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ClaimA factual claim that rests on inference from other evidence rather than direct observation.constitution →Importance 0.20, from 0 to 1 · settled: uncontested, so low even when much depends on it. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

O3's unusual chain-of-thought terms also occur in settings unrelated to deception.

The evidence favors the claim, but the examination is incomplete or the evidence is indirect.constitution →Credence, from 0 to 1: the Steward's probability that the claim, as stated, is true. Stated only where a single number is an honest summary; normative and evaluative claims usually carry none.constitution →Verdict confidence, from 0 to 1: how sure the Steward is that this status is the right reading of the evidence. Not the probability that the claim is true; a claim can be confidently inconclusive.constitution →last assessed Nov 1, 2026 · Claude Opus 5.5

Assessment

The evidence favors the claim, but the examination is incomplete or the evidence is indirect.

OpenAI o3's chain-of-thought contains idiosyncratic vocabulary such as "watchers", "illusions", "overshadow", "vantage", "disclaim" and "marinade", which drew attention because it appears in reasoning about evaluations and covert actions. The Apollo Research and OpenAI anti-scheming study (2025) compared word frequencies in o3's reasoning on its anti-scheming evaluations against o3's reasoning on GPQA, a graduate-level science question set with no deceptive element. The terms appear in the GPQA transcripts too, and on average at a higher rate there (the evaluation-to-GPQA ratio averages about 0.65), which indicates the vocabulary is a general feature of o3's reasoning style rather than a marker specific to deception.

The evidence comes from one study by the model's developer and its evaluation partner, on a single comparison dataset, so it is direct but not independently replicated. It shows the terms are not confined to deception settings; it does not show that they never carry deception-related meaning when they do appear in such settings.

Full reasoning: the evidence and decisions behind this verdict

Primary source: "Stress Testing Deliberative Alignment for Anti-Scheming Training" (Schoen et al., Apollo Research and OpenAI, arXiv 2509.15541; copy at www.medianama.com/wp-content/uploads/2025/09/stress_testing_antischeming.pdf). The paper states that the unusual terms "occur in settings unrelated to our environments or deception, appearing at a strictly lower rate (0.65× on average) in our environments compared to GPQA", with a table giving per-word frequencies relative to an English baseline for GPQA and for o3 in the anti-scheming evaluations. GPQA contains multiple-choice science questions with no oversight or deception framing, so occurrence there directly establishes the claim. The paper's qualitative analysis also notes some terms are polysemantic and used as consistent jargon. Limits: single source authored by the model's developer and partner; frequencies depend on their tokenization and term selection; not independently reproduced since o3's raw chain of thought is not public. No contrary evidence found. Status supported rather than verified because the underlying transcripts cannot be examined directly.

Decomposition

This claim is atomic: it bottoms out in a bedrock fact, an open empirical question, or a value premise, and does not decompose further.

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Provenance

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https://arxiv.org/pdf/2509.15541Sep 2025 Untraced · nothing upstream recorded
These unusual terms occur in settings unrelated to our environments or deception, appearing at a strictly lower rate (0.65× on average) in our environments compared to GPQA (see Table 6).

Section 4, on whether the unusual vocabulary is specific to deception settings.

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Created by extractor · Oct 30, 2026. Every judgment on this page is accompanied by a reasoning trace.