New benchmark reveals perception, spatiotemporal modeling as MLLM weaknesses

Researchers have introduced BEAR, a new benchmark designed to evaluate and diagnose the skill-level capabilities of embodied multimodal large language models (MLLMs). This benchmark decomposes embodied tasks into 14 distinct atomic skills, providing more granular insights into model failures than previous task-level evaluations. Evaluations on BEAR revealed that perceptual limitations and unstable spatiotemporal modeling are significant bottlenecks for current MLLMs. To address these issues, the team developed BEAR-Agent, a conversational agent that enhances MLLMs with visual and spatial reasoning tools, demonstrating substantial performance improvements on the benchmark and in robotic experiments. AI

IMPACT Identifies key weaknesses in embodied AI, guiding future research towards improved perception and spatiotemporal reasoning for robotic agents.

RANK_REASON The cluster contains an academic paper introducing a new benchmark and evaluation framework for multimodal language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• BEAR
• BEAR-Agent
• GPT-5
• MLLMs

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