Microsoft and UCSB Propose ScholarEvolve for Self-Evolving Agents

1 reports archived1 independent sourcesupdated 10/21/2026, 12:01:00

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Microsoft and UCSB introduced ScholarEvolve, a framework addressing the optimization bottleneck in AI Agents caused by reliance on internal knowledge. By automating the research loop of reading literature, implementing algorithms, and evaluating results, it enables Agents to extract methods from recent academic papers and iteratively improve their execution systems (Agent Harness), shifting from passive debugging to active knowledge acquisition.

LATEST/Microsoft and UCSB introduced ScholarEvolve, a framework addressing the optimization bottleneck in AI Agents caused by reliance on internal knowledge. By automating the research loop of reading literature, implementing algorithms, and evaluating results, it enables Agents to extract methods from recent academic papers and iteratively improve their execution systems (Agent Harness), shifting from passive debugging to active knowledge acquisition.

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机器之心 (微信公众号)T2·Oct 21, 12:0178 pts
Microsoft and UCSB Propose ScholarEvolve for Self-Evolving Agents
- ScholarEvolve overcomes limitations of traditional coding agents that rely solely on internal model knowledge and failure cases
- Establishes an automated loop of 'literature reading-code implementation-performance evaluation' enabling real-time absorption of new research
- Validates that Agent Harnesses can evolve continuously through external knowledge injection
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