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arXiv · 2608.11216

AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research

Marjan Moodi·Xuankang Zhu·Fernando De Mesentier Silva·Harold Chaput·Mohammad Reza Taesiri
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Abstract

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 state extracted from each game and consumed through a shared tensor format--which isolates dynamics modeling from perception and enables minutes-per-run iteration. Across 64 sessions, Codex-5.4 and Claude Opus 4.6 improve their base on a held-out test split in all but one session, with about half (33 of 64) a substantial gain ($\Delta \geq +0.10$) and the remaining improvements smaller but positive; in 91% of sessions the winning edit is a substantive change to the model or training rather than a hyperparameter tweak. Our benchmark offers a setting in which frontier coding agents can be evaluated on open-ended research rather than engineering-to-spec problems.

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Marjan Moodi, Xuankang Zhu, Fernando De Mesentier Silva, Harold Chaput, Mohammad Reza Taesiri. 2026-07-20. AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research. https://arxiv.org/abs/2608.11216

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