arXiv:2605.20204v1 Announce Type: cross Abstract: 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+ aut
The rapid advancement of LLM-based agent systems necessitates more robust and realistic evaluation methods to ensure their practical utility and alignment with human behavior.
Accurate user simulation is critical for the development and deployment of reliable AI agents, directly impacting product development cycles, user experience, and the real-world performance of autonomous systems.
The ability to benchmark AI agents against more realistic human behavior, moving beyond the limitations of unconstrained LLM defaults and hyper-interpreted directives, will significantly improve agent reliability and effectiveness.
- · AI agent developers
- · Companies deploying AI agents
- · AI testing and quality assurance platforms
- · Researchers in human-computer interaction
- · Developers relying solely on synthetic, ungrounded user simulations
- · AI products with poor real-world human interaction capabilities
Improved performance and reliability of AI agents in real-world applications.
Faster adoption and integration of AI agents across various industries due to increased trust and effectiveness.
Enhanced automation of complex tasks currently requiring human intervention, leading to significant productivity shifts.
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