Clear-looking AI explanations may not reveal which reasoning steps actually matter
A preprint compares how important a step looks with how much it changes the model’s chance of reaching a correct answer.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the abstract
What the paper reports
The researchers estimated the contribution of individual reasoning steps and tested whether AI judges could recognise the important ones. They report that stronger judges did better than a simple baseline but remained well below the comparison ceiling.
Why it matters
An explanation that reads convincingly can still provide an incomplete picture of the process behind an answer.
Training a model to assess individual steps improved results for incorrect answers, according to the abstract. Its judgments remained less informative for correct responses.
The authors caution against treating readable reasoning text as a complete explanation of model behaviour.
What this does not tell us
The evidence here is the preprint abstract. The findings concern the study’s tasks and definition of step importance, not every kind of AI explanation.
Original sources · 1
- Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning ↗arXiv · 2026-09-03
Check the original paper for its authors, methods, version and access terms.
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