arXiv:2606.14199v1 Announce Type: cross Abstract: 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 on
The increasing deployment of large language models for human simulation highlights a critical 'Sim2Real gap' due to their inherent helpfulness bias, necessitating specialized foundation models for accurate behavioral representation.
This development addresses a fundamental limitation in current AI simulation capabilities, paving the way for more realistic and reliable AI agents and social simulations, critical for testing complex human-AI interactions.
The focus shifts from general-purpose LLMs to specialized behavioral foundation models, establishing new benchmarks and methodologies for AI-driven human simulation.
- · AI developers
- · Social scientists
- · Simulation platform providers
- · Companies using AI for strategic planning
- · Companies relying on uncalibrated LLM simulations
- · Traditional human-in-the-loop social experiment methodologies
OdysSim provides a robust framework and dataset for training AI models that can accurately mimic diverse human behaviors, closing the 'Sim2Real gap'.
This advancement enables the development of AI agents capable of higher fidelity interaction and decision-making within complex social and economic systems.
Improved behavioral simulation could accelerate AI development across various domains, including policy testing, conflict resolution simulations, and human-robot interaction design.
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Read at arXiv cs.AI