Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Models

Abstract:We investigate the geometry of decision-making in Multiple Choice Question Answering (MCQA) through the lens of isotropy. Analyzing five open-weight models across diverse datasets, we identify decision-critical transition layers characterized by a shift in isotropy, coinciding with a major representational change and the emergence of task-relevant clusters. We demonstrate that this synchronized geometric behavior is strongly correlated with downstream accuracy ($r\approx0.84$), displaying its relevance for successful decision-making. Furthermore, we show that this transition is robust to prompt variations, suggesting that it reflects a general mechanism of model behavior.

Artificial Intelligence (cs.AI)

arXiv:2608.00828 [cs.AI]

(or arXiv:2608.00828v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.00828 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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