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 similarity and hit quality is continuous rather than binary. Through experiments on two datasets from MemoryBench-Full (LoCoMo, DialSim) with 8 replacement policies, we reveal a surprising finding: classic heuristics (LRU, LFU) \emph{consistently underperform} the naive FIFO baseline on semantic workloads, due to the absence of temporal locality and frequency concentration. We propose SOLAR, a learning...
| Authors | Yushi Sun, Bowen Cao, Wai Lam |
|---|---|
| Primary category | cs.DB |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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