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SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents
Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorithm discovery, yet existing systems are largely memoryless: each run explores from scratch, so agents repeatedly rediscover the same improvements and re-encounter the same dead ends. We intro
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