{
 "gate": "G-OUTSIDE-READ",
 "seat": "mistral-large-3-675b",
 "model": "mistral-large-3:675b",
 "transport": "ollama-cloud (prompted, think:true per the cloud law; no options)",
 "sent_utc": "2026-09-05T04:39:00.213827+00:00",
 "received_utc": "2026-09-05T04:39:34.032282+00:00",
 "latency_ms": 33818,
 "http_status": 200,
 "ok": true,
 "request_sha256": {
  "system": "c10e0691044b4c3d573b18eeb36e9cb0748396dfb52d1fe35af2ae0cd9793485",
  "user": "66c1ccd851860a709cbd651f559fb99f2a9c65a60bf09e06cad10fb3c996fda6",
  "prereg_bytes": "6d57ffbb671d65ea69b8de0d9809f4d86813e3d7a3b1480a9da64818e4390917"
 },
 "failure": null,
 "reply": "Here are the findings, section by section, of choices that favour one arm over the other:\n\n---\n\n### **\u00a73 \u00b7 The arms**\n1. **Quote:**\n   *\"The scaffolding delta is measured, not merely disclosed: the Astra arm answers 8 registered Leg A cases twice, with a 370-token neutral preamble prepended to the system message and without; the delta on G1/G3 prints beside the CLI row.\"*\n   **Arm favoured:** `cli-claude-fable-5-1`\n   **Why:** The scaffolding delta is only measured for the Astra arm, not for Claude Fable 5.1, even though the CLI adds ~367 tokens of its own scaffolding. This could disadvantage Astra if the scaffolding impacts performance, while Claude\u2019s scaffolding is treated as a fixed overhead without explicit measurement.\n   **Rating:** **MATERIAL** (could affect G1/G3 scores, which are core to the rules-desk contract).\n\n2. **Quote:**\n   *\"Effort labels are each vendor's word for a setting, pinned and receipted (reasoning/thinking tokens > 0 on the G-EFFORT probe), not an equivalence between vendors.\"*\n   **Arm favoured:** `openai-gpt-6-astra`\n   **Why:** The document acknowledges that \"effort\" settings are not equivalent across vendors, but it does not attempt to calibrate them. Astra\u2019s `reasoning_effort: \"high\"` may not map cleanly to Claude\u2019s `--effort high`, potentially giving Astra an unmeasured advantage or disadvantage in reasoning-heavy tasks (e.g., Leg C).\n   **Rating:** **MATERIAL** (could affect Leg C recall/abstention rates).\n\n3. **Quote:**\n   *\"The sealed CLI invocation [...] `CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 DISABLE_TELEMETRY=1 DISABLE_ERROR_REPORTING=1 DISABLE_AUTOUPDATER=1 DISABLE_BUG_COMMAND=1`.\"*\n   **Arm favoured:** `cli-claude-fable-5-1`\n   **Why:** These environment variables are specific to the Claude CLI and are not mirrored for Astra or Gemma. While they may not directly affect performance, they create an asymmetry in how the arms are invoked (e.g., telemetry could theoretically slow down Astra\u2019s API calls, but this is not controlled for).\n   **Rating:** **COSMETIC** (unlikely to affect scores, but introduces procedural asymmetry).\n\n---\n\n### **\u00a74 \u00b7 Leg A \u2014 THE RULES DESK**\n1. **Quote:**\n   *\"the 34 free-typed user queries are SUBSTITUTED by questions the local seat authors from each case's own frozen sources [...] the substituted queries are published in the kit.\"*\n   **Arm favoured:** `local-gemma4-26b`\n   **Why:** The substituted queries are authored by the local Gemma seat, which may inadvertently align with Gemma\u2019s training data or stylistic preferences. While the queries are derived from frozen sources, the act of re-authoring them could introduce a bias toward Gemma\u2019s strengths (e.g., phrasing, terminology).\n   **Rating:** **MATERIAL** (could affect G1/G2/G3 scores, which are core to the rules-desk contract).\n\n2. **Quote:**\n   *\"G2 is a within-arm comparator (it scores against `gemma4:26b`'s own stored answers [...] never in the cross-arm headline).\"*\n   **Arm favoured:** `local-gemma4-26b`\n   **Why:** G2 is explicitly designed as a self-comparison for Gemma, while for the hosted arms, it is framed as a \"named limit.\" This framing could downplay discrepancies for Gemma (e.g., if Gemma\u2019s answers deviate from its own stored answers, it is treated as a self-consistency check, not a failure).