EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation

Excerpt

arXiv:2605.07247v2 Announce Type: replace Abstract: Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments are expensive to build, brittle to extend, and fundamentally limited in diversity. A promising direction is to replace manually crafted environments with LLM-simulated counterparts. However, this paradigm hinges on an unexamined core assumption: LLMs can accurately simulate environmental feedba