AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research
New benchmark pushes coding agents beyond engineering tasks into open-ended scientific discovery.
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 whi...
ArXiv CS.AI
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AutoWorldModel-Bench shifts the evaluation of coding agents from executing predefined tasks to conducting autonomous, creative scientific research. By automating the iterative development of world models, this benchmark provides a critical framework for testing the high-level reasoning capabilities required for future AI scientists.
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