EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery
EvoDuet co-evolves web queries and solutions, raising LLM discovery scores on 21 scientific tasks.
EvoDuet is a bilevel method that co-evolves scientific-discovery solutions and web-search queries while keeping model weights fixed. A retrieval gate decides whether to fetch new documents, reuse stored ones, or proceed without retrieval, and an inner loop ranks documents by predicted solution score. Across 21 tasks with one candidate per iteration, it lifts OpenEvolve's normalized discovery gain from 74.1% to 78.0% with GPT-5.6-Luna and from 61.3% to 82.3% with Gemini-3.8-Flash, while Qwen3.5-9B does not improve. Best runs exceed previously reported scores on eight tasks and also help other evolutionary scaffolds.
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