DarwinX: Evolving Agent Harnesses Through Natural Selection
An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow. Self-improvement loops already edit harnesses, yet single-lineage search is path-dependent and local wins often regress other tasks. We introduce DarwinX, which treats self-evolution as selection over a population of harnesses with the model frozen: a preserve-and-extend contract admits only variants that extend coverage without regressing, an archive keeps alternative lineages for recombination, and failure-, teacher-, and self-derived evidence share one edit interface. Fitness comes from each benchmark's own verifier: no gold solutions, no hand-picked winners. Across...
| Authors | Yifan Zhang, Yutong Dai, Juntao Tan et al. |
|---|---|
| Primary category | cs.NE |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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