BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry
arXiv:2603.05016v1 Announce Type: new Abstract: Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language …
Zuo Fei, Kezhi Wang, Xiaomin Chen, Yizhou Huang
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