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

• URL: http://arxiv.org/abs/2608.11216v1
• Date: Mon, 20 Jul 2026 23:35:45 GMT
• Title: AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research
• Abstract summary: AutoWorldModel-Bench is a benchmark for AI coding agents acting as autonomous researchers.<n>It spans eight game environments under a unified structured-state representation--ground-truth entity state extracted from each game.<n>Our benchmark offers a setting in which frontier coding agents can be evaluated on open-ended research rather than engineering-to-spec problems.
• Score: 39.68061830142623
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 world-model starter 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 starter on 63; in 91% of sessions the winning edit is a non-trivial research-style modification--a new objective, representation, rollout procedure, or architectural change--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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