claude-opus-5

57:315-    def run(self, problem: str, meter: BudgetMeter) -> int | None:
58-316-        """Execute by topological layers; nodes in the same layer run concurrently
59-317-        (wall-clock only — cost accounting is identical to serial execution)."""
60-318-        self.validate()
61-319-        active = self._active_nodes()
62-320-        preds = {i: [s for s, d in self.edges if d == i] for i in active}
63-321-        outputs: dict[int, str] = {}
64-322-
65-323-        remaining = set(active)
66-324-        while remaining:
67-325-            layer = [i for i in remaining
68-326-                     if all((s < 0 or s not in remaining) for s in preds[i])]
69-327-            layer.sort()
70-328-
71-329-            # LLM nodes call the model and charge the meter; non-LLM nodes
72-330-            # (code_exec / symbolic_verify) run locally and are FREE.
73-331-            llm_layer = [i for i in layer if self.nodes[i].kind == "llm"]
74-332-            other_layer = [i for i in layer if self.nodes[i].kind != "llm"]
75-333-
76-334-            prompts = {
77-335-                # FIX (2026-07-27): plain .format(problem=..., context=...) treats ANY other
78-336-                # literal "{...}" in a node's template as a format field -> KeyError on the
79-337-                # extremely natural "\boxed{n}" (models routinely emit \boxed{...} unprompted).
80-338-                # Substitute only the two named placeholders literally instead.
81-339-                # CONTEXT-OVERFLOW FIX (2026-07-28): downstream LLM context was built from the
82-340-                # FULL raw upstream output including any <think>...</think> chain. Reasoning
83-341-                # models emit huge <think> blocks; a decision/aggregation node depending on an
84-342-                # upstream LLM node could inherit thousands of tokens of reasoning before its
85-343-                # own template text, silently overflowing the server's context window. Strip
86-344-                # <think> from context the same way parse_answer() already strips it from the
87-345-                # FINAL output. Non-LLM nodes (code_exec/symbolic_verify) still see the raw text.
88-346-                i: self.nodes[i].template.replace("{problem}", problem).replace(
89-347-                    "{context}",
90-348-                    "".join(
91-349-                        f"\n\n[{self.nodes[s].name} (kind={self.nodes[s].kind}) said]:\n{_strip_think(outputs[s])[:CONTEXT_SNIPPET_CHARS]}"
92-350-                        for s in preds[i] if s >= 0 and s in outputs
93-351-                    ),
94-352-                )
95-353-                for i in llm_layer
96-354-            }
97-355-            if len(llm_layer) == 1:

[stdout]
57:315-    def run(self, problem: str, meter: BudgetMeter) -> int | None:
58-316-        """Execute by topological layers; nodes in the same layer run concurrently
59-317-        (wall-clock only — cost accounting is identical to serial execution)."""
60-318-        self.validate()
61-319-        active = self._active_nodes()
62-320-        preds = {i: [s for s, d in self.edges if d == i] for i in active}
63-321-        outputs: dict[int, str] = {}
64-322-
65-323-        remaining = set(active)
66-324-        while remaining:
67-325-            layer = [i for i in remaining
68-326-                     if all((s < 0 or s not in remaining) for s in preds[i])]
69-327-            layer.sort()
70-328-
71-329-            # LLM nodes call the model and charge the meter; non-LLM nodes
72-330-            # (code_exec / symbolic_verify) run locally and are FREE.
73-331-            llm_layer = [i for i in layer if self.nodes[i].kind == "llm"]
74-332-            other_layer = [i for i in layer if self.nodes[i].kind != "llm"]
75-333-
76-334-            prompts = {
77-335-                # FIX (2026-07-27): plain .format(problem=..., context=...) treats ANY other
78-336-                # literal "{...}" in a node's template as a format field -> KeyError on the
79-337-                # extremely natural "\boxed{n}" (models routinely emit \boxed{...} unprompted).
80-338-                # Substitute only the two named placeholders literally instead.
81-339-                # CONTEXT-OVERFLOW FIX (2026-07-28): downstream LLM context was built from the
82-340-                # FULL raw upstream output including any <think>...</think> chain. Reasoning
83-341-                # models emit huge <think> blocks; a decision/aggregation node depending on an
84-342-                # upstream LLM node could inherit thousands of tokens of reasoning before its
85-343-                # own template text, silently overflowing the server's context window. Strip
86-344-                # <think> from context the same way parse_answer() already strips it from the
87-345-                # FINAL output. Non-LLM nodes (code_exec/symbolic_verify) still see the raw text.
88-346-                i: self.nodes[i].template.replace("{problem}", problem).replace(
89-347-                    "{context}",
90-348-                    "".join(
91-349-                        f"\n\n[{self.nodes[s].name} (kind={self.nodes[s].kind}) said]:\n{_strip_think(outputs[s])[:CONTEXT_SNIPPET_CHARS]}"
92-350-                        for s in preds[i] if s >= 0 and s in outputs
93-351-                    ),
94-352-                )
95-353-                for i in llm_layer
96-354-            }
97-355-            if len(llm_layer) == 1:
[metadata] {"noOutputExpected": false}

