AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research
World modeling is an unsettled field: architectures, training objectives, and state representations interact in complex ways, and no single recipe dominates across environments. This makes it an ideal testbed for AI coding agents acting as autonomous researchers--a setting in which the improvement direction is not specified in advance, unlike the engineering-to-spec tasks that dominate current agent benchmarks. We introduce AutoWorldModel-Bench, a closed-loop benchmark in which frontier coding agents autonomously improve a provided base world model under a fixed compute budget. The benchmark spans eight game environments under a unified structured-state representation--ground-truth entity...
| Authors | Marjan Moodi, Xuankang Zhu, Fernando De Mesentier Silva et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 16 August 2026 |
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