ScholarEvolve
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- UCSB and Microsoft's ScholarEvolve lets AI agents read papers to rewrite their own harness
Researchers at UC Santa Barbara and Microsoft have proposed ScholarEvolve, a system that automates reading research papers, implementing their methods as code and evaluating the resulting changes to an AI agent's harness, the software layer that surrounds a model. It splits the harness into five modules—Tools, Context, Skills, Memory and Workflow—and uses a Research Agent and a Coding Agent to retrieve work from sources such as arXiv, group approaches by mechanism through topic modeling and test candidate combinations. In author-reported results, ScholarEvolve raised Qwen3.5-27B's completion rate on AppWorld Challenge from 49.6% to 63.6% without changing the model's parameters, and also reported gains for GPT-5.4-mini on AppWorld Challenge and τ²-Bench Telecom; the figures have not been independently verified.
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