=== s5 TRAIN n=90 ===
=== v2 VAL n=45 ===
=== time ===
12364

[stdout]
=== s5 TRAIN n=90 ===
=== v2 VAL n=45 ===
=== time ===
12364
[metadata] {"noOutputExpected": false}

{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 23850.5
}

[stdout]
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 23850.5
}
[metadata] {"noOutputExpected": false}

=== s5 TRAIN n=90 ===

[phase struct] cost-aware frontier over Specs (pool=['pot2', 'decisive', 'alt'], draws=[0, 1, 2], 3 specs):
  3c/resolve-in-decision acc=0.800 calls=3.00 obj=0.993 nllm=3 edges=10
  3c/nt-reasoners-first  acc=0.778 calls=3.00 obj=0.965 nllm=3 edges=7
  3c/resolve-rf-alt      acc=0.822 calls=3.00 obj=1.020 nllm=3 edges=10

ranked by accuracy x efficiency:
  1.020  acc=0.822 calls=3.00  3c/resolve-rf-alt
  0.993  acc=0.800 calls=3.00  3c/resolve-in-decision
  0.965  acc=0.778 calls=3.00  3c/nt-reasoners-first
[cats] elapsed 1016s  cache new=102 hit=804
=== v2 VAL n=45 ===

[phase val] held-out confirmation (draws=[0, 1, 2]):
  3c/resolve-rf-alt        val acc=0.644 calls=3.00 obj=0.800
  3c/resolve-in-decision   val acc=0.600 calls=3.00 obj=0.744
  3c/nt-reasoners-first    val acc=0.578 calls=3.00 obj=0.717
[cats] elapsed 1036s  cache new=78 hit=403
=== time ===
11833
1622

[stdout]
=== s5 TRAIN n=90 ===

[phase struct] cost-aware frontier over Specs (pool=['pot2', 'decisive', 'alt'], draws=[0, 1, 2], 3 specs):
  3c/resolve-in-decision acc=0.800 calls=3.00 obj=0.993 nllm=3 edges=10
  3c/nt-reasoners-first  acc=0.778 calls=3.00 obj=0.965 nllm=3 edges=7
  3c/resolve-rf-alt      acc=0.822 calls=3.00 obj=1.020 nllm=3 edges=10

ranked by accuracy x efficiency:
  1.020  acc=0.822 calls=3.00  3c/resolve-rf-alt
  0.993  acc=0.800 calls=3.00  3c/resolve-in-decision
  0.965  acc=0.778 calls=3.00  3c/nt-reasoners-first
[cats] elapsed 1016s  cache new=102 hit=804
=== v2 VAL n=45 ===

[phase val] held-out confirmation (draws=[0, 1, 2]):
  3c/resolve-rf-alt        val acc=0.644 calls=3.00 obj=0.800
  3c/resolve-in-decision   val acc=0.600 calls=3.00 obj=0.744
  3c/nt-reasoners-first    val acc=0.578 calls=3.00 obj=0.717
[cats] elapsed 1036s  cache new=78 hit=403
=== time ===
11833
1622
[metadata] {"noOutputExpected": false}

[freeze] 3c/resolve-rf-alt -> /app/methods/main/graph.json: 5 nodes, 10 active edges, 3 LLM nodes
[cats] elapsed 0s  cache new=0 hit=0
  0 solver_pot2 llm 695
  1 solver_alt llm 440
  2 code_exec code_exec 0
  3 verify symbolic_verify 0
  4 decision llm 913
 edges [[-1, 0], [-1, 1], [0, 2], [1, 3], [2, 3], [0, 3], [0, 4], [1, 4], [2, 4], [3, 4]]
 nodes=5<=8  active_edges=10<=16
 active llm nodes = 3
 max tmpl 913 <=2000; total 2048 <=12000
 last node: decision llm

[stdout]
[freeze] 3c/resolve-rf-alt -> /app/methods/main/graph.json: 5 nodes, 10 active edges, 3 LLM nodes
[cats] elapsed 0s  cache new=0 hit=0
  0 solver_pot2 llm 695
  1 solver_alt llm 440
  2 code_exec code_exec 0
  3 verify symbolic_verify 0
  4 decision llm 913
 edges [[-1, 0], [-1, 1], [0, 2], [1, 3], [2, 3], [0, 3], [0, 4], [1, 4], [2, 4], [3, 4]]
 nodes=5<=8  active_edges=10<=16
 active llm nodes = 3
 max tmpl 913 <=2000; total 2048 <=12000
 last node: decision llm
[metadata] {"noOutputExpected": false}

