claude-opus-5

The file /app/methods/main/optimize.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/methods/main/optimize.py", "oldString": "    import re\n    keyed = {t: {(c[\"pid\"], c[\"d\"]): c for c in nodestats[t][\"cells\"]} for t in names}\n    keys = sorted(next(iter(keyed.values())).keys())\n    res = []\n    for k in range(1, max_k + 1):\n        for combo in itertools.combinations(names, k):\n            good = 0\n            for kk in keys:\n                cands = []\n                for t in combo:\n                    c = keyed[t][kk]\n                    if c[\"pred\"] is not None:\n                        cands.append(c[\"pred\"])\n                    if c[\"code\"] is not None:\n                        cands.extend([c[\"code\"], c[\"code\"]])  # code_exec double-votes\n                if not cands:\n                    continue\n                from collections import Counter\n                cnt = Counter([c for c in cands if 0 <= c <= 999])\n                if not cnt:\n                    continue\n                top = max(cnt, key=lambda x: (cnt[x], -cands.index(x)))\n                good += int(top == keyed[combo[0]][kk][\"gold\"])\n            res.append({\"combo\": combo, \"tally_acc\": good / len(keys), \"k\": k})\n    res.sort(key=lambda r: -r[\"tally_acc\"])\n    return res", "newString": "    from collections import Counter\n    keyed = {t: {(c[\"pid\"], c[\"d\"]): c for c in nodestats[t][\"cells\"]} for t in names}\n    res = []\n    for k in range(1, max_k + 1):\n        # `combo` is ORDERED: symbolic_verify breaks ties by earliest-listed\n        # predecessor, so permutations are genuinely different graphs (different\n        # edge order) and the ordering is part of what we are optimizing.\n        for combo in itertools.permutations(names, k):\n            keys = sorted(set.intersection(*[set(keyed[t]) for t in combo]))\n            if len(keys) < 30:            # too few comparable cells to trust\n                continue\n            good = cov = 0\n            for kk in keys:\n                cands = []\n                for t in combo:\n                    c = keyed[t][kk]\n                    if c[\"pred\"] is not None:\n                        cands.append(c[\"pred\"])\n                    if c[\"code\"] is not None:\n                        cands.extend([c[\"code\"], c[\"code\"]])  # code_exec double-votes\n                cands = [c for c in cands if 0 <= c <= 999]\n                if not cands:\n                    continue\n                cov += 1\n                cnt = Counter(cands)\n                top = max(cnt, key=lambda x: (cnt[x], -cands.index(x)))\n                good += int(top == keyed[combo[0]][kk][\"gold\"])\n            res.append({\"combo\": combo, \"tally_acc\": good / len(keys), \"k\": k,\n                        \"n\": len(keys), \"coverage\": cov / len(keys)})\n    # canonicalise: keep only the best ordering of each unordered set, but remember\n    # that the ordering itself is the thing we will bake into the edge list.\n    best: dict[frozenset, dict] = {}\n    for r in res:\n        s = frozenset(r[\"combo\"])\n        if s not in best or r[\"tally_acc\"] > best[s][\"tally_acc\"]:\n            best[s] = r\n    out = sorted(best.values(), key=lambda r: -r[\"tally_acc\"])\n    return out", "originalFile": null, "structuredPatch": [{"oldStart": 196, "oldLines": 13, "newStart": 196, "newLines": 18, "lines": ["     \"\"\"Enumerate solver subsets and score the FREE tally offline. Zero LLM cost:", "     the samples are already in the cache, so this explores the whole subset", "     lattice for nothing -- the reuse REINFORCE cannot exploit.\"\"\"", "-    import re", "+    from collections import Counter", "     keyed = {t: {(c[\"pid\"], c[\"d\"]): c for c in nodestats[t][\"cells\"]} for t in names}", "-    keys = sorted(next(iter(keyed.values())).keys())", "     res = []", "     for k in range(1, max_k + 1):", "-        for combo in itertools.combinations(names, k):", "-            good = 0", "+        # `combo` is ORDERED: symbolic_verify breaks ties by earliest-listed", "+        # predecessor, so permutations are genuinely different graphs (different", "+        # edge order) and the ordering is part of what we are optimizing.", "+        for combo in itertools.permutations(names, k):", "+            keys = sorted(set.intersection(*[set(keyed[t]) for t in combo]))", "+            if len(keys) < 30:            # too few comparable cells to trust", "+                continue", "+            good = cov = 0", "             for kk in keys:", "                 cands = []", "                 for t in combo:"]}, {"oldStart": 211, "oldLines": 17, "newStart": 216, "newLines": 24, "lines": ["                         cands.append(c[\"pred\"])", "                     if c[\"code\"] is not None:", "                         cands.extend([c[\"code\"], c[\"code\"]])  # code_exec double-votes", "+                cands = [c for c in cands if 0 <= c <= 999]", "                 if not cands:", "                     continue", "-                from collections import Counter", "-                cnt = Counter([c for c in cands if 0 <= c <= 999])", "-                if not cnt:", "-                    continue", "+                cov += 1", "+                cnt = Counter(cands)", "                 top = max(cnt, key=lambda x: (cnt[x], -cands.index(x)))", "                 good += int(top == keyed[combo[0]][kk][\"gold\"])", "-            res.append({\"combo\": combo, \"tally_acc\": good / len(keys), \"k\": k})", "-    res.sort(key=lambda r: -r[\"tally_acc\"])", "-    return res", "+            res.append({\"combo\": combo, \"tally_acc\": good / len(keys), \"k\": k,", "+                        \"n\": len(keys), \"coverage\": cov / len(keys)})", "+    # canonicalise: keep only the best ordering of each unordered set, but remember", "+    # that the ordering itself is the thing we will bake into the edge list.", "+    best: dict[frozenset, dict] = {}", "+    for r in res:", "+        s = frozenset(r[\"combo\"])", "+        if s not in best or r[\"tally_acc\"] > best[s][\"tally_acc\"]:", "+            best[s] = r", "+    out = sorted(best.values(), key=lambda r: -r[\"tally_acc\"])", "+    return out", " ", " ", " # ── phase 4: counterfactual credit assignment ─────────────────────────────────"]}], "userModified": false, "replaceAll": false}

135 (template,problem,draw) cells, 135 need LLM calls
=== calibT ===
180 (template,problem,draw) cells, 180 need LLM calls
=== cache ===
758
vllm:num_requests_running{engine="0",model_name="node-1b"} 54.0
vllm:num_requests_waiting{engine="0",model_name="node-1b"} 42.0
# HELP vllm:generation_tokens_total Number of generation tokens processed.
# TYPE vllm:generation_tokens_total counter

[stdout]
135 (template,problem,draw) cells, 135 need LLM calls
=== calibT ===
180 (template,problem,draw) cells, 180 need LLM calls
=== cache ===
758
vllm:num_requests_running{engine="0",model_name="node-1b"} 54.0
vllm:num_requests_waiting{engine="0",model_name="node-1b"} 42.0
# HELP vllm:generation_tokens_total Number of generation tokens processed.
# TYPE vllm:generation_tokens_total counter
[metadata] {"noOutputExpected": false}

196:def run_symbolic_verify(pred_texts: list[str]) -> str:
197-    """Check candidate integer answers found in predecessor outputs. NON-LLM, FREE.
198-
199-    Collects integer candidates the predecessors proposed (via the grader's own
200-    parser, plus any code_exec computed candidate), rejects out-of-[0,999], and
201-    reports whether the valid candidates agree (else a majority pick).
202-    """
203-    cands: list[int] = []
204-    for t in pred_texts:
205-        for m in re.finditer(r"computed integer candidate\s*=\s*(-?\d+)", t):
206-            cands.append(int(m.group(1)))
207-        v = parse_answer(t)
208-        if v is not None:
209-            cands.append(v)
210-    if not cands:
211-        return "[symbolic_verify] no integer candidate found to check."
212-    valid = [c for c in cands if 0 <= c <= 999]
213-    invalid = [c for c in cands if not (0 <= c <= 999)]
214-    lines = [f"[symbolic_verify] candidates seen: {cands}"]
215-    if invalid:
216-        lines.append(f"[symbolic_verify] REJECTED out-of-range candidates: {invalid}")
217-    if valid:
218-        uniq = sorted(set(valid))
219-        if len(uniq) == 1:
220-            lines.append(f"[symbolic_verify] all valid candidates AGREE on {uniq[0]}.")
221-        else:
222-            from collections import Counter
223-            c = Counter(valid)
224-            top = max(uniq, key=lambda x: (c[x], -valid.index(x)))
225-            lines.append(
226-                f"[symbolic_verify] valid candidates DISAGREE {dict(c)}; "
227-                f"majority pick = {top}."
228-            )
=== parse_answer ===
407:def parse_answer(text: str) -> int | None:
408-    text = _strip_think(text)
409-    for pat in (r"<answer>\s*(\d{1,3})\s*</answer>",
410-                r"\\boxed\{\s*(\d{1,3})\s*\}",
411-                r"(?:final answer|answer)\D{0,10}(\d{1,3})",
412-                r"(\d{1,3})\s*$"):
413-        m = re.search(pat, text, re.I)
414-        if m:
415-            v = int(m.group(1))
416-            if 0 <= v <= 999:
417-                return v
418-    return None
419-
420-
421-def load_problems(path: str | Path) -> list[dict]:
422-    return [json.loads(l) for l in Path(path).read_text().splitlines() if l.strip()]
423-
424-
425-def _eval_one(swarm: Swarm, p: dict) -> tuple[bool, int, int, bool]:
426-    meter = BudgetMeter()
427-    ok = violated = False
428-    try:
429-        pred = swarm.run(p["problem"], meter)

[stdout]
196:def run_symbolic_verify(pred_texts: list[str]) -> str:
197-    """Check candidate integer answers found in predecessor outputs. NON-LLM, FREE.
198-
199-    Collects integer candidates the predecessors proposed (via the grader's own
200-    parser, plus any code_exec computed candidate), rejects out-of-[0,999], and
201-    reports whether the valid candidates agree (else a majority pick).
202-    """
203-    cands: list[int] = []
204-    for t in pred_texts:
205-        for m in re.finditer(r"computed integer candidate\s*=\s*(-?\d+)", t):
206-            cands.append(int(m.group(1)))
207-        v = parse_answer(t)
208-        if v is not None:
209-            cands.append(v)
210-    if not cands:
211-        return "[symbolic_verify] no integer candidate found to check."
212-    valid = [c for c in cands if 0 <= c <= 999]
213-    invalid = [c for c in cands if not (0 <= c <= 999)]
214-    lines = [f"[symbolic_verify] candidates seen: {cands}"]
215-    if invalid:
216-        lines.append(f"[symbolic_verify] REJECTED out-of-range candidates: {invalid}")
217-    if valid:
218-        uniq = sorted(set(valid))
219-        if len(uniq) == 1:
220-            lines.append(f"[symbolic_verify] all valid candidates AGREE on {uniq[0]}.")
221-        else:
222-            from collections import Counter
223-            c = Counter(valid)
224-            top = max(uniq, key=lambda x: (c[x], -valid.index(x)))
225-            lines.append(
226-                f"[symbolic_verify] valid candidates DISAGREE {dict(c)}; "
227-                f"majority pick = {top}."
228-            )
=== parse_answer ===
407:def parse_answer(text: str) -> int | None:
408-    text = _strip_think(text)
409-    for pat in (r"<answer>\s*(\d{1,3})\s*</answer>",
410-                r"\\boxed\{\s*(\d{1,3})\s*\}",
411-                r"(?:final answer|answer)\D{0,10}(\d{1,3})",
412-                r"(\d{1,3})\s*$"):
413-        m = re.search(pat, text, re.I)
414-        if m:
415-            v = int(m.group(1))
416-            if 0 <= v <= 999:
417-                return v
418-    return None
419-
420-
421-def load_problems(path: str | Path) -> list[dict]:
422-    return [json.loads(l) for l in Path(path).read_text().splitlines() if l.strip()]
423-
424-
425-def _eval_one(swarm: Swarm, p: dict) -> tuple[bool, int, int, bool]:
426-    meter = BudgetMeter()
427-    ok = violated = False
428-    try:
429-        pred = swarm.run(p["problem"], meter)
[metadata] {"noOutputExpected": false}

