M$^\star$: Every Task Deserves Its Own Memory Harness
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. Specifically, M$^\star$ models an agent memory system as a memory program written in Python. This program encapsulates the data Schema, the storage Logic, a...
| Authors | Wenbo Pan, Shujie Liu, Xiangyang Zhou et al. |
|---|---|
| Primary category | cs.PL |
| Published | 10 April 2026 |
| Last updated at source | 23 May 2026 |
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