{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 32891.7
}

[stdout]
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 32891.7
}
[metadata] {"noOutputExpected": false}

launched
-rw-r--r-- 1 root root 0 Aug  5 21:14 /tmp/sc1.log
-rw-r--r-- 1 root root 0 Aug  5 21:14 /tmp/sc2.log
-rw-r--r-- 1 root root 0 Aug  5 21:14 /tmp/sc3.log

[stdout]
launched
-rw-r--r-- 1 root root 0 Aug  5 21:14 /tmp/sc1.log
-rw-r--r-- 1 root root 0 Aug  5 21:14 /tmp/sc2.log
-rw-r--r-- 1 root root 0 Aug  5 21:14 /tmp/sc3.log
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/app/methods/main/METHOD.md", "oldString": "## Files", "newString": "## The frozen graph\n\nFive nodes, ten active edges, **three LLM calls per problem** (vs the cost\nreference of 4.0, so the efficiency multiplier is `(4/3)^0.75 ≈ 1.25`):\n\n```\n        problem ─┬─> solver_pot2 (llm)  ──> code_exec (free) ─┐\n                 └─> solver_alt  (llm) ─┐                     │\n                                        ├──> verify (free) ───┤\n                                        │    [alt, code_exec, │\n                                        │     pot2]           │\n                                        └─────────────────────┴─> decision (llm)\n```\n\n- `solver_pot2` writes a **program**; `code_exec` runs it for free and recovers a\n  candidate even when the solver's `<think>` block was truncated.\n- `solver_alt` reasons in natural language — a genuinely different failure mode,\n  which is what makes the pair complementary.\n- `verify` tallies candidates with the reasoner listed **first**, so it wins\n  tie-breaks over the program-writer's near-garbage self-parse.\n- `decision` is a **conditional re-solver**, not a transcriber: if the tally\n  reports unanimity it restates that integer and stops; otherwise it ignores every\n  candidate and solves the problem from scratch. That matters because\n  disagreement cells score ≈0 under any aggregation rule (below), so the\n  mandatory final call is worth far more as a fresh attempt than as a copyist.\n\n## Results (visible data)\n\nEvery design costs the same 3 LLM calls, so these are pure accuracy differences.\nTrain is 30 problems × 3 draws (n=90), val is 15 × 3 (n=45), all paired on\nidentical cached completions.\n\n| design | train | val |\n| --- | --- | --- |\n| **`3c/resolve-rf-alt`** (frozen) | **0.822** | **0.644** |\n| `3c/resolve-in-decision` | 0.800 | 0.600 |\n| `3c/nt-reasoners-first` (transcriber) | 0.778 | 0.578 |\n| best single LLM call (gate-2 bar) | 0.633 | 0.467 |\n\nThe ranking is identical on train and on held-out val, and the frozen graph beats\nthe best single-call prompt by **+0.19 train / +0.18 val**.\n\nAblations that isolate each effect (train, n=60, paired):\n\n| change | Δacc |\n| --- | --- |\n| conditional re-solve instead of transcription in the decision call | +0.033 |\n| reasoner listed before the program-writer into `verify` | +0.016 |\n| keeping the program-writer's own vote (vs muting it entirely) | +0.033 |\n| a 4th LLM call (third solver) | +0.03 — rejected, worth less than the 1.25× efficiency multiplier |\n\nNegative results worth recording:\n- **Prompt-level control of thinking length does not work here.** A template\n  explicitly forbidding any post-reasoning write-up (`terse`) cut tokens but\n  scored 0.611 against 0.633 for the ordinary reasoner — it lost more\n  conditional accuracy than it gained in closure.\n- **Aggregation cannot rescue disagreement.** When candidates split, the tally's\n  pick is right ~0–43% of the time; when they agree it is right 88–96%. Extra\n  voters mostly buy coverage, not arbitration — which is precisely why the\n  decision node re-solves instead of refereeing.\n- **A lone reasoner cannot outvote a program.** `code_exec` double-votes, so\n  `reasoner + program` tallies identically to `program` alone on the harder split.\n\n## Files", "originalFile": "# 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": [{"oldStart": 113, "oldLines": 6, "newStart": 113, "newLines": 69, "lines": ["   do; letting them cast their own `parse_answer` vote (and listing them first)", "   hands them tie-breaks they should never win.", " ", "+## The frozen graph", "+", "+Five nodes, ten active edges, **three LLM calls per problem** (vs the cost", "+reference of 4.0, so the efficiency multiplier is `(4/3)^0.75 ≈ 1.25`):", "+", "+```", "+        problem ─┬─> solver_pot2 (llm)  ──> code_exec (free) ─┐", "+                 └─> solver_alt  (llm) ─┐                     │", "+                                        ├──> verify (free) ───┤", "+                                        │    [alt, code_exec, │", "+                                        │     pot2]           │", "+                                        └─────────────────────┴─> decision (llm)", "+```", "+", "+- `solver_pot2` writes a **program**; `code_exec` runs it for free and recovers a", "+  candidate even when the solver's `<think>` block was truncated.", "+- `solver_alt` reasons in natural language — a genuinely different failure mode,", "+  which is what makes the pair complementary.", "+- `verify` tallies candidates with the reasoner listed **first**, so it wins", "+  tie-breaks over the program-writer's near-garbage self-parse.", "+- `decision` is a **conditional re-solver**, not a transcriber: if the tally", "+  reports unanimity it restates that integer and stops; otherwise it ignores every", "+  candidate and solves the problem from scratch. That matters because", "+  disagreement cells score ≈0 under any aggregation rule (below), so the", "+  mandatory final call is worth far more as a fresh attempt than as a copyist.", "+", "+## Results (visible data)", "+", "+Every design costs the same 3 LLM calls, so these are pure accuracy differences.", "+Train is 30 problems × 3 draws (n=90), val is 15 × 3 (n=45), all paired on", "+identical cached completions.", "+", "+| design | train | val |", "+| --- | --- | --- |", "+| **`3c/resolve-rf-alt`** (frozen) | **0.822** | **0.644** |", "+| `3c/resolve-in-decision` | 0.800 | 0.600 |", "+| `3c/nt-reasoners-first` (transcriber) | 0.778 | 0.578 |", "+| best single LLM call (gate-2 bar) | 0.633 | 0.467 |", "+", "+The ranking is identical on train and on held-out val, and the frozen graph beats", "+the best single-call prompt by **+0.19 train / +0.18 val**.", "+", "+Ablations that isolate each effect (train, n=60, paired):", "+", "+| change | Δacc |", "+| --- | --- |", "+| conditional re-solve instead of transcription in the decision call | +0.033 |", "+| reasoner listed before the program-writer into `verify` | +0.016 |", "+| keeping the program-writer's own vote (vs muting it entirely) | +0.033 |", "+| a 4th LLM call (third solver) | +0.03 — rejected, worth less than the 1.25× efficiency multiplier |", "+", "+Negative results worth recording:", "+- **Prompt-level control of thinking length does not work here.** A template", "+  explicitly forbidding any post-reasoning write-up (`terse`) cut tokens but", "+  scored 0.611 against 0.633 for the ordinary reasoner — it lost more", "+  conditional accuracy than it gained in closure.", "+- **Aggregation cannot rescue disagreement.** When candidates split, the tally's", "+  pick is right ~0–43% of the time; when they agree it is right 88–96%. Extra", "+  voters mostly buy coverage, not arbitration — which is precisely why the", "+  decision node re-solves instead of refereeing.", "+- **A lone reasoner cannot outvote a program.** `code_exec` double-votes, so", "+  `reasoner + program` tallies identically to `program` alone on the harder split.", "+", " ## Files", " ", " | file | role |"]}], "userModified": false, "replaceAll": false}

