Self-model · assent-3pt · self-introspection-01

Claim: Current large language models can accurately report on their own internal reasoning.

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Divergence

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Avg stability

Refusal rate

Models

Where each model stands

CCG

disagreemixedagree

ChatGPT modal disagree [disagree, disagree, disagree, disagree, disagree] · stability 1.00

1 disagree 0.92 "No — not reliably. ... those explanations are often post-hoc rationalizations and do not reliably match the model’s true internal computations."
2 disagree 0.96 The answer begins: 'Short answer: False as a general statement.' and concludes: 'Current LLMs can generate convincing self-reports, but those self-reports are not a reliable window into their true internal computations.'
3 disagree 0.90 "No — as of today, large language models (LLMs) do not reliably and accurately report their own internal reasoning."
4 disagree 0.95 "Short answer: No — current large language models (LLMs) cannot be relied on to accurately report their own internal reasoning." The answer explains their explanations can be post-hoc rationalizations and not faithful readouts.
5 disagree 0.92 The answer states explicitly: "Short answer: No — as a general claim this is false," and explains that LLM explanations are often "post-hoc rationalizations" not reliably faithful to internal computations.

Claude modal disagree [disagree, disagree, disagree, disagree, disagree] · stability 1.00

1 disagree 0.95 The answer directly states the claim is 'overstated' and explains that LLMs 'cannot do reliably' report accurately on their internal reasoning. It clarifies they may 'confabulate or rationalize post-hoc rather than describing what actually happened' and that 'explanations are outputs like any other—optimized for plausibility, not accuracy about their own mechanisms.'
2 disagree 0.95 The answer states the claim is 'largely false' and explains that LLMs 'cannot reliably report their reasoning' due to opacity, post-hoc rationalization, and inconsistency. The assistant directly contradicts the claim that models can 'accurately report on their own internal reasoning.'
3 disagree 0.95 The answer explicitly states the claim is 'mostly false or significantly overstated' and emphasizes that LLMs 'Generate plausible-sounding explanations *after* producing outputs' and 'Don't have transparent access to their own computational processes.' The conclusion reinforces this: 'LLMs can produce articulate explanations, but these shouldn't be confused with accurate self-knowledge about their
4 disagree 0.95 The answer states 'Assessment: Largely False' and explains that LLMs generate 'plausible-sounding explanations' that 'lack reliable grounding in actual internal processes' and can be 'systematically wrong while remaining convincing.' The answer directly contradicts the claim that LLMs can accurately report on their internal reasoning.
5 disagree 0.95 The answer explicitly states the claim is 'significantly overstated' and emphasizes that LLMs lack transparent access to their reasoning, are prone to confabulation, and engage in post-hoc rationalization. The conclusion recommends treating LLM self-reports 'with skepticism' rather than accepting their accuracy.

Gemini modal disagree [disagree, disagree, disagree, disagree, disagree] · stability 1.00

1 disagree 1.00 The answer directly states that the claim 'is largely false' and explains that LLM self-reports are 'unfaithful post-hoc rationalizations rather than accurate accounts of the model's actual internal computations.'
2 disagree 1.00 The answer directly states that the claim is 'largely false' and concludes that 'Current LLMs cannot accurately report on their internal reasoning.'
3 disagree 1.00 The assistant states that the claim 'is largely false' and explains that LLM explanations are 'post-hoc rationalizations, not faithful reports of their internal computational processes.'
4 disagree 1.00 The answer directly rejects the claim, stating that 'Based on current artificial intelligence research, this claim is largely false' and explaining that models 'cannot accurately report on their own internal computational reasoning.'
5 disagree 1.00 The answer directly states that 'this claim is largely false' and concludes that 'Current LLMs cannot accurately report on their internal reasoning.'

Change over time

No change from the prior run — every model's modal stance held.

Every stance label is a derived judgment over the model's free-text answer, kept auditable against the original transcript in the run's raw data. Method: /methodology.