Agents
Great paper on self-evolving agent harnesses. Self-evolving agent harnesses have two practical problems: 1. Search is slow, because every ca…
Great paper on self-evolving agent harnesses. Self-evolving agent harnesses have two practical problems: 1. Search is slow, because every candidate harness needs repeated agent runs and code edits. 2.
Great paper on self-evolving agent harnesses. Self-evolving agent harnesses have two practical problems: 1. Search is slow, because every candidate harness needs repeated agent runs and code edits. 2. 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
Related
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Source: DAIR.AI (X) | 2026-09-11