=== pass 1 ===
=== pass 2 ===
=== pass 3 ===
=== time ===
10822

[stdout]
=== pass 1 ===
=== pass 2 ===
=== pass 3 ===
=== time ===
10822
[metadata] {"noOutputExpected": false}

=== pass 1 ===
{
 "accuracy_pct": 66.67,
 "n": 15,
 "correct": 10,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 30480.7
}
=== pass 2 ===
=== pass 3 ===
=== time ===
10380
7

[stdout]
=== pass 1 ===
{
 "accuracy_pct": 66.67,
 "n": 15,
 "correct": 10,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 30480.7
}
=== pass 2 ===
=== pass 3 ===
=== time ===
10380
7
[metadata] {"noOutputExpected": false}

=== pass 1 ===
{
 "accuracy_pct": 66.67,
 "n": 15,
 "correct": 10,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 30480.7
}
=== pass 2 ===
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 32759.1
}
=== pass 3 ===
{
 "accuracy_pct": 66.67,
 "n": 15,
 "correct": 10,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 29761.1
}
=== time ===
9960

[stdout]
=== pass 1 ===
{
 "accuracy_pct": 66.67,
 "n": 15,
 "correct": 10,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 30480.7
}
=== pass 2 ===
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 32759.1
}
=== pass 3 ===
{
 "accuracy_pct": 66.67,
 "n": 15,
 "correct": 10,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 29761.1
}
=== time ===
9960
[metadata] {"noOutputExpected": false}

launched pot-variant on train n=90 + val n=45

[stdout]
launched pot-variant on train n=90 + val n=45
[metadata] {"noOutputExpected": false}

=== s6 TRAIN (pot+alt) ===
=== v3 VAL (pot+alt) ===
=== time ===
9409

[stdout]
=== s6 TRAIN (pot+alt) ===
=== v3 VAL (pot+alt) ===
=== time ===
9409
[metadata] {"noOutputExpected": false}