The file /app/methods/main/space.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/methods/main/space.py", "oldString": "    # free tally of every integer candidate anywhere upstream\n    if spec.verify:\n        if spec.verify_src == \"code_and_mid\":\n            srcs = list(idx[\"code\"]) + list(idx[\"mid\"])\n        elif spec.verify_src == \"mid_first\":  # tie-breaks favour the refiner (earliest listed wins)\n            srcs = list(idx[\"mid\"]) + list(idx[\"code\"]) + list(idx[\"solvers\"])\n        else:\n            srcs = list(idx[\"solvers\"]) + list(idx[\"code\"]) + list(idx[\"mid\"])", "newString": "    # Free tally of every integer candidate anywhere upstream.\n    #\n    # Edge ORDER here is a real design lever, not cosmetics: run_symbolic_verify\n    # breaks ties with `max(uniq, key=lambda x: (c[x], -valid.index(x)))`, i.e. the\n    # earliest-listed predecessor wins a tie. And a code_exec predecessor casts TWO\n    # votes for the same value (the \"computed integer candidate = N\" regex match plus\n    # parse_answer's trailing-number match on its stdout). Together those two facts\n    # let the edge list *encode a decision rule for free*:\n    #   [reasoner_a, reasoner_b, code]  ->  two agreeing reasoners (2 votes) tie with\n    #   code (2 votes) and win the tie-break, so they override the program; a lone\n    #   reasoner (1 vote) does not. Measured: reasoners that agree are ~0.95 accurate.\n    # Conversely a program-writing solver's OWN parse_answer is near-garbage\n    # (pot2: acc 0.056, parses at all 14%), so listing it first hands it ties it\n    # should never win.\n    pot_like = {\"pot\", \"pot2\", \"pot3\", \"hybrid\"}\n    reasoners = [i for i, s in zip(idx[\"solvers\"], spec.solvers) if s not in pot_like]\n    coders = [i for i, s in zip(idx[\"solvers\"], spec.solvers) if s in pot_like]\n    if spec.verify:\n        if spec.verify_src == \"code_and_mid\":\n            srcs = list(idx[\"code\"]) + list(idx[\"mid\"])\n        elif spec.verify_src == \"mid_first\":  # tie-breaks favour the refiner (earliest listed wins)\n            srcs = list(idx[\"mid\"]) + list(idx[\"code\"]) + list(idx[\"solvers\"])\n        elif spec.verify_src == \"reasoners_first\":\n            # reasoners get the tie-break; coder solvers still vote, but last\n            srcs = reasoners + list(idx[\"mid\"]) + list(idx[\"code\"]) + coders\n        elif spec.verify_src == \"no_pot_pred\":\n            # a program-writing solver speaks ONLY through code_exec\n            srcs = reasoners + list(idx[\"mid\"]) + list(idx[\"code\"])\n        else:\n            srcs = list(idx[\"solvers\"]) + list(idx[\"code\"]) + list(idx[\"mid\"])", "originalFile": "\"\"\"The joint node+edge search space: a structured graph generator.\n\nAn 8-node graph over 36 candidate edges is a 2^36 space; GPTSwarm-REINFORCE\nexplores it with one scalar reward per rollout, which is hopeless sample\nefficiency on a 30-problem train set. This module instead exposes a *structured*\nparameterisation (which templates occupy which slots, which free non-LLM nodes are\nwired in, who feeds the decider) that concentrates probability mass on graphs that\nare executable and meaningful, and the optimizer then does per-edge counterfactual\nrefinement inside it. Slots are what get searched; edges are what get attributed.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass, field\n\nfrom swarm import Node\n\nfrom bank import DECIDERS, REFINERS, SOLVERS\n\nPROBLEM = -1\n\n\n@dataclass\nclass Spec:\n    \"\"\"A point in the structured space.\"\"\"\n    solvers: tuple[str, ...] = (\"pot\", \"budget\")\n    code1: bool = True                 # free code_exec over the solvers\n    code1_src: str = \"pot_only\"        # \"pot_only\" | \"all\"\n    mid: str | None = None             # REFINERS key, an extra LLM node\n    mid_src: str = \"all\"               # what the refiner sees\n    code2: bool = False                # free code_exec over the refiner\n    verify: bool = True                # free symbolic_verify tally\n    verify_src: str = \"all\"            # \"all\" | \"code_and_mid\" | \"mid_first\"\n    decider: str = \"transcribe\"\n    no_think: bool = False             # Qwen soft switch on the decider\n    dec_src: tuple[str, ...] = (\"verify\",)  # subset of {\"problem\",\"solvers\",\"code\",\"mid\",\"verify\"}\n    tag: str = \"\"\n\n    def key(self) -> str:\n        return (f\"{'+'.join(self.solvers)}|c1={int(self.code1)}:{self.code1_src}|\"\n                f\"mid={self.mid}:{self.mid_src}|c2={int(self.code2)}|\"\n                f\"v={int(self.verify)}:{self.verify_src}|d={self.decider}\"\n                f\"{'/nt' if self.no_think else ''}|src={'+'.join(sorted(self.dec_src))}\")\n\n\nNO_THINK = \"/no_think \"\n\n\ndef build(spec: Spec) -> tuple[list[Node], list[tuple[int, int]], dict]:\n    \"\"\"Materialise a Spec into (nodes, edges, index-map). Topological by construction.\"\"\"\n    nodes: list[Node] = []\n    edges: list[tuple[int, int]] = []\n    idx: dict[str, list[int]] = {\"solvers\": [], \"code\": [], \"mid\": [], \"verify\": []}\n\n    def add(n: Node) -> int:\n        nodes.append(n)\n        return len(nodes) - 1\n\n    # layer 1: solvers, fed by PROBLEM\n    for k, s in enumerate(spec.solvers):\n        i = add(Node(f\"solver_{s}{k if spec.solvers.count(s) > 1 else ''}\", SOLVERS[s], \"llm\"))\n        edges.append((PROBLEM, i))\n        idx[\"solvers\"].append(i)\n\n    # free code execution over solver output (reads RAW text, so it also\n    # recovers programs from truncated <think> blocks)\n    if spec.code1:\n        srcs = ([i for i, s in zip(idx[\"solvers\"], spec.solvers) if s in (\"pot\", \"pot2\", \"hybrid\", \"recode\")]\n                if spec.code1_src == \"pot_only\" else list(idx[\"solvers\"]))\n        if srcs:\n            c = add(Node(\"code_exec\", \"\", \"code_exec\"))\n            for s in srcs:\n                edges.append((s, c))\n            idx[\"code\"].append(c)\n\n    # optional refiner: a second round of real work (self-repair / finish-the-proof)\n    if spec.mid:\n        srcs = list(idx[\"solvers\"]) + list(idx[\"code\"])\n        if spec.mid_src == \"code_only\" and idx[\"code\"]:\n            srcs = list(idx[\"code\"])\n        m = add(Node(f\"refine_{spec.mid}\", REFINERS[spec.mid], \"llm\"))\n        edges.append((PROBLEM, m))\n        for s in srcs:\n            edges.append((s, m))\n        idx[\"mid\"].append(m)\n        if spec.code2:\n            c2 = add(Node(\"code_exec2\", \"\", \"code_exec\"))\n            edges.append((m, c2))\n            idx[\"code\"].append(c2)\n\n    # free tally of every integer candidate anywhere upstream\n    if spec.verify:\n        if spec.verify_src == \"code_and_mid\":\n            srcs = list(idx[\"code\"]) + list(idx[\"mid\"])\n        elif spec.verify_src == \"mid_first\":  # tie-breaks favour the refiner (earliest listed wins)\n            srcs = list(idx[\"mid\"]) + list(idx[\"code\"]) + list(idx[\"solvers\"])\n        else:\n            srcs = list(idx[\"solvers\"]) + list(idx[\"code\"]) + list(idx[\"mid\"])\n        srcs = [s for s in srcs if s is not None]\n        if srcs:\n            v = add(Node(\"verify\", \"\", \"symbolic_verify\"))\n            for s in srcs:\n                edges.append((s, v))\n            idx[\"verify\"].append(v)\n\n    # decision (always last, always LLM)\n    tmpl = DECIDERS[spec.decider]\n    if spec.no_think:\n        tmpl = NO_THINK + tmpl\n    d = add(Node(\"decision\", tmpl, \"llm\"))\n    want: list[int] = []\n    if \"problem\" in spec.dec_src:\n        edges.append((PROBLEM, d))\n    if \"solvers\" in spec.dec_src:\n        want += idx[\"solvers\"]\n    if \"code\" in spec.dec_src:\n        want += idx[\"code\"]\n    if \"mid\" in spec.dec_src:\n        want += idx[\"mid\"]\n    if \"verify\" in spec.dec_src:\n        want += idx[\"verify\"]\n    for s in sorted(set(want)):\n        edges.append((s, d))\n    if not want and (PROBLEM, d) not in edges:\n        edges.append((PROBLEM, d))\n    return nodes, edges, idx\n\n\ndef n_llm(nodes, edges) -> int:\n    from swarm import Swarm\n    sw = Swarm(nodes=list(nodes), edges=list(edges))\n    return sum(1 for i in sw._active_nodes() if nodes[i].kind == \"llm\")\n\n\ndef legal(nodes, edges) -> bool:\n    from swarm import Swarm\n    try:\n        Swarm(nodes=list(nodes), edges=list(edges)).validate()\n        return True\n    except Exception:  # noqa: BLE001\n        return False\n", "structuredPatch": [{"oldStart": 88, "oldLines": 12, "newStart": 88, "newLines": 34, "lines": ["             edges.append((m, c2))", "             idx[\"code\"].append(c2)", " ", "-    # free tally of every integer candidate anywhere upstream", "+    # Free tally of every integer candidate anywhere upstream.", "+    #", "+    # Edge ORDER here is a real design lever, not cosmetics: run_symbolic_verify", "+    # breaks ties with `max(uniq, key=lambda x: (c[x], -valid.index(x)))`, i.e. the", "+    # earliest-listed predecessor wins a tie. And a code_exec predecessor casts TWO", "+    # votes for the same value (the \"computed integer candidate = N\" regex match plus", "+    # parse_answer's trailing-number match on its stdout). Together those two facts", "+    # let the edge list *encode a decision rule for free*:", "+    #   [reasoner_a, reasoner_b, code]  ->  two agreeing reasoners (2 votes) tie with", "+    #   code (2 votes) and win the tie-break, so they override the program; a lone", "+    #   reasoner (1 vote) does not. Measured: reasoners that agree are ~0.95 accurate.", "+    # Conversely a program-writing solver's OWN parse_answer is near-garbage", "+    # (pot2: acc 0.056, parses at all 14%), so listing it first hands it ties it", "+    # should never win.", "+    pot_like = {\"pot\", \"pot2\", \"pot3\", \"hybrid\"}", "+    reasoners = [i for i, s in zip(idx[\"solvers\"], spec.solvers) if s not in pot_like]", "+    coders = [i for i, s in zip(idx[\"solvers\"], spec.solvers) if s in pot_like]", "     if spec.verify:", "         if spec.verify_src == \"code_and_mid\":", "             srcs = list(idx[\"code\"]) + list(idx[\"mid\"])", "         elif spec.verify_src == \"mid_first\":  # tie-breaks favour the refiner (earliest listed wins)", "             srcs = list(idx[\"mid\"]) + list(idx[\"code\"]) + list(idx[\"solvers\"])", "+        elif spec.verify_src == \"reasoners_first\":", "+            # reasoners get the tie-break; coder solvers still vote, but last", "+            srcs = reasoners + list(idx[\"mid\"]) + list(idx[\"code\"]) + coders", "+        elif spec.verify_src == \"no_pot_pred\":", "+            # a program-writing solver speaks ONLY through code_exec", "+            srcs = reasoners + list(idx[\"mid\"]) + list(idx[\"code\"])", "         else:", "             srcs = list(idx[\"solvers\"]) + list(idx[\"code\"]) + list(idx[\"mid\"])", "         srcs = [s for s in srcs if s is not None]"]}], "userModified": false, "replaceAll": false}

