Learning a Foundation Model of Human Social Reasoning from Heterogeneous Experts

Large language models (LLMs) have demonstrated strong capabilities in symbolic reasoning, tool use, and complex task solving, yet they still lack a fundamental understanding of human social reasoning: how people interpret the same situation, weigh relevant information, and arrive at decisions. This limitation largely stems from the scarcity of explicit decision knowledge in pretraining corpora about how individual information leads to behavioral choices. To recover such knowledge from structured behavioral data, we use heterogeneous experts with complementary inductive biases to uncover relationships between individual information and behavioral choices. Building on these relationships, we introduce SocialReasoner to supplement LLMs with the decision knowledge required for human social reasoning through natural language reasoning supervision. Drawing on four major survey programs, we construct nine large datasets for human social reasoning, comprising 281,995 respondents and over 8.7 million observed responses across 365 questions, 13 social domains, and diverse countries and regions. Across multiple model families and scales, SocialReasoner consistently outperforms all evaluated state-of-the-art baselines, with average relative accuracy gains exceeding 40% and substantially better alignment with observed population behavioral distributions. It further generalizes to an unseen survey and extends to decision tasks in finance and healthcare. Code and models will be made publicly available.