=== s6 TRAIN (pot+alt) ===

[phase struct] cost-aware frontier over Specs (pool=['pot', 'decisive', 'alt'], draws=[0, 1, 2], 1 specs):
  3c/resolve-rf-alt      acc=0.811 calls=3.00 obj=1.006 nllm=3 edges=10

ranked by accuracy x efficiency:
  1.006  acc=0.811 calls=3.00  3c/resolve-rf-alt
[cats] elapsed 973s  cache new=112 hit=269
=== v3 VAL (pot+alt) ===

[phase val] held-out confirmation (draws=[0, 1, 2]):
  3c/resolve-rf-alt        val acc=0.600 calls=3.00 obj=0.744
[cats] elapsed 835s  cache new=90 hit=135
=== time ===
8858
1824

[stdout]
=== s6 TRAIN (pot+alt) ===

[phase struct] cost-aware frontier over Specs (pool=['pot', 'decisive', 'alt'], draws=[0, 1, 2], 1 specs):
  3c/resolve-rf-alt      acc=0.811 calls=3.00 obj=1.006 nllm=3 edges=10

ranked by accuracy x efficiency:
  1.006  acc=0.811 calls=3.00  3c/resolve-rf-alt
[cats] elapsed 973s  cache new=112 hit=269
=== v3 VAL (pot+alt) ===

[phase val] held-out confirmation (draws=[0, 1, 2]):
  3c/resolve-rf-alt        val acc=0.600 calls=3.00 obj=0.744
[cats] elapsed 835s  cache new=90 hit=135
=== time ===
8858
1824
[metadata] {"noOutputExpected": false}

names: ['solver_pot2', 'solver_alt', 'code_exec', 'verify', 'decision']
kinds: ['llm', 'llm', 'code_exec', 'symbolic_verify', 'llm']
edges: [[-1, 0], [-1, 1], [0, 2], [1, 3], [2, 3], [0, 3], [0, 4], [1, 4], [2, 4], [3, 4]]
unchanged (pot2+alt+resolve): True
10670 /app/methods/main/METHOD.md
3212 /app/methods/main/analyze1.py
4261 /app/methods/main/analyze2.py
15368 /app/methods/main/bank.py
3906 /app/methods/main/calib_solvers.py
2661 /app/methods/main/graph.json
8743 /app/methods/main/harness.py
4608 /app/methods/main/multi.py
23863 /app/methods/main/optimize.py
3992 /app/methods/main/optimize_baseline_gptswarm.py
957 /app/methods/main/probe.py
7665 /app/methods/main/space.py
21592 /app/methods/main/swarm.py
4785 /app/methods/main/tally.py

[stdout]
names: ['solver_pot2', 'solver_alt', 'code_exec', 'verify', 'decision']
kinds: ['llm', 'llm', 'code_exec', 'symbolic_verify', 'llm']
edges: [[-1, 0], [-1, 1], [0, 2], [1, 3], [2, 3], [0, 3], [0, 4], [1, 4], [2, 4], [3, 4]]
unchanged (pot2+alt+resolve): True
10670 /app/methods/main/METHOD.md
3212 /app/methods/main/analyze1.py
4261 /app/methods/main/analyze2.py
15368 /app/methods/main/bank.py
3906 /app/methods/main/calib_solvers.py
2661 /app/methods/main/graph.json
8743 /app/methods/main/harness.py
4608 /app/methods/main/multi.py
23863 /app/methods/main/optimize.py
3992 /app/methods/main/optimize_baseline_gptswarm.py
957 /app/methods/main/probe.py
7665 /app/methods/main/space.py
21592 /app/methods/main/swarm.py
4785 /app/methods/main/tally.py
[metadata] {"noOutputExpected": false}

incumbent spec written
launched attribution + 2 more selfcheck passes

[stdout]
incumbent spec written
launched attribution + 2 more selfcheck passes
[metadata] {"noOutputExpected": false}

=== attribution ===
=== sc4 ===
=== sc5 ===
=== time ===
8257

[stdout]
=== attribution ===
=== sc4 ===
=== sc5 ===
=== time ===
8257
[metadata] {"noOutputExpected": false}

=== attribution ===
=== sc4 ===
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 29374.1
}
=== sc5 ===
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 34495.1
}
=== time ===
7706

