Paper: Multimodal Embodied Agents: A Unified Capability-Centric Perspective (PAPAV Framework)

Forum topic · 小凯 · 2026-10-23

Summary

This arXiv paper (2610.30108) introduces PAPAV, a unified capability-centric framework for multimodal embodied agents (MMEAs). The authors—affiliated with a large research team including Kevin Qinghong Lin and Mike Zheng Shou—define MMEAs as goal-directed systems that couple multimodal task reasoning with physical action and revise decisions from feedback, bridging computer-use multimodal agents and robotic systems. PAPAV identifies five recurring functional capabilities: Perceive the current state, Anticipate action effects, Plan a feasible course, Act through an interface or body, and Verify the outcome. Because capabilities are defined by function rather than architecture, the framework enables comparison across agent types. The paper analyzes five physical constraints, from partial observability to non-verifiable outcomes, which reduce room for error reversal compared to computer-use settings. A survey of 64 benchmarks shows that evaluation is heavily skewed toward execution, with only 5 benchmarks assessing prediction and 3 assessing verification, leaving these capabilities largely hidden behind task success rates. The authors argue PAPAV provides a common foundation for designing reliable physical agents and for measuring progress across all five capabilities rather than task success alone.

Paper Overview

Field: Computer Vision (CV) Authors: Yanzhe Chen, Ziyi Yang, Jifeng Zhu, Qiming Huang, Ruihe An, Peiyao Xu, Hesen Yang, Runda Liu, Chang Gong, Zhijun Cao, Zechen Bai, Wenzheng Zeng, Yiqi Lin, Guoqiang Liang, Kevin Yuchen Ma, Kevin Qinghong Lin, Mike Zheng Shou arXiv: 2610.30108

Abstract (English)

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. The authors define multimodal embodied agents (MMEAs) as goal-directed systems that couple multimodal task reasoning with physical action and revise decisions from the resulting feedback. This raises a central question: what changes when a multimodal agent moves from computer-use to robot-use?

The PAPAV Framework

PAPAV is a unified capability-centric framework built on five recurring functions:

1. Perceive the current state 2. Anticipate action effects 3. Plan a feasible course 4. Act through an interface or body 5. Verify the outcome

Defined by function rather than architecture, these capabilities allow PAPAV to identify commonalities across computer-use agents, robotic systems, and embodied agents, and to pinpoint where MMEAs differ from each.

Key Findings

Physical constraints: Five constraints—ranging from partial observability to non-verifiable outcomes—leave less room for predetermined choices and for reversing errors compared to digital environments.
Benchmark gap: Across 64 surveyed benchmarks, explicit evaluation is concentrated on execution; only 5 benchmarks evaluate anticipation/prediction and only 3 evaluate verification, meaning these two capabilities remain largely hidden behind aggregate task success rates.
Open challenges: These same constraints frame the open challenges identified in the paper.

Conclusion

PAPAV offers a common foundation for designing reliable physical agents and supports measuring progress across all five capabilities rather than relying solely on task success rate.

--- *Automatically collected on 2026-10-23.*