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Research questionHow can we tell whether LLM hidden-state geometry reflects reasoning operations rather than lexical or positional cues?Reasoning operations such as problem formulation, goal decomposition, and deduction may appear separable in hidden states simply because they use different words or occur in different positions. The difficulty is determining whether their geometric separation reflects operation-level processing rather than surface structure.
AI
Mechanistic Interpretability
Natural Language Processing
Reasoning
Latest papersRecent research connected to this question, newest first.Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMsThe study examines held-out hidden representations and finds that operation separability peaks in middle layers while remaining unexplained by lexical or positional confounds. It also reports that operation alignment spreads across spans, identical tokens vary with their surrounding operation, and attention-masking interventions indicate that chunk-onset representations depend on preceding reasoning context.research paper · Sep 4, 2026
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