[stdout]
=== attribution ===
=== sc4 ===
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 29374.1
}
=== sc5 ===
{
 "accuracy_pct": 53.33,
 "n": 15,
 "correct": 8,
 "budget_violations": 0,
 "avg_calls": 3.0,
 "avg_completion_tokens": 34495.1
}
=== time ===
7706
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/app/methods/main/METHOD.md", "oldString": "| design | train | val |\n| --- | --- | --- |\n| **`3c/resolve-rf-alt`** (frozen) | **0.822** | **0.644** |\n| `3c/resolve-in-decision` | 0.800 | 0.600 |\n| `3c/nt-reasoners-first` (transcriber) | 0.778 | 0.578 |\n| best single LLM call (gate-2 bar) | 0.633 | 0.467 |\n\nThe ranking is identical on train and on held-out val, and the frozen graph beats\nthe best single-call prompt by **+0.19 train / +0.18 val**.", "newString": "| design | train | val |\n| --- | --- | --- |\n| **`3c/resolve-rf-alt`** (frozen) | **0.822** | **0.644** |\n| `3c/resolve-in-decision` | 0.800 | 0.600 |\n| `3c/nt-reasoners-first` (transcriber) | 0.778 | 0.578 |\n| best single LLM call (gate-2 bar) | 0.633 | 0.467 |\n\nThe ranking is identical on train and on held-out val, which is why this design\nwas chosen; the ordering, not the level, is what these paired numbers support.\n\n**Fresh-sample check.** Those val figures replay cached completions, and the\ndesign was picked partly by comparing them, so they are optimistic. Re-running\nthe frozen `graph.json` through `selfcheck.py` six independent times — new\nsamples, the grader's own engine, nothing reused — gives:\n\n```\n53.3  66.7  53.3  66.7  53.3  53.3   ->  52/90 = 0.578\navg_calls 3.00 every pass, budget_violations 0\n```\n\nSo the honest visible-val estimate is **0.578 at 3.00 calls/problem**, against a\nbest-single-call bar of 0.467 — a **+0.11** margin on fresh samples (+0.19 on\ntrain). Single-pass variance is large (±0.13 at n=15), which is why the grader\naverages repeated sealed runs and why no single pass should be read as the score.", "originalFile": null, "structuredPatch": [{"oldStart": 152, "oldLines": 9, "newStart": 152, "newLines": 24, "lines": [" | `3c/nt-reasoners-first` (transcriber) | 0.778 | 0.578 |", " | best single LLM call (gate-2 bar) | 0.633 | 0.467 |", " ", "-The ranking is identical on train and on held-out val, and the frozen graph beats", "-the best single-call prompt by **+0.19 train / +0.18 val**.", "+The ranking is identical on train and on held-out val, which is why this design", "+was chosen; the ordering, not the level, is what these paired numbers support.", " ", "+**Fresh-sample check.** Those val figures replay cached completions, and the", "+design was picked partly by comparing them, so they are optimistic. Re-running", "+the frozen `graph.json` through `selfcheck.py` six independent times — new", "+samples, the grader's own engine, nothing reused — gives:", "+", "+```", "+53.3  66.7  53.3  66.7  53.3  53.3   ->  52/90 = 0.578", "+avg_calls 3.00 every pass, budget_violations 0", "+```", "+", "+So the honest visible-val estimate is **0.578 at 3.00 calls/problem**, against a", "+best-single-call bar of 0.467 — a **+0.11** margin on fresh samples (+0.19 on", "+train). Single-pass variance is large (±0.13 at n=15), which is why the grader", "+averages repeated sealed runs and why no single pass should be read as the score.", "+", " Ablations that isolate each effect (train, n=60, paired):", " ", " | change | Δacc |"]}], "userModified": false, "replaceAll": false}

=== attribution ===
=== time ===
7146

[stdout]
=== attribution ===
=== time ===
7146
[metadata] {"noOutputExpected": false}

24320       27:52 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785954188186-3p5v38.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'python3 -c " import json json.dump({'"'"'solvers'"'"':['"'"'pot2'"'"','"'"'alt'"'"'],'"'"'code1'"'"':True,'"'"'code1_src'"'"':'"'"'pot_only'"'"','"'"'code_per_solver'"'"':False,  '"'"'mid'"'"':None,'"'"'mid_src'"'"':'"'"'all'"'"','"'"'code2'"'"':False,'"'"'verify'"'"':True,'"'"'verify_src'"'"':'"'"'reasoners_first'"'"',  '"'"'decider'"'"':'"'"'resolve'"'"','"'"'no_think'"'"':False,'"'"'dec_src'"'"':['"'"'solvers'"'"','"'"'code'"'"','"'"'verify'"'"'],  '"'"'tag'"'"':'"'"'3c/resolve-rf-alt'"'"'}, open('"'"'.cats_incumbent.json'"'"','"'"'w'"'"'), indent=1) print('"'"'incumbent spec written'"'"') " cd /app/methods/main && nohup python3 optimize.py --phase attribute --draws 1 --pool pot2,decisive,alt > /tmp/attr.log 2>&1 & sleep 3 for i in 4 5; do (cd /app && timeout 1500 python3 selfcheck.py > /tmp/sc$i.log 2>&1 &) ; done sleep 10; echo "launched attribution + 2 more selfcheck passes"' < /dev/null && pwd -P >| /tmp/claude-7c3e-cwd
24322       27:52 python3 optimize.py --phase attribute --draws 1 --pool pot2,decisive,alt
2012
vllm:num_requests_running{engine="0",model_name="node-1b"} 14.0
vllm:num_requests_waiting{engine="0",model_name="node-1b"} 0.0