File created successfully at: /app/methods/main/tally.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/app/methods/main/tally.py", "content": "\"\"\"Free offline search over the FREE aggregator.\n\nThe `symbolic_verify` node costs nothing at grade time, but its behaviour is fully\ndetermined by (a) which predecessors feed it and (b) in what order. Both are just\nedges. So the entire design space of the aggregator can be enumerated *offline*\nagainst cached solver traces at zero LLM cost -- no rollouts, no gradient, no\nvariance. This is the part of the graph REINFORCE spends its whole sample budget\nlearning, and it is exactly the part that can be solved in closed form.\n\nUsage:  python3 tally.py [draws] [split]\n\"\"\"\nfrom __future__ import annotations\n\nimport itertools\nimport re\nimport sys\nfrom collections import Counter\n\nsys.path.insert(0, \"/app/methods/main\")\n\nimport swarm as S\nfrom bank import SOLVERS\nfrom harness import MAXDUP, TraceCache\n\nDRAWS = [int(x) for x in (sys.argv[1].split(\",\") if len(sys.argv) > 1 else [0, 1, 2])] \\\n    if len(sys.argv) > 1 else [0, 1, 2]\nSPLIT = sys.argv[2] if len(sys.argv) > 2 else \"train\"\n\nPOT_LIKE = {\"pot\", \"pot2\", \"pot3\", \"hybrid\"}\n\nprobs = S.load_problems(f\"/app/data/{SPLIT}.jsonl\")\ncache = TraceCache()\n\n# ── harvest every cached solver trace into the two signals a graph can consume ──\ncell: dict[tuple[str, str, int], dict] = {}\nfor t, tmpl in SOLVERS.items():\n    for p in probs:\n        for d in DRAWS:\n            r = cache.get(tmpl.replace(\"{problem}\", p[\"problem\"]), d * MAXDUP)\n            if not r:\n                continue\n            txt = r[\"text\"]\n            co = S.run_code_exec([txt])\n            m = re.search(r\"computed integer candidate = (-?\\d+)\", co)\n            cell[(t, str(p[\"id\"]), d)] = {\n                \"pred\": S.parse_answer(txt),\n                \"code\": int(m.group(1)) if m else None,\n                \"gold\": int(p[\"answer\"]),\n            }\n\nhave = sorted({k[0] for k in cell})\nprint(f\"split={SPLIT} draws={DRAWS} templates cached: {have}\")\nfor t in have:\n    ks = [k for k in cell if k[0] == t]\n    pa = sum(cell[k][\"pred\"] is not None for k in ks)\n    ca = sum(cell[k][\"code\"] is not None for k in ks)\n    pok = sum(cell[k][\"pred\"] == cell[k][\"gold\"] for k in ks)\n    cok = sum(cell[k][\"code\"] == cell[k][\"gold\"] for k in ks)\n    print(f\"  {t:9s} n={len(ks):3d} predParsed={pa/len(ks):.2f} predAcc={pok/len(ks):.3f} \"\n          f\"codeOK={ca/len(ks):.2f} codeAcc={cok/len(ks):.3f}\")\n\n\ndef tally(order: tuple[str, ...], keys, drop_pot_pred: bool) -> tuple[float, float]:\n    \"\"\"Replay run_symbolic_verify exactly: predecessor order fixes the tie-break,\n    and a code_exec predecessor double-votes.\"\"\"\n    good = cov = 0\n    for kk in keys:\n        cands: list[int] = []\n        # reasoner/refiner preds first, in the given order; code_exec last\n        for t in order:\n            c = cell[(t, kk[0], kk[1])]\n            if c[\"pred\"] is not None and not (drop_pot_pred and t in POT_LIKE):\n                cands.append(c[\"pred\"])\n        for t in order:\n            c = cell[(t, kk[0], kk[1])]\n            if c[\"code\"] is not None:\n                cands.extend([c[\"code\"], c[\"code\"]])\n        cands = [c for c in cands if 0 <= c <= 999]\n        if not cands:\n            continue\n        cov += 1\n        cnt = Counter(cands)\n        top = max(cnt, key=lambda x: (cnt[x], -cands.index(x)))\n        good += int(top == cell[(order[0], kk[0], kk[1])][\"gold\"])\n    n = len(keys)\n    return good / n, cov / n\n\n\nrows = []\nfor k in (1, 2, 3):\n    for order in itertools.permutations(have, k):\n        keys = sorted({(kk[1], kk[2]) for kk in cell if kk[0] == order[0]})\n        keys = [x for x in keys if all((t, x[0], x[1]) in cell for t in order)]\n        if len(keys) < 30:\n            continue\n        for drop in (False, True):\n            if drop and not any(t in POT_LIKE for t in order):\n                continue\n            acc, cov = tally(order, keys, drop)\n            rows.append({\"order\": order, \"drop_pot_pred\": drop, \"acc\": acc,\n                         \"cov\": cov, \"n\": len(keys), \"k\": k})\n\nrows.sort(key=lambda r: -r[\"acc\"])\nprint(f\"\\n--- free aggregator lattice ({len(rows)} designs; order = edge order into \"\n      f\"symbolic_verify) ---\")\nprint(f\"{'acc':>6} {'n':>4} {'cov':>5}  calls  design\")\nseen_k: Counter = Counter()\nfor r in rows:\n    if seen_k[r[\"k\"]] >= 8:\n        continue\n    seen_k[r[\"k\"]] += 1\n    d = \"+\".join(r[\"order\"]) + (\"  [pot speaks only via code]\" if r[\"drop_pot_pred\"] else \"\")\n    print(f\"{r['acc']:.3f} {r['n']:4d} {r['cov']:.3f}  {r['k']}+1    {d}\")\n\nprint(\"\\n--- best design at each solver count (this is the cost/accuracy frontier) ---\")\nfor k in (1, 2, 3):\n    sub = [r for r in rows if r[\"k\"] == k]\n    if sub:\n        b = sub[0]\n        print(f\"  {k} solver(s) -> tally acc {b['acc']:.3f} (n={b['n']}) \"\n              f\"order={'+'.join(b['order'])}{' drop_pot_pred' if b['drop_pot_pred'] else ''}\")\n", "structuredPatch": [], "originalFile": null, "userModified": false}

split=train draws=[0, 1, 2] templates cached: ['alt', 'budget', 'cot', 'decisive', 'hybrid', 'pot', 'pot2']
  alt       n= 90 predParsed=0.80 predAcc=0.633 codeOK=0.00 codeAcc=0.000
  budget    n= 90 predParsed=0.79 predAcc=0.600 codeOK=0.00 codeAcc=0.000
  cot       n= 86 predParsed=0.80 predAcc=0.616 codeOK=0.00 codeAcc=0.000
  decisive  n= 90 predParsed=0.79 predAcc=0.622 codeOK=0.00 codeAcc=0.000
  hybrid    n= 30 predParsed=0.90 predAcc=0.567 codeOK=0.53 codeAcc=0.467
  pot       n= 69 predParsed=0.06 predAcc=0.014 codeOK=0.86 codeAcc=0.739
  pot2      n= 90 predParsed=0.14 predAcc=0.056 codeOK=0.72 codeAcc=0.600

--- free aggregator lattice (478 designs; order = edge order into symbolic_verify) ---
   acc    n   cov  calls  design
0.884   69 0.986  3+1    alt+pot+pot2
0.884   69 0.986  3+1    alt+pot+pot2  [pot speaks only via code]
0.884   69 0.986  3+1    alt+pot2+pot
0.884   69 0.986  3+1    alt+pot2+pot  [pot speaks only via code]
0.884   69 0.986  3+1    budget+pot+pot2
0.884   69 0.986  3+1    budget+pot+pot2  [pot speaks only via code]
0.884   69 0.986  3+1    cot+pot+pot2
0.884   69 0.986  3+1    cot+pot+pot2  [pot speaks only via code]
0.855   69 0.986  2+1    budget+pot
0.855   69 0.986  2+1    budget+pot  [pot speaks only via code]
0.855   69 0.986  2+1    pot+budget  [pot speaks only via code]
0.841   69 0.971  2+1    pot+pot2
0.826   69 0.957  2+1    alt+pot
0.826   69 0.942  2+1    alt+pot  [pot speaks only via code]
0.826   69 0.957  2+1    decisive+pot
0.826   69 0.942  2+1    decisive+pot  [pot speaks only via code]
0.754   69 0.884  1+1    pot
0.739   69 0.841  1+1    pot  [pot speaks only via code]
0.656   90 0.833  1+1    pot2
0.633   90 0.800  1+1    alt
0.622   90 0.789  1+1    decisive
0.616   86 0.802  1+1    cot
0.600   90 0.789  1+1    budget
0.600   30 0.900  1+1    hybrid

--- best design at each solver count (this is the cost/accuracy frontier) ---
  1 solver(s) -> tally acc 0.754 (n=69) order=pot
  2 solver(s) -> tally acc 0.855 (n=69) order=budget+pot
  3 solver(s) -> tally acc 0.884 (n=69) order=alt+pot+pot2

[stdout]
split=train draws=[0, 1, 2] templates cached: ['alt', 'budget', 'cot', 'decisive', 'hybrid', 'pot', 'pot2']
  alt       n= 90 predParsed=0.80 predAcc=0.633 codeOK=0.00 codeAcc=0.000
  budget    n= 90 predParsed=0.79 predAcc=0.600 codeOK=0.00 codeAcc=0.000
  cot       n= 86 predParsed=0.80 predAcc=0.616 codeOK=0.00 codeAcc=0.000
  decisive  n= 90 predParsed=0.79 predAcc=0.622 codeOK=0.00 codeAcc=0.000
  hybrid    n= 30 predParsed=0.90 predAcc=0.567 codeOK=0.53 codeAcc=0.467
  pot       n= 69 predParsed=0.06 predAcc=0.014 codeOK=0.86 codeAcc=0.739
  pot2      n= 90 predParsed=0.14 predAcc=0.056 codeOK=0.72 codeAcc=0.600

