HumanStudy-Bench: Towards AI Agent Design for Participant Simulation
Quick summary
arXiv:2602.00685v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable and highly sensitive to design choices. Prior evaluations frequently conflate base model capabilities with experimental instantiation, obscuring whether outcomes reflect the model itself or the agent setup. We instead frame $\textbf{participant simulation as an agent-design problem}$ over full experimental protocols, where an agent is defined by a base model and a specification (e.g., participant att
Key takeaways
- arXiv:2602.00685v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable and highly sensitive to design choices.
- Prior evaluations frequently conflate base model capabilities with experimental instantiation, obscuring whether outcomes reflect the model itself or the agent setup.
- We instead frame $\textbf{participant simulation as an agent-design problem}$ over full experimental protocols, where an agent is defined by a base model and a specification (e.g., participant att
Why it matters
“HumanStudy-Bench: Towards AI Agent Design for Participant Simulation” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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