Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Self-evolving runtime harnesses can substantially improve the capabilities of large language model (LLM) agents and provide a promising paradigm for optimizing agent execution. Existing failure-driven approaches often treat observed agent failures as direct evidence for harness modification. A key challenge in failure-driven harness evolution is that observed failures can reflect either limitations of the underlying model or systematic deficiencies of the harness. Directly optimizing against individual failures can therefore induce model-specific accommodation and impair generalization across tasks and models. We study whether failure evidence accumulated across task instances can provide...
| Authors | Ruiqing Yue, Yu Cui, Zhuoyu Sun et al. |
|---|---|
| Primary category | cs.SE |
| Published | 10 September 2026 |
| Last updated at source | 20 September 2026 |
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