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cs.AI2026
BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry
Zuo Fei, Kezhi Wang, Xiaomin Chen +1
Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language mode…
cs.AI2026
Agentic AI Empowered Intent-Based Networking for 6G
Genze Jiang, Kezhi Wang, Xiaomin Chen +1
The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executabl…