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arXiv · 2610.23157

Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models

Hikaru Asano·Hidezo Suganuma·Ryutaro Mori·Yukino Baba
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Abstract

Large language models (LLMs) are increasingly used as computational models of human behavior. Recent models such as Centaur have advanced this goal by fine-tuning LLMs on trial-by-trial decisions from psychological experiments. However, existing large-scale behavioral datasets do not link the same participants across different tasks, so these models learn population-level regularities but are not trained to model individual-level regularities across tasks. We introduce Cross-Psych-101, a dataset of 591 participants and 261{,}551 valid decisions across 12 Bandit, Tradeoff, Structure, and Social tasks. We train Cross-Centaur to predict a participant's target-task actions from a target-task description and the source-task description paired with the same participant's source-task behavior. Cross-Centaur outperforms off-the-shelf LLMs and Centaur on held-out participants, both within and across tasks. Probing analyses suggest that its hidden states encode participant-level behavioral tendencies that transfer across tasks. These results highlight the potential of cross-task individual-level data for predicting human decision-making with language models. Cross-Pysch-101 is available at https://huggingface.co/datasets/HikaruAsano/cross-psych-101.

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Hikaru Asano, Hidezo Suganuma, Ryutaro Mori, Yukino Baba. 2026-10-16. Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models. https://arxiv.org/abs/2610.23157

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