When Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers
arXiv:2607.00394v1 Announce Type: cross Abstract: 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 underperfor
The increasing reliance of Large Language Model (LLM) agents on retrieval buffers for experience reuse makes efficient cache management critical, highlighting a current bottleneck in 'ad-hoc' policies.
Improved semantic retrieval buffer management can significantly enhance the efficiency, performance, and scalability of AI agents, directly impacting their deployment and capabilities.
A formalization of the semantic cache replacement problem and the identification of shortcomings in classic heuristics will drive the development of more sophisticated, learning-augmented cache policies for AI agents.
- · AI agent developers
- · Cloud providers (for efficiency gains)
- · AI-powered applications
- · Systems relying on inefficient cache policies
- · Developers slow to adopt advanced caching
More robust and efficient AI agents will emerge, capable of handling complex tasks with better memory management.
This efficiency gain could lead to a reduction in the computational resources required for certain AI agent operations, lowering operational costs.
Enhanced agent performance and reduced costs could accelerate the widespread adoption of AI agents across various industries, creating new market opportunities and disrupting existing workflows.
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Read at arXiv cs.CL