Harnessing Agentic Evolution
- Published
- Source
- arXiv
- Paper number
- 186
- Field
- Agents / Self-Improvement / Reasoning
- arXiv ID
- 2605.13821
Key points
- Existing agentic evolution methods, whether procedural or agent-based, struggle with long-horizon search and often get stuck in local optima or suffer from context drift.
- Procedural evolution is rigid because it relies on fixed rules that do not adapt when the characteristics of the search space change, which limits continuous improvement.
- There is no stable and systematic mechanism for organizing accumulated evidence, such as candidates, feedback, and failures, and then using it to modify the evolution mechanism itself.
- The paper formalizes agentic evolution as an interactive environment in which the accumulated history of all candidates, feedback, traces, and costs forms an observable process-level state.
- It introduces AEVO, a controlled meta-editing framework in which an external meta-agent observes this process-level state and generates 'meta-actions'.
- These meta-actions do not directly generate new candidates; instead, they strategically edit the mechanisms that control how future evolution proceeds, such as procedural rules or the agent operating context.
Paper links
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