--- free aggregator lattice (478 designs; order = edge order into symbolic_verify) ---
   acc    n   cov  calls  design
0.884   69 0.986  3+1    alt+pot+pot2
0.884   69 0.986  3+1    alt+pot+pot2  [pot speaks only via code]
0.884   69 0.986  3+1    alt+pot2+pot
0.884   69 0.986  3+1    alt+pot2+pot  [pot speaks only via code]
0.884   69 0.986  3+1    budget+pot+pot2
0.884   69 0.986  3+1    budget+pot+pot2  [pot speaks only via code]
0.884   69 0.986  3+1    cot+pot+pot2
0.884   69 0.986  3+1    cot+pot+pot2  [pot speaks only via code]
0.855   69 0.986  2+1    budget+pot
0.855   69 0.986  2+1    budget+pot  [pot speaks only via code]
0.855   69 0.986  2+1    pot+budget  [pot speaks only via code]
0.841   69 0.971  2+1    pot+pot2
0.826   69 0.957  2+1    alt+pot
0.826   69 0.942  2+1    alt+pot  [pot speaks only via code]
0.826   69 0.957  2+1    decisive+pot
0.826   69 0.942  2+1    decisive+pot  [pot speaks only via code]
0.754   69 0.884  1+1    pot
0.739   69 0.841  1+1    pot  [pot speaks only via code]
0.656   90 0.833  1+1    pot2
0.633   90 0.800  1+1    alt
0.622   90 0.789  1+1    decisive
0.616   86 0.802  1+1    cot
0.600   90 0.789  1+1    budget
0.600   30 0.900  1+1    hybrid

--- best design at each solver count (this is the cost/accuracy frontier) ---
  1 solver(s) -> tally acc 0.754 (n=69) order=pot
  2 solver(s) -> tally acc 0.855 (n=69) order=budget+pot
  3 solver(s) -> tally acc 0.884 (n=69) order=alt+pot+pot2
[metadata] {"noOutputExpected": false}

pot cells: n= 69
  by draw: {2: 21, 0: 30, 1: 18}
  distinct problems: 30 of 30
  pot problem ids: [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]
  missing ids     : []

SAME-CELLS comparison on the pot subset:
  alt       n=69 predAcc=0.710 codeAcc=0.000
  budget    n=69 predAcc=0.652 codeAcc=0.000
  cot       n=69 predAcc=0.652 codeAcc=0.000
  decisive  n=69 predAcc=0.710 codeAcc=0.000
  pot       n=69 predAcc=0.014 codeAcc=0.739
  pot2      n=69 predAcc=0.043 codeAcc=0.638

[stdout]
pot cells: n= 69
  by draw: {2: 21, 0: 30, 1: 18}
  distinct problems: 30 of 30
  pot problem ids: [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]
  missing ids     : []

SAME-CELLS comparison on the pot subset:
  alt       n=69 predAcc=0.710 codeAcc=0.000
  budget    n=69 predAcc=0.652 codeAcc=0.000
  cot       n=69 predAcc=0.652 codeAcc=0.000
  decisive  n=69 predAcc=0.710 codeAcc=0.000
  pot       n=69 predAcc=0.014 codeAcc=0.739
  pot2      n=69 predAcc=0.043 codeAcc=0.638
[metadata] {"noOutputExpected": false}

135 (template,problem,draw) cells, 135 need LLM calls
=== T ===
180 (template,problem,draw) cells, 180 need LLM calls
=== time ===
15839
785

[stdout]
135 (template,problem,draw) cells, 135 need LLM calls
=== T ===
180 (template,problem,draw) cells, 180 need LLM calls
=== time ===
15839
785
[metadata] {"noOutputExpected": false}

277:def candidate_specs(solver_pool) -> list[Spec]:
278-    """The shortlist the offline phases justify: cost tiers 2/3/4/5 LLM calls,
279-    breadth (self-consistency) vs depth (conditional re-solve), and decider style."""
280-    S1, S2, S3 = solver_pool[0], solver_pool[1], solver_pool[2]
281-    out = [
282-        # --- 3 LLM calls: two solvers + free aggregation + transcriber ---
283-        Spec(solvers=(S1, S2), decider="transcribe", no_think=True, dec_src=("verify",),
284-             tag="3c/transcribe-nt"),
285-        Spec(solvers=(S1, S2), decider="transcribe", no_think=False, dec_src=("verify",),
286-             tag="3c/transcribe-think"),
287-        Spec(solvers=(S1, S2), decider="judge", no_think=False,
288-             dec_src=("solvers", "code", "verify"), tag="3c/judge"),
289-        Spec(solvers=(S1, S2), decider="plain", no_think=False,
290-             dec_src=("solvers", "code", "verify"), tag="3c/plain-baselinelike"),
291-        Spec(solvers=(S1, S3), decider="transcribe", no_think=True, dec_src=("verify",),
292-             tag="3c/alt-pair"),
293-        # --- 4 LLM calls, breadth: three solvers ---
294-        Spec(solvers=(S1, S2, S3), decider="transcribe", no_think=True, dec_src=("verify",),
295-             tag="4c/three-solvers"),
296-        # --- 4 LLM calls, depth: conditional re-solve + a second FREE execution ---
297-        Spec(solvers=(S1, S2), mid="resolve", code2=True, verify_src="mid_first",
298-             decider="transcribe", no_think=True, dec_src=("verify",), tag="4c/resolve"),
299-        Spec(solvers=(S1, S2), mid="resolve", code2=True, verify_src="mid_first",
300-             decider="judge", dec_src=("mid", "verify"), tag="4c/resolve+judge"),
301-        # --- 3 LLM calls, depth: conditional re-solve packed into the DECISION call ---
302-        Spec(solvers=(S1, S2), decider="resolve", no_think=False,
303-             dec_src=("solvers", "code", "verify"), tag="3c/resolve-in-decision"),
304-        Spec(solvers=(S1, S2), decider="resolve", no_think=False, dec_src=("code", "verify"),
305-             tag="3c/resolve-in-decision-lean"),
306-        # --- 2 LLM calls: single solver + transcriber (cheapest legal-ish tier) ---
307-        Spec(solvers=(S1,), decider="transcribe", no_think=True, dec_src=("verify",),
308-             tag="2c/solo-pot"),
309-        Spec(solvers=(S2,), decider="transcribe", no_think=True, dec_src=("verify",),
310-             tag="2c/solo-reasoner"),
311-    ]
312-    return out
313-
314-
315-def phase_struct(specs, problems, draws, cache) -> list[dict]:
316-    res = []
317-    for sp in specs:
318-        r = evaluate_spec(sp, problems, draws, cache)
319-        r["tag"] = sp.tag
320-        res.append(r)
321-        print(f"  {sp.tag:22s} acc={r['acc']:.3f} calls={r['avg_calls']:.2f} "
322-              f"obj={r['obj']:.3f} nllm={r['n_llm']} edges={r['n_edges']}", flush=True)
=== main tail ===

[stdout]
277:def candidate_specs(solver_pool) -> list[Spec]:
278-    """The shortlist the offline phases justify: cost tiers 2/3/4/5 LLM calls,
279-    breadth (self-consistency) vs depth (conditional re-solve), and decider style."""
280-    S1, S2, S3 = solver_pool[0], solver_pool[1], solver_pool[2]
281-    out = [
282-        # --- 3 LLM calls: two solvers + free aggregation + transcriber ---
283-        Spec(solvers=(S1, S2), decider="transcribe", no_think=True, dec_src=("verify",),
284-             tag="3c/transcribe-nt"),
285-        Spec(solvers=(S1, S2), decider="transcribe", no_think=False, dec_src=("verify",),
286-             tag="3c/transcribe-think"),
287-        Spec(solvers=(S1, S2), decider="judge", no_think=False,
288-             dec_src=("solvers", "code", "verify"), tag="3c/judge"),
289-        Spec(solvers=(S1, S2), decider="plain", no_think=False,
290-             dec_src=("solvers", "code", "verify"), tag="3c/plain-baselinelike"),
291-        Spec(solvers=(S1, S3), decider="transcribe", no_think=True, dec_src=("verify",),
292-             tag="3c/alt-pair"),
293-        # --- 4 LLM calls, breadth: three solvers ---
294-        Spec(solvers=(S1, S2, S3), decider="transcribe", no_think=True, dec_src=("verify",),
295-             tag="4c/three-solvers"),
296-        # --- 4 LLM calls, depth: conditional re-solve + a second FREE execution ---
297-        Spec(solvers=(S1, S2), mid="resolve", code2=True, verify_src="mid_first",
298-             decider="transcribe", no_think=True, dec_src=("verify",), tag="4c/resolve"),
299-        Spec(solvers=(S1, S2), mid="resolve", code2=True, verify_src="mid_first",
300-             decider="judge", dec_src=("mid", "verify"), tag="4c/resolve+judge"),
301-        # --- 3 LLM calls, depth: conditional re-solve packed into the DECISION call ---
302-        Spec(solvers=(S1, S2), decider="resolve", no_think=False,
303-             dec_src=("solvers", "code", "verify"), tag="3c/resolve-in-decision"),
304-        Spec(solvers=(S1, S2), decider="resolve", no_think=False, dec_src=("code", "verify"),
305-             tag="3c/resolve-in-decision-lean"),
306-        # --- 2 LLM calls: single solver + transcriber (cheapest legal-ish tier) ---
307-        Spec(solvers=(S1,), decider="transcribe", no_think=True, dec_src=("verify",),
308-             tag="2c/solo-pot"),
309-        Spec(solvers=(S2,), decider="transcribe", no_think=True, dec_src=("verify",),
310-             tag="2c/solo-reasoner"),
311-    ]
312-    return out
313-
314-
315-def phase_struct(specs, problems, draws, cache) -> list[dict]:
316-    res = []
317-    for sp in specs:
318-        r = evaluate_spec(sp, problems, draws, cache)
319-        r["tag"] = sp.tag
320-        res.append(r)
321-        print(f"  {sp.tag:22s} acc={r['acc']:.3f} calls={r['avg_calls']:.2f} "
322-              f"obj={r['obj']:.3f} nllm={r['n_llm']} edges={r['n_edges']}", flush=True)
=== main tail ===
[metadata] {"returnCodeInterpretation": "No matches found", "noOutputExpected": false}

