RealUserSim: Bridging the Reality Gap in Agent Benchmarking via Grounded User Simulation
LLM-based user simulation is the primary mechanism for end-to-end agent evaluation, yet simulated users are poor proxies for real humans: unconstrained LLM defaults produce a Formalism Ceiling (style match rates of 6-8% against real users), while hand-crafted behavioral directives trigger Directive Amplification, where models hyper-interpret instructions into unnatural behavioral extremes that vary dramatically across simulator models. We present RealUserSim, the first user simulation framework grounded in real behavioral data. From 14,000+ authentic human-LLM conversations (WildChat), we extract 7,275 executable behavioral profiles and use them to ground LLM simulators. A fidelity benchm...
| Authors | Ming Zhu, Juntao Tan, Rithesh Murthy et al. |
|---|---|
| Primary category | cs.HC |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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