MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation

• URL: http://arxiv.org/abs/2605.24914v1
• Date: Sun, 24 May 2026 07:33:46 GMT
• Title: MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation
• Abstract summary: We introduce MVR-cache, a novel semantic caching approach that significantly improves retrieval accuracy.<n>MVR-cache is built upon a learnable segmentation model that intelligently prompts splits.<n>We confirm that in comparison to the state-of-the-art, MVR-cache consistently increases the cache hit rates by up to 37%.
• Score: 4.3943116175907315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To reduce LLM costs and latency, semantic caching systems must accurately identify when a new prompt matches a cached one. Current methods often rely on simplistic similarity measures, which limit their effectiveness. We introduce MVR-cache, a novel semantic caching approach that significantly improves retrieval accuracy by integrating Multi-Vector Retrieval (MVR). MVR-cache is built upon a learnable segmentation model that intelligently splits prompts, enabling fine-grained similarity comparisons via MaxSim. We derive the model's training objective from a rigorous theoretical analysis. This can ensure that optimizing this objective directly maximizes cache hits under strict correctness constraints. To solve the resulting non-differentiable combinatorial optimization problem, we leverage a reinforcement learning-based training strategy with the theoretically grounded objectives as the reward. Experimental results on established benchmarks across diverse tasks confirm that in comparison to the state-of-the-art, MVR-cache consistently increases the cache hit rates by up to 37% while maintaining the same correctness guarantees. MVR-cache is available at https://github.com/PKU-SDS-lab/MVR-Cache

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