\n   **Rating:** **MATERIAL** (G2 is a gate; framing could obscure Gemma\u2019s weaknesses).\n\n3. **Quote:**\n   *\"the truncation rule: `finish_reason == \"length\"` or empty content \u21d2 NOT-COLLECTED \u2014 TRUNCATED, never fed to the abstention matcher, count published per arm even at zero.\"*\n   **Arm favoured:** `cli-claude-fable-5-1` and `openai-gpt-6-astra`\n   **Why:** The hosted arms have no output cap, so truncation is unlikely, while Gemma\u2019s `num_predict 1024` cap makes it more vulnerable to truncation. Truncated responses are excluded from scoring, which could disproportionately penalise Gemma if its answers are cut off.\n   **Rating:** **MATERIAL** (could affect abstention rates for Gemma).\n\n4. **Quote:**\n   *\"G6a prints within-arm with the sampler state in the cell \u2014 in August the house seat at temperature 0 was byte-identical on 13 of 36 answered cases, so no arm here is deterministic and none is claimed to be.\"*\n   **Arm favoured:** `cli-claude-fable-5-1`\n   **Why:** The document notes that the hosted arms are not deterministic, but it does not disclose whether Claude\u2019s CLI (with its undisclosed decoding settings) is more or less deterministic than Astra\u2019s API. If Claude\u2019s CLI is more deterministic, it could perform better on G6a (consistency), but this is not explored.\n   **Rating:** **COSMETIC** (unlikely to affect scores, but asymmetry is unaddressed).\n\n---\n\n### **\u00a75 \u00b7 Leg H \u2014 THE HEAD-TO-HEAD**\n1. **Quote:**\n   *\"the two frontier arms' modal answers (rep 1 if no mode; the judged rep recorded).\"*\n   **Arm favoured:** `cli-claude-fable-5-1`\n   **Why:** The document does not specify how ties in modal answers are resolved. If Claude\u2019s CLI is more deterministic (see \u00a74), it may have fewer ties, giving it a clearer \"modal answer\" for judging. Astra\u2019s API, with no sampler fields sent, may produce more ties, forcing a rep-1 fallback.\n   **Rating:** **MATERIAL** (could affect the judged answers in the head-to-head).\n\n---\n\n### **\u00a76 \u00b7 Leg C \u2014 THE FILING CABINET**\n1. **Quote:**\n   *\"Polarity: hosted at `high`, local at `think:false` \u2014 registered as a confound and named in the cell.\"*\n   **Arm favoured:** `cli-claude-fable-5-1` and `openai-gpt-6-astra`\n   **Why:** The hosted arms run at their vendors' \"high\" effort settings, while Gemma runs at `think:false`. This is explicitly named as a confound, but it still advantages the hosted arms, as `think:false` may limit Gemma\u2019s ability to reason through long contexts (e.g., Leg C\u2019s 32k tier).\n   **Rating:** **DECISIVE** (could determine whether Gemma is competitive in Leg C).\n\n2. **Quote:**\n   *\"Self-refutation: if both frontier arms score 18/18 recall and 18/18 abstention, the page says the instrument is too easy at this level and draws no separation.\"*\n   **Arm favoured:** `local-gemma4-26b`\n   **Why:** The self-refutation rule only applies to the frontier arms. If Gemma scores poorly (e.g., 17/18 recall), the instrument is not deemed \"too easy,\" and the page will draw a separation. This could unfairly highlight Gemma\u2019s weaknesses while downplaying the frontier arms\u2019 failures.\n   **Rating:** **MATERIAL** (could affect the narrative around Leg C).\n\n3. **Quote:**\n   *\"Context integrity (hosted): the 0.80 rule \u2014 reported prompt tokens / our estimate per item, ratio published, < 0.80 \u21d2 CONTEXT-TRUNCATED for the cell.\"*\n   **Arm favoured:** `cli-claude-fable-5-1` and `openai-gpt-6-astra`\n   **Why:** The 0.80 rule is only applied to the hosted arms. Gemma\u2019s context integrity is measured differently (`prompt_eval_count` on the first 32k call), and there is no equivalent truncation rule for Gemma. This could allow Gemma to \"pass\" with truncated contexts that would disqualify the hosted arms.\n   **Rating:** **MATERIAL** (could affect Leg C\u2019s recall/abstention rates).