Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations

2026-09-01

AI Summary

研究提出了一种评估基于LLM的人类模拟器在长时间活动模拟中的行为保真度的方法，并通过比较不同条件机制的效果，发现统计先验能较好地接近真实行为模式但会过度碎片化例行程序。

Abstract

As LLM-based human simulators are increasingly used for policy, evaluation, and training, they must faithfully reproduce real behavioral patterns. While prior work has examined behavioral fidelity in survey responses and dialogue, longer-horizon real-world activity remains largely unexplored. We introduce a framework for evaluating behavioral fidelity in long-horizon activity simulations across temporal granularities and levels of analysis. As a case study, we collect a 43-hour multi-camera dataset of in-the-wild office activity and compare trace-derived conditioning mechanisms: persona descriptors, few-shot exemplars, and statistical transition and time-of-day priors. We find that behavioral fidelity is not uniform across metrics: statistical priors bring activity and sequence distributions closest to real behavior, yet over-fragment routines and suppress within-person variability. These findings motivate a more holistic evaluation that spans multiple metrics, temporal granularities, and levels of analysis.

Problem

• Behavioral Fidelity
• Long-Horizon Activity
• Human Simulators

Innovation

• behavioral fidelity
• long-horizon activity simulations
• temporal granularities
• statistical priors
• multi-metric evaluation