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AI Agent Papers·2026
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Beneath the Surface: What We Overlook When We Talk About Agent Evolution

Xiaobo Wang, Zimu Wang +14·University of Science and Technology of China, University of California, Berkeley +3

Examines whether iteratively changing an agent harness produces reliable, efficient, transferable gains. Finds that higher scores can hide task-level regressions and added deployment costs, then introduces EvoAudit to revise the evolution process.

#paper#research#ai-agent#evaluation#benchmarks
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