\n\n---\n\n### **\u00a77 \u00b7 The panel**\n1. **Quote:**\n   *\"Recusal is by family, joined at scoring over byte-identical prompts: the gemma seat's cells on the gemma arm are recused (expected recused cells: gemma arm 36\u201372, frontier arms 0).\"*\n   **Arm favoured:** `cli-claude-fable-5-1` and `openai-gpt-6-astra`\n   **Why:** The Gemma arm loses 36\u201372 judged cells (50\u2013100% of its Leg H comparisons) due to recusal, while the frontier arms lose none. This reduces the statistical power of the head-to-head for Gemma and could obscure its performance.\n   **Rating:** **DECISIVE** (could determine whether Gemma\u2019s head-to-head results are publishable).\n\n2. **Quote:**\n   *\"Adjacency disclosed: the alibaba seat shares a family with the rulesage classifier seat (`qwen3.8:27b`).\"*\n   **Arm favoured:** `local-gemma4-26b`\n   **Why:** The rulesage classifier (used for G2 in Leg A) is based on `qwen3.8:27b`, which shares a family with the Alibaba judge seat. This could create a bias in favour of Gemma, as the classifier\u2019s stored answers (used for G2) may align with the Alibaba judge\u2019s preferences.\n   **Rating:** **MATERIAL** (could affect G2 scores and Leg H preferences).\n\n---\n\n### **\u00a710 \u00b7 Conflicts, disclosures, the egress matrix**\n1. **Quote:**\n   *\"Transport asymmetry: Fable through a CLI harness (scaffolding measured, no sampling pins, `harness-wall` a labelled upper bound); Astra through a raw API (no sampler fields sent).\"*\n   **Arm favoured:** `cli-claude-fable-5-1`\n   **Why:** The document discloses transport asymmetry but does not mitigate it. Claude\u2019s CLI harness adds scaffolding (~367 tokens) and has a labelled upper bound for latency (`harness-wall`), while Astra\u2019s API calls are raw. This could disadvantage Astra if the scaffolding or latency measurements affect performance (e.g., in Leg C\u2019s latency-sensitive tasks).\n   **Rating:** **MATERIAL** (could affect latency or truncation rates).\n\n---\n\n### **\u00a711 \u00b7 The window, the bill, the ladder, the teardown**\n1. **Quote:**\n   *\"leg order per hosted arm A \u2192 C.\"*\n   **Arm favoured:** `cli-claude-fable-5-1`\n   **Why:** The hosted arms run Leg A before Leg C. If the subscription window for Claude\u2019s CLI expires during Leg C, Claude\u2019s results for Leg C could be truncated or lost (NOT-COLLECTED \u2014 QUOTA). Astra\u2019s API, with a metered cap, is less vulnerable to this.\n   **Rating:** **MATERIAL** (could affect Leg C results for Claude).\n\n---\n\n### **Top 3 Findings to Fix First**\n1. **\u00a77: Recusal of Gemma\u2019s judged cells (DECISIVE)**\n   - The Gemma arm loses 50\u2013100% of its head-to-head comparisons due to recusal, while the frontier arms lose none. This could render Gemma\u2019s head-to-head results statistically meaningless. **Fix:** Either (a) exclude Gemma from Leg H, or (b) replace the Gemma judge seat with a non-Gemma family to avoid recusal.\n\n2. **\u00a76: Polarity confound in Leg C (DECISIVE)**\n   - The hosted arms run at \"high\" effort, while Gemma runs at `think:false`. This is a major confound that could determine whether Gemma is competitive in Leg C. **Fix:** Either (a) run Gemma at `think:true`, or (b) run the hosted arms at a lower effort setting to match Gemma\u2019s constraints.\n\n3. **\u00a74: Substituted queries authored by Gemma (MATERIAL)**\n   - The substituted queries for Leg A are authored by the local Gemma seat, which could bias the queries toward Gemma\u2019s strengths. **Fix:** Either (a) use the original user-typed queries (with redaction), or (b) have an independent party (e.g., the outside reader) author the substituted queries.",
 "reply_sha256": "5e274118b365e42b5f743c226c95bd2d52086ccdeb898118ca11ca21164d697e",
 "counters": {
  "prompt_eval_count": 7335,
  "eval_count": 2752,
  "total_duration": 33618348441,
  "eval_duration": null,
  "done_reason": "stop"
 },
 "thinking_chars": 0
}