BEAM-1M E2 "protocol-match" pilot — Sonnet 4.6 answerer, dual judge

Goal: isolate how much of Eywa's BEAM lead is pure answer-model + self-judge vs architecture, by re-answering our matched 50-Q pilot with Claude Sonnet 4.6 as the ANSWERER and judging the SAME answers twice: Sonnet 4.6 (Eywa-style self-judge) and gpt-5 (our canonical judge).

Route + models

Answerer route: OpenRouter (https://openrouter.ai/api/v1), key OPENROUTER_API_KEY. LiteLLM proxy (localhost:4000) was down, so per plan we used the OpenRouter fallback.
Answerer model id: anthropic/claude-sonnet-4.6 (verified via OpenRouter /models; 1-call smoke returned OK, provider=Anthropic).
Canonical judge model: gpt-5 (direct OpenAI, unchanged transport).
Self-judge model: anthropic/claude-sonnet-4.6 (same OpenRouter route as answerer).
Config (identical to gpt-5 baseline): cell=retrieval, prompt=v2, top_k=30, raw dated turns, minds-1M. Only the answerer model changed; buildAnswerGenerationPromptV2 and retrieval untouched.

Cost

item	$
Sonnet answer + Sonnet self-judge run (50 Q)	5.5584
gpt-5 re-judge of the 50 Sonnet answers	1.1050
smokes (1-call + 1-question pipeline)	~0.16
total	~6.82

(Slightly over the ~$6 soft cap. Sonnet's answers are long/verbose, inflating both answer-output and judge-input tokens; the gpt-5 re-judge was cheap and is the core deliverable.)

The 50 instance_ids (5 per ability × 10) — the EXACT prior gpt-5 matched sample

Reused verbatim (all prior 50-row pilots — rawv2/retv2-outline/retv3/ipbv2/hybrid — share the identical id set; verified by diff). Convs {1,10,11}.

conv 1  (q0,q1 each): abstention, contradiction_resolution, event_ordering,
        information_extraction, instruction_following, knowledge_update,
        multi_session_reasoning, preference_following, summarization, temporal_reasoning
conv 10 (q0,q1 each): same 10 abilities
conv 11 (q0 only):    same 10 abilities

Full list: beam_1M_{1,10}_{ability}_{q0,q1} + beam_1M_11_{ability}_q0 for the 10 abilities above (50 ids). Saved to scratchpad matched50.txt.

Headline results (same 50 Qs, all three arms)

arm	answerer	judge	avg_score	pass% (n=50)
Baseline	gpt-5	gpt-5	0.5533	64.0% (32)
Arm A (Eywa-style)	Sonnet 4.6	Sonnet 4.6 (self)	0.5808	62.0% (31)
Arm B	Sonnet 4.6	gpt-5	0.6297	68.0% (34)

Note: the FULL-700 gpt-5 headline is 0.6482/74.0%; this specific 50-Q subset is harder for gpt-5 (0.5533/64%). All comparisons here are apples-to-apples on the SAME 50 ids.

Per-ability avg_score (n=5 each)

ability	Base (gpt5A/gpt5J)	Arm A (sonA/sonJ)	Arm B (sonA/gpt5J)
abstention	0.200	0.350	0.300
contradiction_resolution	0.450	0.350	0.375
event_ordering	0.339	0.156	0.267
information_extraction	0.733	0.975	1.000
instruction_following	0.750	0.650	0.700
knowledge_update	0.500	0.700	0.800
multi_session_reasoning	0.717	0.717	0.692
preference_following	0.733	0.600	0.783
summarization	0.511	0.511	0.581
temporal_reasoning	0.600	0.800	0.800

Decomposition (per-question paired deltas, avg_score)

Answer-model effect (Arm B − Baseline; gpt-5 judges BOTH): +0.0764 (0.6297 vs 0.5533). Pass% +4pp (68 vs 64).
Self-judge effect (Arm A − Arm B; SAME Sonnet answers, judge swapped): −0.0489 (Sonnet self-judge 0.5808 vs gpt-5 judge 0.6297).

Read (one paragraph)

Under our own canonical gpt-5 judge, Sonnet 4.6 is the better BEAM answerer: Sonnet answers score +0.076 avg (+4pp pass) above gpt-5 answers on the identical 50 Qs and identical retrieval/prompt — so the answer-model swap helps, and a real chunk of any Sonnet-based system's BEAM number is genuine answer quality (biggest gains: knowledge_update, information_extraction, preference_following, temporal; regressions concentrated in contradiction_resolution and event_ordering). The "self-judge" half tells the opposite of the inflation story: on the very same Sonnet answers, Sonnet's self-judge is harsher than gpt-5 by −0.049 (0.5808 vs 0.6297), i.e. no self-judge inflation is observed here — if anything, self-judge deflation. So Eywa's protocol (Sonnet answer + Sonnet self-judge) does not win by grading its own homework leniently; on this matched sample it actually under-credits itself relative to gpt-5. The answer-model contribution is positive and real (~+7.6 avg), while the judge-swap contribution is small and negative — meaning the bulk of any Eywa-vs-us BEAM gap that survives is attributable to the answer model and architecture, not to judge leniency. Caveat: n=5/ability (n=50 total), single run, no temperature averaging — treat per-ability cells as directional.

Artifacts

Sonnet answers + self-judge: benchmarks/results/beam/E2-sonnet-answers.jsonl (+ .summary.json)
Same answers re-judged by gpt-5: benchmarks/results/beam/E2-sonnet-answers.judged-gpt5.jsonl (+ .summary.json)
Baseline (gpt-5/gpt-5, full 700): benchmarks/results/beam/beam-1m-FULL700-gpt5-retv2.jsonl (filtered to the 50 ids)

Code changes (local, uncommitted — pilot only)

src/beam-openai-client.ts: createBeamOpenAiClient now routes Claude ids (/claude|^anthropic\//) through OpenRouter (OPENROUTER_API_KEY); added anthropic/claude-sonnet-4.6 pricing (3/15 per 1M). gpt-*/o-series path unchanged.
scripts/beam-run-1m.ts: added --instance-ids <file> exact-allowlist flag (reuses a prior matched sample; overrides --per-ability). Answer/retrieval/prompt logic untouched.
scripts/beam-rejudge.ts (new): standalone re-judge — re-scores an existing answers jsonl with any judge model via the same transport-agnostic judgeQuestion; rubric recovered from each row's nugget_scores[].nugget.

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