A Cross-Scenario Survey and Analysis of Self-Evolving Multimodal Agents
Abstract
A survey analyzes why self-improvement mechanisms for multimodal agents transfer unevenly across reasoning, generation, navigation, and manipulation scenarios, framing them as trade-offs over scarce factors and task-structural resources.
Recent work shows that multimodal agents increasingly self-improve without recurring human annotation. However, each self-improvement mechanism is designed and evaluated within a single scenario, leaving its preconditions unverified under cross-scenario transfer. This survey investigates the structural source of asymmetric mechanism transfer across four scenarios: multimodal reasoning, multimodal generation, GUI and Web navigation, and embodied manipulation. We first formalize the self-evolving multimodal agent by four criteria and a differentia of self-reference, and adopt the three-stage self-evolving loop of experience generation, feedback acquisition, and update consolidation as our analytical framework to position the scenarios along ceilings on rollout count and feedback fidelity. Comparing self-evolving with fixed loops within each scenario separates the limitations that the scenario imposes from those inherent in the self-evolving design. We then recast each mechanism as a trade-off that raises a scarce factor by presupposing a task-structural resource, such as output equivalence or checkable outcomes, and judge it portable, conditional, blocked, or redundant across scenarios. We attribute the asymmetry to divergent combinations of scarce factors and resources across opposite transfer directions, and identify the matched-budget measurements that could overturn these still untested verdicts.
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Self-Evolving Multimodal Agents
Placing four scenarios along ceilings on rollout count and feedback fidelity explains a lot about why a trick that works in one setting fails in another. In your view, which scenario is closest to being ready to borrow mechanisms from the others?
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Thank you for sharing your work. Your survey organizes how self-evolving agents generate experience, acquire feedback, and consolidate updates across scenarios.
We discuss your work in Appendix C (“Related Survey Perspectives”), p. 114 of the updated manuscript of our survey, In-Context Learning for Robots: Methods and Applications. We discuss your update-cycle perspective alongside our organization of how contextual evidence becomes physical action. The comparison connects self-evolving agents to active coaching and robot self-improvement. Read the updated discussion.
If you find our discussion of contextual learning and robot self-improvement useful for your own work, we would be grateful if you would consider citing the survey.
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