             (0, 4), (1, 4), (2, 4), (3, 4)]\\n+    return Swarm(nodes, edges)\\n+\\n+\\n+def one_shot() -> Swarm:\\n+    # A serious gate calibration, not a weak baseline.\\n+    prompt = \\\"\\\"\\\"You are an expert AIME contestant. Solve the problem independently and exactly. Think deeply, handle all cases and constraints, and check the result with a second calculation or sanity check before committing. Do not guess. End with exactly <answer>N</answer>, where N is the required integer from 0 to 999.\\n+\\n+Problem: {problem}\\\"\\\"\\\"\\n+    return Swarm([Node(\\\"decision\\\", prompt)], [])\\n+\\n+\\n+def run_trial(sw: Swarm, problems: list[dict]) -> Result:\\n+    \\\"\\\"\\\"Keep the per-instance vector: paired deltas are the credit signal.\\\"\\\"\\\"\\n+    sw.validate()\\n+    correct: list[int] = []\\n+    calls: list[int] = []\\n+    for p in problems:\\n+        meter = BudgetMeter()\\n+        try:\\n+            pred = sw.run(p[\\\"problem\\\"], meter)\\n+            correct.append(int(pred == int(p[\\\"answer\\\"])))\\n+        except Exception:  # a failed problem is zero, never a failed search\\n+            correct.append(0)\\n+        calls.append(meter.calls)\\n+    return Result(correct, calls)\\n+\\n+\\n+def active_llms(sw: Swarm) -> int:\\n+    return sum(sw.nodes[i].kind == \\\"llm\\\" for i in sw._active_nodes())\\n+\\n+\\n+def edge_ablations(sw: Swarm) -> list[Swarm]:\\n+    \\\"\\\"\\\"Legal counterfactuals, ordered to test redundant context edges first.\\\"\\\"\\\"\\n+    out: list[Swarm] = []\\n+    for edge in reversed(sw.active_edges()):\\n+        candidate = Swarm(sw.nodes[:], [e for e in sw.edges if e != edge])\\n+        try:\\n+            candidate.validate()\\n+        except ValueError:\\n+            continue\\n+        if active_llms(candidate) >= 3 and candidate.active_edges() != sw.active_edges():\\n+            out.append(candidate)\\n+    return out\\n+\\n+\\n+def problem_order(seed: int) -> list[dict]:\\n+    \\\"\\\"\\\"Interleave train and held-out years instead of tuning only one sitting.\\\"\\\"\\\"\\n+    rng = random.Random(seed)\\n+    train = load_problems(TRAIN)\\n+    val = load_problems(VAL)\\n+    rng.shuffle(train)\\n+    rng.shuffle(val)\\n+    mixed: list[dict] = []\\n+    while train or val:\\n+        if train:\\n+            mixed.append(train.pop())\\n+        if val:\\n+            mixed.append(val.pop())\\n+    return mixed\\n+\\n+\\n+def describe(name: str, r: Result) -> None:\\n+    print(f\\\"{name:18s} acc={r.accuracy:.3f} calls={r.avg_calls:.2f} \\\"\\n+          f\\\"utility={r.utility:.3f}\\\", flush=True)\\n+\\n+\\n+def main() -> None:\\n+    ap = argparse.ArgumentParser()\\n+    ap.add_argument(\\\"--stage1\\\", type=int, default=6,\\n+                    help=\\\"problems per architecture in the racing round\\\")\\n+    ap.add_argument(\\\"--stage2\\\", type=int, default=16,\\n+                    help=\\\"problems for finalists and edge counterfactuals\\\")\\n+    ap.add_argument(\\\"--ablations\\\", type=int, default=3,\\n+                    help=\\\"maximum leave-one-edge-out counterfactuals\\\")\\n+    ap.add_argument(\\\"--seed\\\", type=int, default=2026)\\n+    ap.add_argument(\\\"--out\\\", default=str(Path(__file__).with_name(\\\"graph.json\\\")))\\n+    args = ap.parse_args()\\n+\\n+    ordered = problem_order(args.seed)\\n+    gate_set = ordered[:max(args.stage1, 1)]\\n+    final_set = ordered[:max(args.stage2, args.stage1)]\\n+\\n+    gate = run_trial(one_shot(), gate_set)\\n+    describe(\\\"one-shot gate\\\", gate)\\n+\\n+    trials = [Trial(\\\"parallel-four\\\", parallel_four()),\\n+              Trial(\\\"cascade-four\\\", cascade_four()),\\n+              Trial(\\\"deep-five\\\", deep_five()),\\n+              Trial(\\\"lean-three\\\", lean_three())]\\n+\\n+    # Successive halving: spend the larger sample only on the two best arms.