Great paper on self-evolving agent harnesses.
Self-evolving agent harnesses have two practical problems:
- Search is slow, because every candidate harness needs repeated agent runs and code edits.
- Fixes overfit, because each failure is patched as if it were a harness bug, even when the model caused it.
Ecdysis analyzes failures across a batch of tasks and repairs only patterns that recur.
Several diagnostic roles agree on a change specification before any code is modified.
Harness training runs up to 1.84x faster than existing harness evolution methods, and the resulting harnesses gain 18.56% in reasoning accuracy. They also transfer better across LLMs, use fewer tokens, and match full-data training with a quarter of the data.
Paper: https://arxiv.org/abs/2609.11677
Chat with Paper: https://academy.dair.ai/papers/ecdysis-efficient-and-effective-training-of-runtime-harnesses-for-llm-agents-2609.11677