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arXiv Computation and Language By Amirhossein Abaskohi, Mahdi Mostajabdaveh, Zirui Zhou

SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents

Read the original on arXiv Computation and Language →

SEDIMA is a persistent hierarchical insight memory designed for evolutionary search agents that use large language models. It transforms raw search traces into natural‑language insights, clusters them by semantic similarity, and retrieves relevant guidance to inform future mutations, thereby accumulating transferable knowledge across runs and problems. When added as a drop‑in module, SEDIMA improves average final performance by 5.5% on AlgoTune and 6.6% on ALE‑Bench LITE, and reduces the number of iterations needed to reach baseline‑best performance by 32.3% on OpenEvolve.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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