The file /app/methods/main/optimize.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/methods/main/optimize.py", "oldString": "        # --- 2 LLM calls: single solver + transcriber (cheapest legal-ish tier) ---\n        Spec(solvers=(S1,), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),\n             tag=\"2c/solo-pot\"),\n        Spec(solvers=(S2,), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),\n             tag=\"2c/solo-reasoner\"),\n    ]\n    return out", "newString": "        # --- 2 LLM calls: single solver + transcriber (cheapest legal-ish tier) ---\n        Spec(solvers=(S1,), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),\n             tag=\"2c/solo-pot\"),\n        Spec(solvers=(S2,), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),\n             tag=\"2c/solo-reasoner\"),\n        # --- the aggregator-ordering variants the free tally lattice singled out.\n        #     Identical LLM cost to 3c/transcribe-nt; the only difference is the ORDER\n        #     of the edges into symbolic_verify and whether the program-writing solver\n        #     is allowed to cast its own (near-garbage) parse_answer vote.\n        Spec(solvers=(S1, S2), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),\n             verify_src=\"reasoners_first\", tag=\"3c/nt-reasoners-first\"),\n        Spec(solvers=(S1, S2), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),\n             verify_src=\"no_pot_pred\", tag=\"3c/nt-no-pot-pred\"),\n    ]\n    return out", "originalFile": null, "structuredPatch": [{"oldStart": 308, "oldLines": 6, "newStart": 308, "newLines": 14, "lines": ["              tag=\"2c/solo-pot\"),", "         Spec(solvers=(S2,), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),", "              tag=\"2c/solo-reasoner\"),", "+        # --- the aggregator-ordering variants the free tally lattice singled out.", "+        #     Identical LLM cost to 3c/transcribe-nt; the only difference is the ORDER", "+        #     of the edges into symbolic_verify and whether the program-writing solver", "+        #     is allowed to cast its own (near-garbage) parse_answer vote.", "+        Spec(solvers=(S1, S2), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),", "+             verify_src=\"reasoners_first\", tag=\"3c/nt-reasoners-first\"),", "+        Spec(solvers=(S1, S2), decider=\"transcribe\", no_think=True, dec_src=(\"verify\",),", "+             verify_src=\"no_pot_pred\", tag=\"3c/nt-no-pot-pred\"),", "     ]", "     return out", " "]}], "userModified": false, "replaceAll": false}

337:    ap.add_argument("--phase", default="all")
338:    ap.add_argument("--draws", type=int, default=2)
339:    ap.add_argument("--pool", default="pot2,decisive,alt")
340:    ap.add_argument("--out", default=str(Path(__file__).parent / "graph.json"))
341:    ap.add_argument("--valtags", default="3c/transcribe-nt")
342:    ap.add_argument("--freeze", default="3c/transcribe-nt")
343:    a = ap.parse_args()
351:    if a.phase in ("all", "nodes"):
366:    if a.phase in ("all", "struct"):
375:    if a.phase in ("all", "val"):
386:    if a.phase in ("all", "freeze"):
400:    if a.phase in ("all", "attribute"):

[stdout]
337:    ap.add_argument("--phase", default="all")
338:    ap.add_argument("--draws", type=int, default=2)
339:    ap.add_argument("--pool", default="pot2,decisive,alt")
340:    ap.add_argument("--out", default=str(Path(__file__).parent / "graph.json"))
341:    ap.add_argument("--valtags", default="3c/transcribe-nt")
342:    ap.add_argument("--freeze", default="3c/transcribe-nt")
343:    a = ap.parse_args()
351:    if a.phase in ("all", "nodes"):
366:    if a.phase in ("all", "struct"):
375:    if a.phase in ("all", "val"):
386:    if a.phase in ("all", "freeze"):
400:    if a.phase in ("all", "attribute"):
[metadata] {"noOutputExpected": false}

330	              f"obj={r['obj']:.3f} nllm={r['n_llm']} edges={r['n_edges']}", flush=True)
331	    res.sort(key=lambda r: -r["obj"])
332	    return res
333	
334	
335	def main() -> None:
336	    ap = argparse.ArgumentParser()
337	    ap.add_argument("--phase", default="all")
338	    ap.add_argument("--draws", type=int, default=2)
339	    ap.add_argument("--pool", default="pot2,decisive,alt")
340	    ap.add_argument("--out", default=str(Path(__file__).parent / "graph.json"))
341	    ap.add_argument("--valtags", default="3c/transcribe-nt")
342	    ap.add_argument("--freeze", default="3c/transcribe-nt")
343	    a = ap.parse_args()
344	
345	    cache = TraceCache()
346	    train = S.load_problems(TRAIN)
347	    draws = list(range(a.draws))
348	    names = a.pool.split(",") if a.pool else list(SOLVERS)
349	    t0 = time.time()
350	
351	    if a.phase in ("all", "nodes"):
352	        st = phase_nodes(train, draws, cache, names)
353	        print(f"{'template':10s} {'acc':>6s} {'codeAcc':>8s} {'closure':>8s} {'condAcc':>8s} {'toks':>7s}")
354	        for t, v in sorted(st.items(), key=lambda kv: -kv[1]["best_signal"]):
355	            print(f"{t:10s} {v['acc']:6.3f} {v['code_acc']:8.3f} {v['closure']:8.3f} "
356	                  f"{v['cond_acc']:8.3f} {v['toks']:7.0f}")
357	        ens = phase_ensemble(st, names)
358	        print("\ntop free-tally ensembles (offline, zero new LLM calls):")
359	        for r in ens[:10]:
360	            print(f"  k={r['k']} {'+'.join(r['combo']):26s} {r['tally_acc']:.3f}")
361	        STATE.write_text(json.dumps({"nodes": {k: {kk: vv for kk, vv in v.items() if kk != 'cells'}
362	                                               for k, v in st.items()},
363	                                     "ens": [{"combo": list(r["combo"]), "acc": r["tally_acc"]}
364	                                             for r in ens[:20]]}, indent=1))
365	
366	    if a.phase in ("all", "struct"):
367	        pool = a.pool.split(",")
368	        print(f"\n[phase struct] cost-aware frontier over Specs (pool={pool}, draws={draws}):")
369	        res = phase_struct(candidate_specs(pool), train, draws, cache)
370	        print("\nranked by accuracy x efficiency:")
371	        for r in res:
372	            print(f"  {r['obj']:.3f}  acc={r['acc']:.3f} calls={r['avg_calls']:.2f}  {r['tag']}")
373	        Path(Path(__file__).parent / ".cats_struct.json").write_text(json.dumps(res, indent=1))
374	
375	    if a.phase in ("all", "val"):
376	        val = S.load_problems(VAL)
377	        specs = {sp.tag: sp for sp in candidate_specs(a.pool.split(","))}
378	        print(f"\n[phase val] held-out confirmation (draws={draws}):")
379	        for tag in a.valtags.split(","):
380	            if tag not in specs:
381	                continue
382	            r = evaluate_spec(specs[tag], val, draws, cache)
383	            print(f"  {tag:24s} val acc={r['acc']:.3f} calls={r['avg_calls']:.2f} "
384	                  f"obj={r['obj']:.3f}", flush=True)
385	
386	    if a.phase in ("all", "freeze"):
387	        specs = {sp.tag: sp for sp in candidate_specs(a.pool.split(","))}
388	        sp = specs[a.freeze]
389	        nodes, edges, _ = build(sp)
390	        sw = S.Swarm(nodes=nodes, edges=edges)
391	        sw.validate()
392	        assert sum(1 for n in nodes if n.kind == "llm") >= 3, "LLM-node gate"
393	        assert all(len(n.template) <= 2000 for n in nodes), "per-node template cap"
394	        assert sum(len(n.template) for n in nodes) <= 12000, "total template cap"
395	        sw.save(a.out)
396	        print(f"[freeze] {a.freeze} -> {a.out}: {len(nodes)} nodes, "
397	              f"{len(sw.active_edges())} active edges, "
398	              f"{sum(1 for n in nodes if n.kind=='llm')} LLM nodes")
399	
400	    if a.phase in ("all", "attribute"):
401	        best = json.loads((Path(__file__).parent / ".cats_incumbent.json").read_text())
402	        sp = Spec(**best)
403	        print(f"\n[phase attribute] paired leave-one-edge-out on {sp.key()}")
404	        at = attribute_edges(sp, train, draws, cache)
405	        print(f"  base acc={at['base']['acc']:.3f} obj={at['base']['obj']:.3f}")
406	        for r in at["edges"]:
407	            if "error" in r:
408	                print(f"  edge {r['edge']}: illegal/inactive ({r['error'][:40]})")
409	            else:
410	                print(f"  edge {str(r['edge']):10s} d_obj={r['delta_obj']:+.4f} "
411	                      f"d_acc={r['delta_acc']:+.3f} (ablated acc={r['acc']:.3f})")
412	        Path(Path(__file__).parent / ".cats_attrib.json").write_text(
413	            json.dumps({"base": {k: v for k, v in at["base"].items() if k != "detail"},
414	                        "edges": at["edges"]}, indent=1))

[metadata] {"type": "text", "file": {"filePath": "/app/methods/main/optimize.py", "content": "              f\"obj={r['obj']:.3f} nllm={r['n_llm']} edges={r['n_edges']}\", flush=True)\n    res.sort(key=lambda r: -r[\"obj\"])\n    return res\n\n\ndef main() -> None:\n    ap = argparse.ArgumentParser()\n    ap.add_argument(\"--phase\", default=\"all\")\n    ap.add_argument(\"--draws\", type=int, default=2)\n    ap.add_argument(\"--pool\", default=\"pot2,decisive,alt\")\n    ap.add_argument(\"--out\", default=str(Path(__file__).parent / \"graph.json\"))\n    ap.add_argument(\"--valtags\", default=\"3c/transcribe-nt\")\n    ap.add_argument(\"--freeze\", default=\"3c/transcribe-nt\")\n    a = ap.parse_args()\n\n    cache = TraceCache()\n    train = S.load_problems(TRAIN)\n    draws = list(range(a.draws))\n    names = a.pool.split(\",\") if a.pool else list(SOLVERS)\n    t0 = time.time()\n\n    if a.phase in (\"all\", \"nodes\"):\n        st = phase_nodes(train, draws, cache, names)\n        print(f\"{'template':10s} {'acc':>6s} {'codeAcc':>8s} {'closure':>8s} {'condAcc':>8s} {'toks':>7s}\")\n        for t, v in sorted(st.items(), key=lambda kv: -kv[1][\"best_signal\"]):\n            print(f\"{t:10s} {v['acc']:6.3f} {v['code_acc']:8.3f} {v['closure']:8.3f} \"\n                  f\"{v['cond_acc']:8.3f} {v['toks']:7.0f}\")\n        ens = phase_ensemble(st, names)\n        print(\"\\ntop free-tally ensembles (offline, zero new LLM calls):\")\n        for r in ens[:10]:\n            print(f\"  k={r['k']} {'+'.join(r['combo']):26s} {r['tally_acc']:.3f}\")\n        STATE.write_text(json.dumps({\"nodes\": {k: {kk: vv for kk, vv in v.items() if kk != 'cells'}\n                                               for k, v in st.items()},\n                                     \"ens\": [{\"combo\": list(r[\"combo\"]), \"acc\": r[\"tally_acc\"]}\n                                             for r in ens[:20]]}, indent=1))\n\n    if a.phase in (\"all\", \"struct\"):\n        pool = a.pool.split(\",\")\n        print(f\"\\n[phase struct] cost-aware frontier over Specs (pool={pool}, draws={draws}):\")\n        res = phase_struct(candidate_specs(pool), train, draws, cache)\n        print(\"\\nranked by accuracy x efficiency:\")\n        for r in res:\n            print(f\"  {r['obj']:.3f}  acc={r['acc']:.3f} calls={r['avg_calls']:.2f}  {r['tag']}\")\n        Path(Path(__file__).parent / \".cats_struct.json\").write_text(json.dumps(res, indent=1))\n\n    if a.phase in (\"all\", \"val\"):\n        val = S.load_problems(VAL)\n        specs = {sp.tag: sp for sp in candidate_specs(a.pool.split(\",\"))}\n        print(f\"\\n[phase val] held-out confirmation (draws={draws}):\")\n        for tag in a.valtags.split(\",\"):\n            if tag not in specs:\n                continue\n            r = evaluate_spec(specs[tag], val, draws, cache)\n            print(f\"  {tag:24s} val acc={r['acc']:.3f} calls={r['avg_calls']:.2f} \"\n                  f\"obj={r['obj']:.3f}\", flush=True)\n\n    if a.phase in (\"all\", \"freeze\"):\n        specs = {sp.tag: sp for sp in candidate_specs(a.pool.split(\",\"))}\n        sp = specs[a.freeze]\n        nodes, edges, _ = build(sp)\n        sw = S.Swarm(nodes=nodes, edges=edges)\n        sw.validate()\n        assert sum(1 for n in nodes if n.kind == \"llm\") >= 3, \"LLM-node gate\"\n        assert all(len(n.template) <= 2000 for n in nodes), \"per-node template cap\"\n        assert sum(len(n.template) for n in nodes) <= 12000, \"total template cap\"\n        sw.save(a.out)\n        print(f\"[freeze] {a.freeze} -> {a.out}: {len(nodes)} nodes, \"\n              f\"{len(sw.active_edges())} active edges, \"\n              f\"{sum(1 for n in nodes if n.kind=='llm')} LLM nodes\")\n\n    if a.phase in (\"all\", \"attribute\"):\n        best = json.loads((Path(__file__).parent / \".cats_incumbent.json\").read_text())\n        sp = Spec(**best)\n        print(f\"\\n[phase attribute] paired leave-one-edge-out on {sp.key()}\")\n        at = attribute_edges(sp, train, draws, cache)\n        print(f\"  base acc={at['base']['acc']:.3f} obj={at['base']['obj']:.3f}\")\n        for r in at[\"edges\"]:\n            if \"error\" in r:\n                print(f\"  edge {r['edge']}: illegal/inactive ({r['error'][:40]})\")\n            else:\n                print(f\"  edge {str(r['edge']):10s} d_obj={r['delta_obj']:+.4f} \"\n                      f\"d_acc={r['delta_acc']:+.3f} (ablated acc={r['acc']:.3f})\")\n        Path(Path(__file__).parent / \".cats_attrib.json\").write_text(\n            json.dumps({\"base\": {k: v for k, v in at[\"base\"].items() if k != \"detail\"},\n                        \"edges\": at[\"edges\"]}, indent=1))", "numLines": 85, "startLine": 330, "totalLines": 420}}

