GEAR: Genetic AutoResearch for Agentic Code Evolution
Autonomous research agents can already run machine learning experiments without human supervision, but many rely on a narrow search strategy: they repeatedly modify one program and keep changes only when they improve the current best result. This can cause them to discard useful partial ideas, alternative promising directions, and insights from failed or incomplete experiments. GEAR, or Genetic AutoResearch, replaces this single-path search with a population-based search over multiple research states. It keeps a set of strong candidate solutions, selects parents based on productivity, novelty, and coverage, and explores new ideas through mutation and crossover. Each research state stores ...
| Authors | Ahmadreza Jeddi, Minh Ngoc Le, Hakki C. Karaimer et al. |
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| Primary category | cs.NE |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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