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arXiv · 2602.00628

From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs

Louis Schiekiera·Max Zimmer·Christophe Roux·Sebastian Pokutta·Fritz Günther
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Abstract

We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight instruction-tuned transformer models, we run two experimental paradigms -- similarity-based forced choice and free association -- over a shared 5,000-word vocabulary, collecting 17.5M+ trials to build behavior-based similarity matrices. Using representational similarity analysis, we compare behavioral geometries to layerwise hidden-state similarity and benchmark against FastText, BERT, and cross-model consensus. We find that forced-choice behavior aligns substantially more with hidden-state geometry than free association. In a held-out-words regression, behavioral similarity (especially forced choice) predicts unseen hidden-state similarities beyond lexical baselines and cross-model consensus, indicating that behavior-only measurements retain recoverable information about internal semantic geometry. Finally, we discuss implications for the ability of behavioral tasks to uncover hidden cognitive states.

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Louis Schiekiera, Max Zimmer, Christophe Roux, Sebastian Pokutta, Fritz Günther. 2026-01-31. From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs. https://arxiv.org/abs/2602.00628

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