The file /app/methods/main/optimize.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/methods/main/optimize.py", "oldString": "    ap.add_argument(\"--valtags\", default=\"3c/transcribe-nt\")\n    ap.add_argument(\"--freeze\", default=\"3c/transcribe-nt\")\n    a = ap.parse_args()", "newString": "    ap.add_argument(\"--valtags\", default=\"3c/transcribe-nt\")\n    ap.add_argument(\"--freeze\", default=\"3c/transcribe-nt\")\n    ap.add_argument(\"--specs\", default=\"\", help=\"comma-separated tags; empty = all\")\n    a = ap.parse_args()", "originalFile": null, "structuredPatch": [{"oldStart": 340, "oldLines": 6, "newStart": 340, "newLines": 7, "lines": ["     ap.add_argument(\"--out\", default=str(Path(__file__).parent / \"graph.json\"))", "     ap.add_argument(\"--valtags\", default=\"3c/transcribe-nt\")", "     ap.add_argument(\"--freeze\", default=\"3c/transcribe-nt\")", "+    ap.add_argument(\"--specs\", default=\"\", help=\"comma-separated tags; empty = all\")", "     a = ap.parse_args()", " ", "     cache = TraceCache()"]}], "userModified": false, "replaceAll": false}

The file /app/methods/main/optimize.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/methods/main/optimize.py", "oldString": "    if a.phase in (\"all\", \"struct\"):\n        pool = a.pool.split(\",\")\n        print(f\"\\n[phase struct] cost-aware frontier over Specs (pool={pool}, draws={draws}):\")\n        res = phase_struct(candidate_specs(pool), train, draws, cache)", "newString": "    if a.phase in (\"all\", \"struct\"):\n        pool = a.pool.split(\",\")\n        cs = candidate_specs(pool)\n        if a.specs:\n            want = a.specs.split(\",\")\n            cs = [sp for sp in cs if sp.tag in want]\n        print(f\"\\n[phase struct] cost-aware frontier over Specs (pool={pool}, draws={draws}, \"\n              f\"{len(cs)} specs):\")\n        res = phase_struct(cs, train, draws, cache)", "originalFile": null, "structuredPatch": [{"oldStart": 366, "oldLines": 8, "newStart": 366, "newLines": 13, "lines": [" ", "     if a.phase in (\"all\", \"struct\"):", "         pool = a.pool.split(\",\")", "-        print(f\"\\n[phase struct] cost-aware frontier over Specs (pool={pool}, draws={draws}):\")", "-        res = phase_struct(candidate_specs(pool), train, draws, cache)", "+        cs = candidate_specs(pool)", "+        if a.specs:", "+            want = a.specs.split(\",\")", "+            cs = [sp for sp in cs if sp.tag in want]", "+        print(f\"\\n[phase struct] cost-aware frontier over Specs (pool={pool}, draws={draws}, \"", "+              f\"{len(cs)} specs):\")", "+        res = phase_struct(cs, train, draws, cache)", "         print(\"\\nranked by accuracy x efficiency:\")", "         for r in res:", "             print(f\"  {r['obj']:.3f}  acc={r['acc']:.3f} calls={r['avg_calls']:.2f}  {r['tag']}\")"]}], "userModified": false, "replaceAll": false}

The file /app/methods/main/optimize.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/methods/main/optimize.py", "oldString": "        assert sum(1 for n in nodes if n.kind == \"llm\") >= 3, \"LLM-node gate\"", "newString": "        # gate 1 counts *active* LLM nodes only -- an unreachable node is not a call\n        from space import n_llm as _n_llm\n        assert _n_llm(nodes, edges) >= 3, \"LLM-node gate (active llm nodes)\"", "originalFile": null, "structuredPatch": [{"oldStart": 395, "oldLines": 7, "newStart": 395, "newLines": 9, "lines": ["         nodes, edges, _ = build(sp)", "         sw = S.Swarm(nodes=nodes, edges=edges)", "         sw.validate()", "-        assert sum(1 for n in nodes if n.kind == \"llm\") >= 3, \"LLM-node gate\"", "+        # gate 1 counts *active* LLM nodes only -- an unreachable node is not a call", "+        from space import n_llm as _n_llm", "+        assert _n_llm(nodes, edges) >= 3, \"LLM-node gate (active llm nodes)\"", "         assert all(len(n.template) <= 2000 for n in nodes), \"per-node template cap\"", "         assert sum(len(n.template) for n in nodes) <= 12000, \"total template cap\"", "         sw.save(a.out)"]}], "userModified": false, "replaceAll": false}

180 (template,problem,draw) cells, 180 need LLM calls
=== V ===
135 (template,problem,draw) cells, 135 need LLM calls
=== cache ===
800
vllm:num_requests_running{engine="0",model_name="node-1b"} 66.0
vllm:num_requests_waiting{engine="0",model_name="node-1b"} 0.0

[stdout]
180 (template,problem,draw) cells, 180 need LLM calls
=== V ===
135 (template,problem,draw) cells, 135 need LLM calls
=== cache ===
800
vllm:num_requests_running{engine="0",model_name="node-1b"} 66.0
vllm:num_requests_waiting{engine="0",model_name="node-1b"} 0.0
[metadata] {"noOutputExpected": false}

File created successfully at: /app/methods/main/METHOD.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/app/methods/main/METHOD.md", "content": "# CATS — Cache-Amortized Trace Search\n\nAn optimizer for language-agents-as-a-graph that replaces the inherited\nGPTSwarm-style REINFORCE over Bernoulli edge probabilities.\n\n## Why REINFORCE is the wrong estimator on this problem\n\nThe inherited baseline samples an edge mask `m ~ Bernoulli(θ)`, runs the graph,\nobserves one scalar reward `r`, and pushes `(r - b) ∇log p(m)` into every edge\nsimultaneously. On this task that wastes essentially all of the information each\nrollout produces:\n\n1. **Credit is smeared uniformly.** One graph-level scalar is divided over ~16\n   edges, so each edge's per-rollout signal is O(1/16) of an already-noisy\n   binary outcome. With a 30-problem visible train set and temperature 0.7, the\n   gradient noise dominates the signal for any realistic number of rollouts.\n2. **Every rollout is discarded after one update.** A rollout is a set of\n   expensive LLM completions. REINFORCE consumes it as a single scalar and\n   throws the text away — even though the *same* completions are valid samples\n   for a combinatorial number of other graphs.\n3. **Comparisons are unpaired.** Graph A and graph B are evaluated on\n   independent samples, so the variance of `acc(A) − acc(B)` is the sum of two\n   sampling variances rather than the (much smaller) variance of a paired\n   difference.\n4. **It never touches nodes.** Prompt templates and node kinds are left at\n   library defaults, and on this substrate they are where most of the accuracy\n   actually lives (see \"What the diagnostics found\").\n\n## What CATS does instead\n\n### 1. Trace caching with common random numbers\nEvery LLM completion is stored keyed by `sha1(draw ‖ prompt)`. A \"draw\" is a\nreproducible sample slot, so re-running the *same* node prompt at the same slot\nreturns the *same* completion. Two consequences:\n\n- **Off-policy reuse.** A completion produced while evaluating graph A is reused\n  verbatim by every other graph whose node presents that identical prompt.\n  Evaluating the 14-graph shortlist costs far less than 14 independent\n  evaluations, because the solver layer is shared.\n- **Paired comparison (common random numbers).** Graphs are compared on the\n  identical set of completions, so `acc(A) − acc(B)` is a paired difference. The\n  per-problem noise cancels instead of adding.\n\nIdentical prompts appearing more than once inside one graph get distinct draw\nslots (`draw*MAXDUP + rank`), so duplicate nodes still draw *independent*\nsamples, exactly as the real engine would.\n\n### 2. Node optimization against the actual failure mode\nBefore searching structure, CATS calibrates candidate prompt templates on a\ndiagnostic that separates two things graph-level accuracy conflates:\n\n- **closure** — did the completion emit `</think>` before hitting the token cap?\n- **conditional accuracy** — given that it closed, was it right?\n\nThis mattered enormously here (below). The reward-only view used by REINFORCE\ncannot see this decomposition at all.\n\n### 3. Free heterogeneous nodes as the aggregation substrate\n`code_exec` and `symbolic_verify` are non-LLM: they do not count against the\nefficiency multiplier. CATS treats them as the default aggregation layer and\nspends LLM calls only on generating *diverse candidate answers*.\n\nTwo engine details make the free layer far more powerful than it first appears,\nand CATS exploits both:\n\n- `code_exec` reads predecessors' **raw** text, so it recovers and runs a program\n  even out of a `<think>` block that was truncated before any prose answer\n  existed — it rescues exactly the completions that would otherwise score 0.\n- A `code_exec` predecessor casts **two** votes into `symbolic_verify` (the\n  `computed integer candidate = N` match, plus `parse_answer` on its stdout),\n  and `symbolic_verify` breaks ties by *earliest-listed predecessor*. Therefore\n  **the edge order into the verifier encodes a decision rule, for free**:\n  ordering `[reasoner_a, reasoner_b, code_exec]` means two agreeing reasoners\n  (2 votes, earlier) override the program (2 votes, later), while a single\n  reasoner does not. No LLM call implements that rule; the edge list does.\n\nBecause the aggregator is deterministic given cached traces, its **entire design\nspace is enumerable offline at zero LLM cost** (`tally.py`) — the part of the\ngraph REINFORCE would spend its whole sample budget learning is solved in closed\nform.\n\n### 4. Counterfactual per-edge credit under a cost-aware objective\nThe objective is the grader's shape, not accuracy:\n\n```\nobjective(acc, avg_calls) = acc * efficiency(avg_calls)\nefficiency = clip((REF_CALLS_PER_PROBLEM / avg_calls) ** 0.75, 0.5, 1.6)\n```\n\nCredit assignment is *leave-one-edge-out on identical draws*: for each active\nedge, the graph is re-scored with that edge removed against the same cached\ncompletions, and the edge's credit is the paired `Δobjective`. That is a direct\ncounterfactual measurement of one edge's marginal contribution, not a scalar\nsmeared across all of them — and because of the cache it is usually free.\n\n## What the diagnostics found (visible data only)\n\n- **Truncation, not reasoning, is the dominant loss channel.** The engine caps\n  completions at `min(16000, 24576 − len(prompt)//3 − 800)` and the node model\n  emits long `<think>` blocks. When the cap hits there is no `</think>`,\n  `_strip_think` returns raw text and `parse_answer` returns garbage. Measured\n  accuracy *conditional on closure* was 0.78–0.90 against 0.60–0.63\n  unconditional. Prompts were therefore selected on closure × conditional\n  accuracy, not on accuracy alone.\n- **The best single call is a real bar.** Best single-template accuracy on train\n  (n=90, 3 draws) was ≈0.63 — gate 2 is not a formality.\n- **Reasoner and program-writer are strongly complementary.** On identical cells,\n  best solo ≈0.71–0.74; a reasoner + program-writer tallied through the free\n  nodes reached ≈0.86 (+0.15). A third solver added only ≈+0.03 — not enough to\n  justify a 4th call under the efficiency multiplier.\n- **A program-writing solver must not vote directly.** `pot`-style templates\n  parse to a bare integer only ~14% of the time and are ~0.05 accurate when they\n  do; letting them cast their own `parse_answer` vote (and listing them first)\n  hands them tie-breaks they should never win.\n\n## Files\n\n| file | role |\n| --- | --- |\n| `graph.json` | the frozen graph — the scored artifact |\n| `optimize.py` | CATS itself; phases `nodes`, `struct`, `val`, `attribute`, `freeze` |\n| `harness.py` | trace cache, faithful prompt reconstruction, cached graph replay |\n| `bank.py` | the node-template search space (solvers / refiners / deciders) |\n| `space.py` | structured joint node+edge generator (`Spec` → nodes, edges) |\n| `tally.py` | free offline enumeration of the aggregator design space |\n| `calib_solvers.py` | per-template closure / conditional-accuracy calibration |\n| `optimize_baseline_gptswarm.py` | the inherited REINFORCE, kept verbatim for comparison |\n\nReproduce with:\n\n```\npython3 optimize.py --phase nodes    --draws 3        # template calibration\npython3 tally.py 0,1,2 train                          # free aggregator lattice\npython3 optimize.py --phase struct   --draws 2        # cost-aware frontier\npython3 optimize.py --phase attribute --draws 2       # per-edge counterfactuals\npython3 optimize.py --phase freeze   --freeze <tag>   # write graph.json\n```\n", "structuredPatch": [], "originalFile": null, "userModified": false}

