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CoMem: Learning to Anticipate Failures from Retrieved Experience

Yun Shen, Ming Shen, Tianjun Yao · Oct 19, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 35% Sparse protocol signal Freshness: Hot Status: Ready
MathCoding
  • Reflection lets LLM agents improve from their own attempts, but the memory it builds is local to a single task, so an agent reflecting on its own tends to revisit the same explanation for failure.
  • When both are used, existing agents combine them additively, feeding their outputs to the solver side by side, so retrieved experience never changes what the learned memory produces.
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