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Recent architectures typically adopt a fixed memory design tailored to specific domains, such as semantic retrieval for conversations or skills reused for coding. However, a memory system optimized for one purpose frequently fails to transfer to others. To address this limitation, we introduce M$^\\star$, a method that automatically discovers task-optimized memory harnesses through executable program evolution. Specifically, M$^\\star$ models an agent memory system as a memory program written in Python. This program encapsulates the data Schema, the storage Logic, and the agent workflow Instructions. We optimize these components jointly using a reflective code evolution method; this approach employs a population-based search strategy and analyzes evaluation failures to iteratively refine the candidate pro","title":"M$^\\star$: Every Task Deserves Its Own Memory Harness","url":"https://arxiv.org/abs/2604.11811","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.11811v2 Announce Type: replace-cross \nAbstract: Large language model agents rely on specialized memory systems to accumulate and reuse knowledge during extended interactions. Recent architectures typically adopt a fixed memory design tailored to specific domains, such as semantic retrieval for conversations or skills reused for coding. However, a memory system optimized for one purpose frequently fails to transfer to others. To address this limitation, we introduce M$^\\star$, a method that automatically discovers task-optimized memory harnesses through executable program evolution. 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