BEAKER benchmark evaluates embodied MLLMs in chemical labs
Original title: BEAKER: An Expert-Curated Benchmark for Embodied Brains in Self-Driving Chemical Laboratories
BEAKER reveals gaps in action-grounded perception for self-driving chemical laboratories using 1,000 expert-annotated samples.
BEAKER contains 1,000 expert-curated samples across 11 image and video task types for SDCLs. It covers affordance grounding, trajectory planning, equipment recognition and safety norms. Current MLLMs struggle with fine-grained perception and step ordering despite following coarse procedures. The benchmark provides a diagnostic for embodied AI in chemical experimentation.
- 1,000 expert-curated samples
- 11 image and video task types
- Offline single-turn evaluation
- 28 MLLMs tested
- Manual review by chemistry experts
From the original article
1 min read at arxiv.orgSelf-Driving Chemical Laboratories (SDCLs) are moving chemical experimentation toward embodied automation, where machines must perceive laboratory scenes, reason about experimental states, and act under physical, procedural, and safety constraints.
By Fei Lin · arxiv.org
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