SocialAgent: Second Workshop on Large Language Models for Social Reasoning and Simulation

Large Language Models (LLMs) are increasingly used not as analytic tools but as interactive social agents that make decisions, negotiate norms, and coordinate with one another. Generative-agent frameworks sustain role-consistent behavior and emergent coordination; LLM populations produce convention formation, polarization, and bias amplification; and models now stand in for human participants at scale. Yet these systems also encode strong normative assumptions and amplified biases in sensitive domains, revealing both the power and the fragility of treating LLMs as social simulators. The 2nd Workshop on Social LLMs (SocialAgent@NeurIPS 2026) convenes the ML community around the core technical questions this raises: how to model, evaluate, and align LLMs that reason about and simulate human social behavior. The first edition (SocialLLM@ICWSM 2026) targeted computational social science; this NeurIPS edition recenters on methods, evaluation, and alignment, organized around three themes: (i) social reasoning and cognition---theory of mind, moral judgment, and relational reasoning, and when these are genuine versus prompting artifacts; (ii) social simulation and its validity---LLMs as proxies for individuals and populations, with attendant threats from causal-inference pitfalls, simulation boundaries, persona drift, and validation standards from agent-based modeling; and (iii) pluralistic alignment and evaluation---whose values a model represents, how to measure contested values, and how emergent norms and biases in multi-agent settings should be read.