Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
arXiv:2605.30621v1 Announce Type: new Abstract: LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task execution without changing model parameters. Harness self-evolution adapts such agents by updating these harnesses from execution evidence. Yet it remains unclear whether a model's base capability in task-solving predicts its capabilities in harness self-evolution: which models produce useful harness updates, and which actually benefit from them? We analyze two harness self-evolution capabilities
The rapid advancement and deployment of LLM agents in various applications necessitate a deeper understanding of their self-evolution capabilities and limitations.
This research provides critical insights into how self-evolving AI agents learn and adapt, which is fundamental for developing robust and economically impactful autonomous systems.
Our understanding of what drives effective AI agent evolution is refined, allowing for more targeted development of AI that truly benefits from self-improvement rather than just 'updating'.
- · AI development platforms
- · Companies deploying LLM agents
- · AI researchers
- · Inefficient AI agent developers
- · Companies with poorly designed AI implementations
Improved performance and reliability of autonomous AI agents across various tasks.
Accelerated adoption of AI agents in white-collar workflows, leading to increased automation efficiencies.
Enhanced economic productivity and potentially a redefinition of work as AI agents assume more complex, self-optimizing roles.
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Read at arXiv cs.AI