180 (template,problem,draw) cells, 180 need LLM calls
=== V ===
135 (template,problem,draw) cells, 135 need LLM calls
prefetch done in 1761s  (new=135 hit=0)

tmpl        n    acc  closed  parsed  codeOK  codeAcc  anyAcc   toks
pot2       45  0.000   0.756   0.178   0.733    0.578   0.578  10773
decisive   45  0.444   0.622   0.778   0.000    0.000   0.444  11634
alt        45  0.467   0.511   0.644   0.000    0.000   0.467  12142

conditional on closed </think>:
  pot2     n= 34 acc=0.000
  decisive n= 28 acc=0.714
  alt      n= 23 acc=0.870

code_exec state histogram:
  pot2: {'[code_exec] program stdout:': 33, '[code_exec] no python code block found in predecessor output': 11, '[code_exec] script errored (rc=1):': 1}
  decisive: {'[code_exec] no python code block found in predecessor output': 45}
  alt: {'[code_exec] no python code block found in predecessor output': 45}
=== time/cache ===
15619
833

[stdout]
180 (template,problem,draw) cells, 180 need LLM calls
=== V ===
135 (template,problem,draw) cells, 135 need LLM calls
prefetch done in 1761s  (new=135 hit=0)

tmpl        n    acc  closed  parsed  codeOK  codeAcc  anyAcc   toks
pot2       45  0.000   0.756   0.178   0.733    0.578   0.578  10773
decisive   45  0.444   0.622   0.778   0.000    0.000   0.444  11634
alt        45  0.467   0.511   0.644   0.000    0.000   0.467  12142

conditional on closed </think>:
  pot2     n= 34 acc=0.000
  decisive n= 28 acc=0.714
  alt      n= 23 acc=0.870

code_exec state histogram:
  pot2: {'[code_exec] program stdout:': 33, '[code_exec] no python code block found in predecessor output': 11, '[code_exec] script errored (rc=1):': 1}
  decisive: {'[code_exec] no python code block found in predecessor output': 45}
  alt: {'[code_exec] no python code block found in predecessor output': 45}
=== time/cache ===
15619
833
[metadata] {"noOutputExpected": false}

0.600   45 0.956  3+1    alt+decisive+pot2
0.600   45 0.933  3+1    alt+decisive+pot2  [pot speaks only via code]
0.600   45 0.956  3+1    alt+pot2+decisive
0.600   45 0.933  3+1    alt+pot2+decisive  [pot speaks only via code]
0.600   45 0.956  3+1    decisive+alt+pot2
0.600   45 0.933  3+1    decisive+alt+pot2  [pot speaks only via code]
0.600   45 0.956  3+1    decisive+pot2+alt
0.600   45 0.933  3+1    decisive+pot2+alt  [pot speaks only via code]
0.578   45 0.889  1+1    pot2
0.578   45 0.733  1+1    pot2  [pot speaks only via code]
0.578   45 0.911  2+1    alt+pot2
0.578   45 0.778  2+1    alt+pot2  [pot speaks only via code]
0.578   45 0.933  2+1    decisive+pot2
0.578   45 0.889  2+1    decisive+pot2  [pot speaks only via code]
0.578   45 0.911  2+1    pot2+alt
0.578   45 0.778  2+1    pot2+alt  [pot speaks only via code]
0.578   45 0.933  2+1    pot2+decisive
0.578   45 0.889  2+1    pot2+decisive  [pot speaks only via code]
0.467   45 0.644  1+1    alt
0.444   45 0.778  1+1    decisive

--- best design at each solver count (this is the cost/accuracy frontier) ---
  1 solver(s) -> tally acc 0.578 (n=45) order=pot2
  2 solver(s) -> tally acc 0.578 (n=45) order=alt+pot2
  3 solver(s) -> tally acc 0.600 (n=45) order=alt+decisive+pot2

[stdout]
0.600   45 0.956  3+1    alt+decisive+pot2
0.600   45 0.933  3+1    alt+decisive+pot2  [pot speaks only via code]
0.600   45 0.956  3+1    alt+pot2+decisive
0.600   45 0.933  3+1    alt+pot2+decisive  [pot speaks only via code]
0.600   45 0.956  3+1    decisive+alt+pot2
0.600   45 0.933  3+1    decisive+alt+pot2  [pot speaks only via code]
0.600   45 0.956  3+1    decisive+pot2+alt
0.600   45 0.933  3+1    decisive+pot2+alt  [pot speaks only via code]
0.578   45 0.889  1+1    pot2
0.578   45 0.733  1+1    pot2  [pot speaks only via code]
0.578   45 0.911  2+1    alt+pot2
0.578   45 0.778  2+1    alt+pot2  [pot speaks only via code]
0.578   45 0.933  2+1    decisive+pot2
0.578   45 0.889  2+1    decisive+pot2  [pot speaks only via code]
0.578   45 0.911  2+1    pot2+alt
0.578   45 0.778  2+1    pot2+alt  [pot speaks only via code]
0.578   45 0.933  2+1    pot2+decisive
0.578   45 0.889  2+1    pot2+decisive  [pot speaks only via code]
0.467   45 0.644  1+1    alt
0.444   45 0.778  1+1    decisive

--- best design at each solver count (this is the cost/accuracy frontier) ---
  1 solver(s) -> tally acc 0.578 (n=45) order=pot2
  2 solver(s) -> tally acc 0.578 (n=45) order=alt+pot2
  3 solver(s) -> tally acc 0.600 (n=45) order=alt+decisive+pot2
[metadata] {"noOutputExpected": false}