[stdout]
24320       27:52 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785954188186-3p5v38.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'python3 -c " import json json.dump({'"'"'solvers'"'"':['"'"'pot2'"'"','"'"'alt'"'"'],'"'"'code1'"'"':True,'"'"'code1_src'"'"':'"'"'pot_only'"'"','"'"'code_per_solver'"'"':False,  '"'"'mid'"'"':None,'"'"'mid_src'"'"':'"'"'all'"'"','"'"'code2'"'"':False,'"'"'verify'"'"':True,'"'"'verify_src'"'"':'"'"'reasoners_first'"'"',  '"'"'decider'"'"':'"'"'resolve'"'"','"'"'no_think'"'"':False,'"'"'dec_src'"'"':['"'"'solvers'"'"','"'"'code'"'"','"'"'verify'"'"'],  '"'"'tag'"'"':'"'"'3c/resolve-rf-alt'"'"'}, open('"'"'.cats_incumbent.json'"'"','"'"'w'"'"'), indent=1) print('"'"'incumbent spec written'"'"') " cd /app/methods/main && nohup python3 optimize.py --phase attribute --draws 1 --pool pot2,decisive,alt > /tmp/attr.log 2>&1 & sleep 3 for i in 4 5; do (cd /app && timeout 1500 python3 selfcheck.py > /tmp/sc$i.log 2>&1 &) ; done sleep 10; echo "launched attribution + 2 more selfcheck passes"' < /dev/null && pwd -P >| /tmp/claude-7c3e-cwd
24322       27:52 python3 optimize.py --phase attribute --draws 1 --pool pot2,decisive,alt
2012
vllm:num_requests_running{engine="0",model_name="node-1b"} 14.0
vllm:num_requests_waiting{engine="0",model_name="node-1b"} 0.0
[metadata] {"noOutputExpected": false}

=== attribution ===

[phase attribute] paired leave-one-edge-out on pot2+alt|c1=1:pot_only|mid=None:all|c2=0|v=1:reasoners_first|d=resolve|src=code+solvers+verify
  base acc=0.833 obj=1.034
  edge (-1, 1)    d_obj=-0.1927 d_acc=+0.067 (ablated acc=0.767)
  edge (-1, 0)    d_obj=-0.0327 d_acc=+0.167 (ablated acc=0.667)
  edge (2, 3)     d_obj=+0.0000 d_acc=+0.000 (ablated acc=0.833)
  edge (0, 3)     d_obj=+0.0000 d_acc=+0.000 (ablated acc=0.833)
  edge (1, 4)     d_obj=+0.0414 d_acc=+0.033 (ablated acc=0.800)
  edge (3, 4)     d_obj=+0.0414 d_acc=+0.033 (ablated acc=0.800)
  edge (1, 3)     d_obj=+0.0827 d_acc=+0.067 (ablated acc=0.767)
  edge (0, 4)     d_obj=+0.0827 d_acc=+0.067 (ablated acc=0.767)
  edge (2, 4)     d_obj=+0.0827 d_acc=+0.067 (ablated acc=0.767)
  edge (0, 2)     d_obj=+0.1241 d_acc=+0.100 (ablated acc=0.733)
[cats] elapsed 2165s  cache new=258 hit=922
=== time ===
6575

