PolyWorld: Benchmarking Multimodal Reasoning over Evolving Polymers

Multimodal large language models (MLLMs) have shown strong capabilities in understanding images and solving scientific problems, but most existing benchmarks focus on static observations, property prediction, or knowledge-based reasoning. They rarely test whether models can understand how a physical system changes after an intervention and use this understanding to guide subsequent actions. We introduce PolyWorld, a multimodal benchmark for physical reasoning over evolving polymers. PolyWorld contains 5,530 diverse 3D polymer structures built from 2,333 unique repeat units. We use controlled interventions and molecular dynamics simulations to create 4,093 multimodal questions spanning four capabilities: perception, reasoning, decision, and planning. Our evaluation reveals a substantial gap between current MLLMs and human experts across these tasks. These results show that understanding and acting on evolving physical systems remains challenging for multimodal models.