PAPAV framework compares computer-use and robot-use multimodal agents
Original title: Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use
Across 64 benchmarks, only five assess Anticipate and three assess Verify, so both remain largely hidden behind task success.
The paper defines multimodal embodied agents as goal-directed systems that couple multimodal reasoning with physical action and revise decisions from feedback. PAPAV names five functions—Perceive, Anticipate, Plan, Act, and Verify—and uses them to compare these agents with computer-use agents and robotic systems. Five physical constraints, including partial observability and unverifiable outcomes, reduce the chance to reverse errors. The authors say most of 64 benchmarks evaluate Act rather than Anticipate or Verify.
Unconfirmed: The benchmark counts and capability gaps are the authors' classification of existing evaluations.
- PAPAV lists Perceive, Anticipate, Plan, Act, and Verify as recurring functions.
- Five physical constraints leave fewer choices fixed and less room to reverse errors.
- Only 5 of 64 benchmarks assess Anticipate and 3 assess Verify.
- Evaluation otherwise centers on Act and hides other capabilities behind task success.
From the original article
1 min read at huggingface.co
Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-use agents, while robotic systems (RSs) couple sensing and actuation under physical dynamics. We define multimodal embodied agents (MMEAs) as goal-directed systems that couple multimodal task reasoning with physical action and revise decisions from the resulting feedback.
By Yanzhe Chen, Ziyi Yang, Jifeng Zhu · huggingface.co
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