MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation
arXiv:2605.24914v1 Announce Type: cross 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 obje
The rapid adoption and scaling of Large Language Models (LLMs) have made cost and latency significant bottlenecks, driving the immediate need for more efficient operational strategies like advanced caching.
This development addresses critical infrastructure challenges for AI deployment, directly impacting the economic viability and performance of LLM-powered applications for strategical organizations.
Semantic caching for LLMs moves beyond simplistic similarity, enabling more accurate and resource-efficient reuse of cached responses through multi-vector retrieval and learned prompt segmentation.
- · AI application developers
- · Cloud infrastructure providers
- · Companies with high LLM usage
- · LLM service providers
- · Inefficient LLM architectures
- · Basic caching solution providers
Reduced operational costs and improved response times for LLM-based services are immediately realized.
This efficiency could accelerate the development and deployment of more complex and agentic AI systems, broadening their applicability.
Lower compute barriers might lead to saturation in specific AI application markets as more players can afford to run sophisticated models.
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Read at arXiv cs.LG