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arXiv · 2511.18685

Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents

Dayong Liu·Chao Xu·Weihong Chen·Suyu Zhang·Juncheng Wang·Jiankang Deng·Baigui Sun·Yang Liu
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Abstract

Multimodal Large Language Models (MLLMs) show promising results as decision-making engines for embodied agents operating in complex, physical environments. However, existing benchmarks often prioritize high-level planning or spatial reasoning, leaving the fine-grained action intelligence required for embodied physical interaction underexplored. To address this gap, we introduce CFG-Bench, a new benchmark designed to systematically evaluate this crucial capability. CFG-Bench consists of 1,368 curated videos paired with 19,562 question-answer pairs spanning three evaluation paradigms targeting four cognitive abilities: 1) Physical Interaction, 2) Temporal-Causal Relation, 3) Intentional Understanding, and 4) Evaluative Judgment. Together, these dimensions provide a systematic framework for assessing a model's ability to translate visual observations into actionable knowledge, moving beyond mere surface-level recognition. Our comprehensive evaluation on CFG-Bench reveals that leading MLLMs struggle to produce detailed instructions for physical interactions and exhibit profound limitations in the higher-order reasoning of intention and evaluation. Moreover, supervised fine-tuning (SFT) on our data demonstrates that teaching an MLLMs to articulate fine-grained actions directly translates to significant performance gains on established embodied benchmarks. Our analysis highlights these limitations and offers insights for developing more capable and grounded embodied agents. Project page: https://cfg-bench.github.io/

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Dayong Liu, Chao Xu, Weihong Chen, Suyu Zhang, Juncheng Wang, Jiankang Deng, Baigui Sun, Yang Liu. 2026-07-15. Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents. https://arxiv.org/abs/2511.18685

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