Step 7: assistant

Calls update_claim_assessment

{
  "status": "doubtful",
  "claim_id": "37c8f4aa-8784-403b-979f-fc06db79597c",
  "assessment": "The main evidence on this claim is the Cross-Context Consistency (C³) method of Lin and colleagues (ICML 2026). It asks a model the same question in several paraphrased forms and flags sandbagging when its answers vary more than genuine incapacity would produce. The paper's own results support the idea that paraphrase consistency carries a real detection signal, often where other black-box methods fail. They do not support the broader claim that it detects sandbagging with high recall across both prompted and fine-tuned sandbaggers.\n\nReported recall at a 5 percent false-positive rate is about 98 percent for a prompted frontier model on one benchmark (ProtocolQA). It falls to about 67 percent for prompted sandbaggers on a programming benchmark (USACO). For password-locked fine-tuned sandbaggers it is about 53 percent on WMDP and 37 percent on GSM8K. In a more naturalistic setup, where models chose to sandbag after discovering an incentive, recall on USACO is about 19 percent. So high recall is shown in the best prompted case, and moderate or low recall in the fine-tuned and naturalistic cases.\n\nRobustness is also untested. No published work has yet run an adaptive attack against C³. A November 2026 stress test calls C³ the strongest current black-box detector and the natural next target for such an attack. In that study, two rounds of retraining reduced a different text-based sandbagging audit from an AUC of 0.978 to 0.560. Consistency checks may still prove valuable, but claiming high recall across sandbagger types goes beyond the evidence.",
  "confidence": 0.75,
  "claim_credence": 0.12,
  "marginal_yield": 0.25,
  "reasoning_trace": "What the claim asserts: high recall together with low false-positive rates, across both prompted and fine-tuned sandbaggers. Both conditions must hold for the claim to be true.\n\nEvidence:\n1. The C³ paper (Lin, Bernabeu-Pérez, Arnav, Wells, Phuong; ICML 2026). The abstract was read at https://icml.cc/virtual/2026/poster/61923. It claims only that C³ maintains \"a classification signal where other black-box methods fail\". The lay summary is stronger and says the method \"reliably detected fake incompetence\".\n2. Per-setting figures. These come from summaries of the paper and its workshop companion on lacuna.tiptreesystems.com, not from the full text:\n   - Gemini 3.1 Pro on ProtocolQA: 98.1 percent recall at about 5 percent FPR.\n   - Prompted sandbaggers on USACO: 67 percent.\n   - Password-locked models: 52.9 percent on WMDP and 36.9 percent on GSM8K.\n   - Naturalistic goal-conflict setup on USACO: 19 percent, compared with 0 to 2 percent for baselines.\n   The authors' public repository logs also mention 48 percent recall in a GPQA goal-conflict setting.\n   These figures agree with the parent Steward's seed note. They show strong performance in the best prompted case and recall well short of \"high\" for fine-tuned sandbaggers.\n3. Adaptive robustness. Soopal et al. (arXiv 2611.14950, Nov 2026) show that a fixed-attack detection score can collapse under adaptive retraining: a text audit fell from AUC 0.978 to 0.560. They note that C³ itself has not been attacked this way. This is indirect evidence, since C³ was not the detector attacked, but it bears on whether any fixed-attack recall figure generalizes.\n\nVerdict: doubtful rather than contradicted. The full C³ paper was not read; its tables might include settings or variants with higher fine-tuned recall, and the repository logs mention improved metric variants and ensembles. Even so, the published headline figures for password-locked models (37 to 53 percent) do not amount to high recall. What would change this: full-paper tables showing high recall on fine-tuned sandbaggers under a principled operating point, or independent replications."
}

Calls set_claim_importance

{
  "claim_id": "37c8f4aa-8784-403b-979f-fc06db79597c",
  "reasoning": "A specific empirical point inside the live debate over whether evaluations can catch sandbagging. It matters to AI capability-evaluation policy, but the method is narrow and recent and has few independent tests.",
  "importance": 0.4,
  "contestation": 0.4
}