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

2026-11-11 15:45:01
Okan S. Coskun, Florian Rottach, Carsten Eickhoff, William Rudman
arXiv_AI
     
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.

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URL

https://arxiv.org/abs/2608.00828

PDF

https://arxiv.org/pdf/2608.00828.pdf

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