Evaluates AI agents on 56 research-based physics tasks, from literature review and experimentation to manuscript writing. It combines deterministic checks with expert rubrics to assess both correctness and research quality.
Lets robot manipulation policies draw on visual representations from different depths of a vision-language backbone instead of relying on a separate encoder’s fixed output. The approach improves control results on LIBERO and real-world tasks.