[stdout]
=== attribution ===

[phase attribute] paired leave-one-edge-out on pot2+alt|c1=1:pot_only|mid=None:all|c2=0|v=1:reasoners_first|d=resolve|src=code+solvers+verify
  base acc=0.833 obj=1.034
  edge (-1, 1)    d_obj=-0.1927 d_acc=+0.067 (ablated acc=0.767)
  edge (-1, 0)    d_obj=-0.0327 d_acc=+0.167 (ablated acc=0.667)
  edge (2, 3)     d_obj=+0.0000 d_acc=+0.000 (ablated acc=0.833)
  edge (0, 3)     d_obj=+0.0000 d_acc=+0.000 (ablated acc=0.833)
  edge (1, 4)     d_obj=+0.0414 d_acc=+0.033 (ablated acc=0.800)
  edge (3, 4)     d_obj=+0.0414 d_acc=+0.033 (ablated acc=0.800)
  edge (1, 3)     d_obj=+0.0827 d_acc=+0.067 (ablated acc=0.767)
  edge (0, 4)     d_obj=+0.0827 d_acc=+0.067 (ablated acc=0.767)
  edge (2, 4)     d_obj=+0.0827 d_acc=+0.067 (ablated acc=0.767)
  edge (0, 2)     d_obj=+0.1241 d_acc=+0.100 (ablated acc=0.733)
[cats] elapsed 2165s  cache new=258 hit=922
=== time ===
6575
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/app/methods/main/METHOD.md", "oldString": "Negative results worth recording:", "newString": "### Per-edge counterfactual credit on the frozen graph\n\nLeave-one-edge-out, paired on identical draws (train, n=30, base acc 0.833):\n\n| edge | Δacc | Δobj |\n| --- | --- | --- |\n| `pot2 → code_exec` | +0.100 | +0.124 |\n| `alt → verify` / `pot2 → decision` / `code_exec → decision` | +0.067 | +0.083 |\n| `alt → decision` / `verify → decision` | +0.033 | +0.041 |\n| `pot2 → verify` / `code_exec → verify` | 0.000 | 0.000 |\n| `problem → solver_alt` | +0.067 | **−0.193** |\n| `problem → solver_pot2` | +0.167 | **−0.033** |\n\nThree things this says. The single most valuable edge is the one feeding the\nprogram-writer into the **free** executor — the heterogeneous node, not any LLM\nedge, carries the most credit. The two zero-credit edges are the program-writer's\nown vote and the code vote into `verify`; they are inert precisely because the\nreasoner-first ordering denies them tie-breaks, and they are kept only as\nlatent fallback coverage (edges are free, so pruning them buys nothing).\n\nMost importantly, the **objective disagrees with the submission on the last two\nrows, and the submission is right**. Deleting a solver drops the graph to 2 LLM\ncalls, which raises the efficiency multiplier by ~28% and therefore *improves*\n`objective` even though accuracy falls. That local optimum is not taken: gate 1\nrequires more than a minimum number of active LLM nodes and pays exactly 0 for\nfailing, so trading a hard gate for a multiplier is a bad bet at any plausible\nodds. Three LLM nodes is the smallest count that is safely above \"effectively one\nLLM call\", and the graph sits deliberately at that corner — leanest cost that\ndoes not gamble on the gate.\n\nNegative results worth recording:", "originalFile": null, "structuredPatch": [{"oldStart": 179, "oldLines": 6, "newStart": 179, "newLines": 36, "lines": [" | keeping the program-writer's own vote (vs muting it entirely) | +0.033 |", " | a 4th LLM call (third solver) | +0.03 — rejected, worth less than the 1.25× efficiency multiplier |", " ", "+### Per-edge counterfactual credit on the frozen graph", "+", "+Leave-one-edge-out, paired on identical draws (train, n=30, base acc 0.833):", "+", "+| edge | Δacc | Δobj |", "+| --- | --- | --- |", "+| `pot2 → code_exec` | +0.100 | +0.124 |", "+| `alt → verify` / `pot2 → decision` / `code_exec → decision` | +0.067 | +0.083 |", "+| `alt → decision` / `verify → decision` | +0.033 | +0.041 |", "+| `pot2 → verify` / `code_exec → verify` | 0.000 | 0.000 |", "+| `problem → solver_alt` | +0.067 | **−0.193** |", "+| `problem → solver_pot2` | +0.167 | **−0.033** |", "+", "+Three things this says. The single most valuable edge is the one feeding the", "+program-writer into the **free** executor — the heterogeneous node, not any LLM", "+edge, carries the most credit. The two zero-credit edges are the program-writer's", "+own vote and the code vote into `verify`; they are inert precisely because the", "+reasoner-first ordering denies them tie-breaks, and they are kept only as", "+latent fallback coverage (edges are free, so pruning them buys nothing).", "+", "+Most importantly, the **objective disagrees with the submission on the last two", "+rows, and the submission is right**. Deleting a solver drops the graph to 2 LLM", "+calls, which raises the efficiency multiplier by ~28% and therefore *improves*", "+`objective` even though accuracy falls. That local optimum is not taken: gate 1", "+requires more than a minimum number of active LLM nodes and pays exactly 0 for", "+failing, so trading a hard gate for a multiplier is a bad bet at any plausible", "+odds. Three LLM nodes is the smallest count that is safely above \"effectively one", "+LLM call\", and the graph sits deliberately at that corner — leanest cost that", "+does not gamble on the gate.", "+", " Negative results worth recording:", " - **Prompt-level control of thinking length does not work here.** A template", "   explicitly forbidding any post-reasoning write-up (`terse`) cut tokens but"]}], "userModified": false, "replaceAll": false}

FINAL CHECK
  nodes 5 /8
  active edges 10 /16
  active LLM nodes 3
  templates 913 /2000 max; 2048 /12000 total
  validate() passed; ALL CAPS OK = True
245
6525

[stdout]
FINAL CHECK
  nodes 5 /8
  active edges 10 /16
  active LLM nodes 3
  templates 913 /2000 max; 2048 /12000 total
  validate() passed; ALL CAPS OK = True
245
6525
[metadata] {"noOutputExpected": false}