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When Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers
LLM agents increasingly rely on retrieval buffers to store and reuse past experience, yet the cache management policies governing these buffers remain largely ad-hoc. We formalize this as an online semantic cache replacement problem with switching costs, where items are matched by embedding similari