OdysSim: Building Foundation Models for Human Behavior Simulation
Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven post-training pulls them toward a homogeneous, overly agreeable assistant register, creating a behavioral Sim2Real gap. We present OdysSim, the largest open systematic investigation of behavioral foundation models, i.e., models trained to simulate human behavior at scale. We propose SOUL, a taxonomy of five capability axes (CONV, SS, COG, ROLE, EVAL) that unifies 62 datasets and 23 benchmark tasks under one framework. Specifically, we curate the OdysSim corpus (21.4M interactions, 10B tokens, retrofitted with back-generated social contexts), construc...
| Authors | Xuhui Zhou, Weiwei Sun, Weihua Du et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 June 2026 |
| Last updated at source | 7 September 2026 |
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