OmniBehavior：基于真实数据的长程跨场景异构行为轨迹 LLM 用户模拟基准
Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces
研究者提出 OmniBehavior，首个完全基于真实世界数据构建的预测式用户模拟基准，将长程、跨场景、异构行为模式整合进统一框架。对 SOTA LLM 的评测显示，模型难以准确预测这类复杂行为，即使扩大上下文窗口性能也趋于停滞。模拟与真实行为对比还揭示结构性偏差：LLM 倾向收敛为“积极平均人”，表现出超活跃、人格同质化和乌托邦偏差，导致个体差异与长尾行为丢失。
Authors:Jiawei Chen, Ruoxi Xu, Boxi Cao, Ruotong Pan, Yunfei Zhang, Yifei Hu, Yong Du, Tingting Gao, Han Li, Yaojie Lu, Yingfei Sun, Xianpei Han, Le Sun, Xiangyu Wu, Hongyu Lin
Abstract:The emergence of Large Language Models (LLMs) has illuminated the potential for a general-purpose user simulator. However, existing benchmarks remain constrained to isolated scenarios, narrow action spaces, or synthetic data, failing to capture the holistic nature of authentic human behavior. To bridge this gap, we introduce OmniBehavior, the first predictive user simulation benchmark constructed entirely from real-world data, integrating long-horizon, cross-scenario, and heterogeneous behavioral patterns into a unified framework. Based on this benchmark, we first provide empirical evidence that previous datasets with isolated scenarios suffer from tunnel vision, whereas real-world decision-making relies on long-term, cross-scenario causal chains. Extensive evaluations of state-of-the-art LLMs reveal that current models struggle to accurately predict these complex behaviors, with performance plateauing even as context windows expand. Crucially, a systematic comparison between simulated and authentic behaviors uncovers a fundamental structural bias: LLMs tend to converge towards a positive average person, exhibiting hyper-activity, persona homogenization, and a utopian bias. This results in the loss of individual differences and long-tail behaviors, highlighting critical directions for future high-fidelity simulation research.
| Comments: | Accepted by NeurIPS 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.08362 [cs.CL] |
| (or arXiv:2604.08362v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.08362 arXiv-issued DOI via DataCite |
Submission history
From: Jiawei Chen [view email]
[v1]
Thu, 9 Apr 2026 15:26:21 UTC (3,581 KB)
[v2]
Thu, 21 May 2026 16:20:11 UTC (3,540 KB)
[v3]
Sat, 24 Oct 2026 10:11:59 UTC (3,540 KB)
来源：arXiv cs.CL · arxiv.org