AI RESEARCH
HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns
arXiv CS.CL
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ArXi:2601.10198v3 Announce Type: replace Large Language Models (LLMs) have nstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs). However, achieving authentic alignment with human cognitive and behavioral patterns remains a critical challenge for these agents. We present HumanLLM, a framework treating psychological patterns as interacting causal forces.