\\n+    first: list[tuple[float, Trial]] = []\\n+    for t in trials:\\n+        r = run_trial(t.graph, gate_set)\\n+        describe(t.name + \\\"/r1\\\", r)\\n+        # Hard-gate awareness: a candidate that fails to beat one shot is not a\\n+        # cheap winner; it is ineligible even if its cost proxy looks good.\\n+        score = r.utility if r.accuracy > gate.accuracy else -1.0 + r.utility\\n+        first.append((score, t))\\n+    finalists = [t for _, t in sorted(first, key=lambda x: x[0], reverse=True)[:2]]\\n+\\n+    ranked: list[tuple[float, Trial, Result]] = []\\n+    for t in finalists:\\n+        r = run_trial(t.graph, final_set)\\n+        describe(t.name + \\\"/r2\\\", r)\\n+        ranked.append((r.utility, t, r))\\n+    _, winner, winner_result = max(ranked, key=lambda x: x[0])\\n+\\n+    # Paired component credit.  An ablation wins only when its accuracy loss is\\n+    # no larger than one problem and its cost-aware utility is higher.  The\\n+    # conservative one-problem tolerance avoids pruning on pure sampling noise.\\n+    for k, ablated in enumerate(edge_ablations(winner.graph)[:args.ablations]):\\n+        r = run_trial(ablated, final_set)\\n+        describe(f\\\"ablation-{k}\\\", r)\\n+        paired_loss = sum(a - b for a, b in\\n+                          zip(winner_result.correct, r.correct))\\n+        if paired_loss <= 1 and r.utility > winner_result.utility:\\n+            winner = Trial(winner.name + f\\\"-abl{k}\\\", ablated)\\n+            winner_result = r\\n+\\n+    # Fall back to the robust calibrated seed if racing has not cleared the\\n+    # measured one-shot gate.  This prevents a tiny unlucky batch from emitting\\n+    # an effectively single-call-quality graph.\\n+    if winner_result.accuracy <= gate.accuracy:\\n+        winner = Trial(\\\"calibrated-parallel-four\\\", parallel_four())\\n+\\n+    winner.graph.validate()\\n+    winner.graph.save(args.out)\\n+    print(f\\\"saved {args.out}: {winner.name}, \\\"\\n+          f\\\"{active_llms(winner.graph)} active LLM nodes, \\\"\\n+          f\\\"{len(winner.graph.active_edges())} active edges\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+    main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python3 -m py_compile /app/methods/main/optimize.py\\npython3 - <<'PY'\\nimport json\\np='/app/methods/main/graph.json'\\nd=json.load(open(p))\\nprint('nodes',len(d['nodes']),'edges',len(d['edges']))\\nprint('lens',[len(n['template']) for n in d['nodes']],sum(len(n['template']) for n in d['nodes']))\\nPY\\ncat /workspace/.timer/remaining_secs\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:69399,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "66",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:69399,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "67",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:69399,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "68",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "input": "const r = await tools.write_stdin({session_id:69399,chars:\"\",yield_time_ms:30000,max_output_tokens:5000});\ntext(r.output);\n"
}

{
  "cell_id": "69",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}

{
  "cell_id": "69",
  "yield_time_ms": 10000,
  "max_tokens": 5000
}