EvoMem: Memory-Augmented Evolution for Code Optimization
Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks and domains. However, existing LLM-driven evolutionary frameworks largely discard such knowledge, repeatedly rediscovering similar ideas and limiting opportunities for cross-run and cross-task learning. We introduce EvoMem, a persistent memory architecture for LLM-based evolutionary program search that captures and reuses candidate mutation knowledge. EvoMem converts successful mutation events into structured, task-aware advice for future runs. It operates in two phases: after each run, it extracts and stores prom...
| Authors | Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin et al. |
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| Primary category | cs.AI |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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