Evo-Harness: A New Method for Self-Improving LLM Agents
A new research paper on arXiv (2608.15071) introduces Evo-Harness, a framework for online harness learning in large language model (LLM) agents. The approach allows a frozen agent to improve by continuously updating a structured harness across sequential tasks. The core mechanism, context-to-harness skill compilation, distills noisy contexts from one-shot learning opportunities into reusable skills. The paper addresses gaps in prior work by systematically studying self-improvement factors and validating on complex real-world tasks.
Key facts
- Paper arXiv:2608.15071 introduces Evo-Harness.
- Evo-Harness formulates online harness learning.
- A frozen agent improves by updating a structured harness.
- Context-to-harness skill compilation distills noisy contexts.
- The method targets one-shot learning opportunities.
- It addresses gaps in prior self-improvement research.
- The paper validates on complex real-world tasks.
- It systematically studies key self-improvement factors.
Entities
Institutions
- arXiv