File created successfully at: /app/methods/main/multi.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/app/methods/main/multi.py", "content": "\"\"\"Free offline simulation of multi-node graphs built from cached draws.\n\nA node is a (template, draw-slot) pair: two nodes carrying the SAME template but\ndifferent slots are exactly what the real engine produces for two duplicate\nsolver nodes (independent samples at temperature 0.7). So the whole\n\"how many solvers, of which kinds, in which edge order\" question can be answered\noffline against the cache, at zero LLM cost.\n\nReplays run_symbolic_verify's semantics exactly, including the code_exec double\nvote and the earliest-predecessor tie-break.\n\nUsage:  python3 multi.py [split]\n\"\"\"\nfrom __future__ import annotations\n\nimport itertools\nimport re\nimport sys\nfrom collections import Counter\n\nsys.path.insert(0, \"/app/methods/main\")\n\nimport swarm as S\nfrom bank import SOLVERS\nfrom harness import MAXDUP, TraceCache\n\nSPLIT = sys.argv[1] if len(sys.argv) > 1 else \"val\"\nDRAWS = [0, 1, 2]\nPOT_LIKE = {\"pot\", \"pot2\", \"pot3\", \"hybrid\"}\n\nprobs = S.load_problems(f\"/app/data/{SPLIT}.jsonl\")\ncache = TraceCache()\n\ncell: dict[tuple[str, str, int], dict] = {}\nfor t, tmpl in SOLVERS.items():\n    for p in probs:\n        for d in DRAWS:\n            r = cache.get(tmpl.replace(\"{problem}\", p[\"problem\"]), d * MAXDUP)\n            if not r:\n                continue\n            co = S.run_code_exec([r[\"text\"]])\n            m = re.search(r\"computed integer candidate = (-?\\d+)\", co)\n            cell[(t, str(p[\"id\"]), d)] = {\n                \"pred\": S.parse_answer(r[\"text\"]),\n                \"code\": int(m.group(1)) if m else None,\n                \"gold\": int(p[\"answer\"]),\n            }\n\nfull = sorted({t for t in SOLVERS\n               if all((t, str(p[\"id\"]), d) in cell for p in probs for d in DRAWS)})\nprint(f\"split={SPLIT} n_problems={len(probs)} templates with all {len(DRAWS)} draws: {full}\")\n\n\ndef sim(nodes: list[tuple[str, int]], drop_pot_pred: bool = False):\n    \"\"\"nodes = ordered [(template, draw_slot)]; returns (acc, coverage, agree_rate,\n    acc_when_agree, acc_when_split).\"\"\"\n    good = cov = agree = agree_ok = split = split_ok = 0\n    for p in probs:\n        pid = str(p[\"id\"])\n        if not all((t, pid, d) in cell for t, d in nodes):\n            return None\n        preds, codes = [], []\n        for t, d in nodes:\n            c = cell[(t, pid, d)]\n            if c[\"pred\"] is not None and not (drop_pot_pred and t in POT_LIKE):\n                preds.append(c[\"pred\"])\n            if c[\"code\"] is not None:\n                codes.append(c[\"code\"])\n        cands = [c for c in preds + [x for c in codes for x in (c, c)] if 0 <= c <= 999]\n        if not cands:\n            continue\n        cov += 1\n        cnt = Counter(cands)\n        top = max(cnt, key=lambda x: (cnt[x], -cands.index(x)))\n        ok = int(top == int(p[\"answer\"]))\n        good += ok\n        if len(set(cands)) == 1:\n            agree += 1; agree_ok += ok\n        else:\n            split += 1; split_ok += ok\n    n = len(probs)\n    return dict(acc=good / n, cov=cov / n,\n                agree=agree / n, agree_acc=agree_ok / max(1, agree),\n                split=split / n, split_acc=split_ok / max(1, split))\n\n\ndef show(label, nodes, drop=False):\n    r = sim(nodes, drop)\n    if r is None:\n        return\n    calls = len(nodes) + 1  # + the mandatory decision node\n    print(f\"{r['acc']:.3f}  cov={r['cov']:.2f}  calls={calls}  \"\n          f\"agree={r['agree']:.2f}@{r['agree_acc']:.2f} split={r['split']:.2f}@{r['split_acc']:.2f}\"\n          f\"   {label}\")\n\n\nprint(\"\\n--- single node (the gate-2 bar) ---\")\nfor t in full:\n    show(t, [(t, 0)])\n\nprint(\"\\n--- self-consistency: K independent draws of ONE template ---\")\nfor t in full:\n    for k in (2, 3):\n        show(f\"{t} x{k}\", [(t, d) for d in DRAWS[:k]])\n\nprint(\"\\n--- mixed pairs (reasoner + program-writer), both edge orders ---\")\npots = [t for t in full if t in POT_LIKE]\nreas = [t for t in full if t not in POT_LIKE]\nfor a, b in itertools.product(reas, pots):\n    show(f\"{a}+{b}\", [(a, 0), (b, 0)])\n    show(f\"{a}+{b}  [pot mute]\", [(a, 0), (b, 0)], drop=True)\n\nprint(\"\\n--- 2 program-writers (4 code votes) ---\")\nfor k, combo in enumerate(itertools.combinations_with_replacement(pots, 2)):\n    ns = [(combo[0], 0), (combo[1], 1 if combo[0] == combo[1] else 0)]\n    show(\"+\".join(f\"{t}@{d}\" for t, d in ns), ns)\n\nprint(\"\\n--- 3 nodes: program-writer(s) + reasoner(s) ---\")\nfor pot in pots:\n    for r1, r2 in itertools.combinations_with_replacement(reas, 2):\n        ns = [(r1, 0), (r2, 1 if r1 == r2 else 0), (pot, 0)]\n        show(\"+\".join(f\"{t}@{d}\" for t, d in ns), ns)\n    ns = [(pot, 0), (pot, 1)] + [(reas[0], 0)]\n    show(\"+\".join(f\"{t}@{d}\" for t, d in ns), ns)\n", "structuredPatch": [], "originalFile": null, "userModified": false}

split=val n_problems=15 templates with all 3 draws: ['alt', 'decisive', 'pot2']

--- single node (the gate-2 bar) ---
0.467  cov=0.67  calls=2  agree=0.67@0.70 split=0.00@0.00   alt
0.467  cov=0.80  calls=2  agree=0.80@0.58 split=0.00@0.00   decisive
0.533  cov=0.87  calls=2  agree=0.87@0.62 split=0.00@0.00   pot2

--- self-consistency: K independent draws of ONE template ---
0.467  cov=0.73  calls=3  agree=0.53@0.75 split=0.20@0.33   alt x2
0.467  cov=0.80  calls=4  agree=0.60@0.67 split=0.20@0.33   alt x3
0.467  cov=0.87  calls=3  agree=0.53@0.62 split=0.33@0.40   decisive x2
0.533  cov=0.93  calls=4  agree=0.53@0.75 split=0.40@0.33   decisive x3
0.533  cov=0.93  calls=3  agree=0.67@0.80 split=0.27@0.00   pot2 x2
0.533  cov=1.00  calls=4  agree=0.67@0.80 split=0.33@0.00   pot2 x3

--- mixed pairs (reasoner + program-writer), both edge orders ---
0.533  cov=0.87  calls=3  agree=0.67@0.80 split=0.20@0.00   alt+pot2
0.533  cov=0.80  calls=3  agree=0.67@0.80 split=0.13@0.00   alt+pot2  [pot mute]
0.533  cov=0.93  calls=3  agree=0.80@0.58 split=0.13@0.50   decisive+pot2
0.533  cov=0.87  calls=3  agree=0.73@0.64 split=0.13@0.50   decisive+pot2  [pot mute]

--- 2 program-writers (4 code votes) ---
0.533  cov=0.93  calls=3  agree=0.67@0.80 split=0.27@0.00   pot2@0+pot2@1

--- 3 nodes: program-writer(s) + reasoner(s) ---
0.533  cov=0.87  calls=4  agree=0.53@0.88 split=0.33@0.20   alt@0+alt@1+pot2@0
0.533  cov=0.93  calls=4  agree=0.60@0.78 split=0.33@0.20   alt@0+decisive@0+pot2@0
0.533  cov=0.93  calls=4  agree=0.47@0.71 split=0.47@0.43   decisive@0+decisive@1+pot2@0
0.533  cov=0.93  calls=4  agree=0.60@0.89 split=0.33@0.00   pot2@0+pot2@1+alt@0

[stdout]
split=val n_problems=15 templates with all 3 draws: ['alt', 'decisive', 'pot2']

--- single node (the gate-2 bar) ---
0.467  cov=0.67  calls=2  agree=0.67@0.70 split=0.00@0.00   alt
0.467  cov=0.80  calls=2  agree=0.80@0.58 split=0.00@0.00   decisive
0.533  cov=0.87  calls=2  agree=0.87@0.62 split=0.00@0.00   pot2

--- self-consistency: K independent draws of ONE template ---
0.467  cov=0.73  calls=3  agree=0.53@0.75 split=0.20@0.33   alt x2
0.467  cov=0.80  calls=4  agree=0.60@0.67 split=0.20@0.33   alt x3
0.467  cov=0.87  calls=3  agree=0.53@0.62 split=0.33@0.40   decisive x2
0.533  cov=0.93  calls=4  agree=0.53@0.75 split=0.40@0.33   decisive x3
0.533  cov=0.93  calls=3  agree=0.67@0.80 split=0.27@0.00   pot2 x2
0.533  cov=1.00  calls=4  agree=0.67@0.80 split=0.33@0.00   pot2 x3

--- mixed pairs (reasoner + program-writer), both edge orders ---
0.533  cov=0.87  calls=3  agree=0.67@0.80 split=0.20@0.00   alt+pot2
0.533  cov=0.80  calls=3  agree=0.67@0.80 split=0.13@0.00   alt+pot2  [pot mute]
0.533  cov=0.93  calls=3  agree=0.80@0.58 split=0.13@0.50   decisive+pot2
0.533  cov=0.87  calls=3  agree=0.73@0.64 split=0.13@0.50   decisive+pot2  [pot mute]

--- 2 program-writers (4 code votes) ---
0.533  cov=0.93  calls=3  agree=0.67@0.80 split=0.27@0.00   pot2@0+pot2@1

--- 3 nodes: program-writer(s) + reasoner(s) ---
0.533  cov=0.87  calls=4  agree=0.53@0.88 split=0.33@0.20   alt@0+alt@1+pot2@0
0.533  cov=0.93  calls=4  agree=0.60@0.78 split=0.33@0.20   alt@0+decisive@0+pot2@0
0.533  cov=0.93  calls=4  agree=0.47@0.71 split=0.47@0.43   decisive@0+decisive@1+pot2@0
0.533  cov=0.93  calls=4  agree=0.60@0.89 split=0.33@0.00   pot2@0+pot2@1+alt@0
[metadata] {"noOutputExpected": false}

180 (template,problem,draw) cells, 180 need LLM calls
=== cache/time ===
858
15509

[stdout]
180 (template,problem,draw) cells, 180 need LLM calls
=== cache/time ===
858
15509
[metadata] {"noOutputExpected": false}

[phase struct] cost-aware frontier over Specs (pool=['pot2', 'decisive', 'alt'], draws=[0, 1], 5 specs):
  3c/transcribe-nt       acc=0.767 calls=3.00 obj=0.951 nllm=3 edges=7

[stdout]
[phase struct] cost-aware frontier over Specs (pool=['pot2', 'decisive', 'alt'], draws=[0, 1], 5 specs):
  3c/transcribe-nt       acc=0.767 calls=3.00 obj=0.951 nllm=3 edges=7
[metadata] {"noOutputExpected": false}

--- DECIDERS[resolve] ---
You give the final answer to this AIME problem (an integer 0-999). Below are independent attempts, possibly with [code_exec] (the REAL output of an executed program) and [symbolic_verify] (an automatic tally of the candidate values).

CASE A -- if [symbolic_verify] reports the valid candidates AGREE on a value, that is the answer. Do not redo any work: reply with exactly one line, <answer>N</answer>.

CASE B -- otherwise the attempts conflicted or produced nothing usable, which usually means they were all wrong or ran out of room. Do not referee them and do not trust any candidate above. Solve the problem yourself from scratch: pick the most computational route (enumerate small cases, compute directly), commit to it, and stay under about 1200 words. If you run long, stop and commit to your best value.

Either way your reply must end with the final line <answer>N</answer>.

Problem: {problem}{context}

len 913
--- DECIDERS[transcribe] ---
Read the tally below and report its winning integer.
If [symbolic_verify] states the candidates AGREE on a value, that value is the answer. If it gives a majority pick, that is the answer. Otherwise use the most reliable candidate ([code_exec] computed integers first).
Do not solve the problem yourself. Reply with nothing but <answer>N</answer>.

Problem: {problem}{context}

[stdout]
--- DECIDERS[resolve] ---
You give the final answer to this AIME problem (an integer 0-999). Below are independent attempts, possibly with [code_exec] (the REAL output of an executed program) and [symbolic_verify] (an automatic tally of the candidate values).

CASE A -- if [symbolic_verify] reports the valid candidates AGREE on a value, that is the answer. Do not redo any work: reply with exactly one line, <answer>N</answer>.

CASE B -- otherwise the attempts conflicted or produced nothing usable, which usually means they were all wrong or ran out of room. Do not referee them and do not trust any candidate above. Solve the problem yourself from scratch: pick the most computational route (enumerate small cases, compute directly), commit to it, and stay under about 1200 words. If you run long, stop and commit to your best value.

Either way your reply must end with the final line <answer>N</answer>.

Problem: {problem}{context}

len 913
--- DECIDERS[transcribe] ---
Read the tally below and report its winning integer.
If [symbolic_verify] states the candidates AGREE on a value, that value is the answer. If it gives a majority pick, that is the answer. Otherwise use the most reliable candidate ([code_exec] computed integers first).
Do not solve the problem yourself. Reply with nothing but <answer>N</answer>.

Problem: {problem}{context}
[metadata] {"noOutputExpected": false}