CacheWeaver: Cache-Aware Evidence Ordering for Efficient Grounded RAG Inference
On the map: Retrieval-augmented generation → Generation and context integration
Abstract (arXiv)
Retrieval-Augmented Generation (RAG) improves factual grounding, but it also lengthens prompts and raises prefill cost. Prefix caching in serving engines such as vLLM reduces this cost only when requests share the same token prefix. In grounded generation, however, adjacent queries may retrieve overlapping evidence in different orders, so set overlap does not become reusable prefix overlap. We present CacheWeaver, a lightweight prompt-layer method for cache-aware evidence ordering. The method keeps a prefix tree over recently served evidence sequences and uses a greedy walk to place the most reusable prefix first, while leaving the serving engine and retrieved evidence set unchanged. Across three vLLM configurations, the method lowers median time-to-first-token (TTFT) by about 20-33 percent relative to retrieval-order prefix caching, without hurting answer quality in our QA tests. The greedy policy reaches 97.5 percent of the median TTFT gain from oracle ordering, indicating that most reusable prefix locality can be recovered by a simple scheduling layer between retrieval and inference.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 10 of its 16 references that are in the map. First posted on arXiv 18 Jun 2026.
Builds on
10 of 16 references.
Closest ancestors · 2
Same direction, newest first
- 2024TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text10 Oct
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May
Other references · 8
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
- 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,141
- 2024CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusioncited by 223
- 2023CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Servingcited by 203
- 2025ContextPilot: Fast Long-Context Inference via Context Reusecited by 8
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Prefix Sharing Is a Sorting Problemcited by 0
- 2026Prefix Sharing Is a Sorting Problem12 Sep 2026
Similar, not cited
Close in wording, no citation either way
- 2026Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer?
- 2026From Prefix Cache to Fusion RAG Cache: Accelerating LLM Inference in Retrieval-Augmented Generation
- 2025CacheClip: Accelerating RAG with Effective KV Cache Reuse
- 2026ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation
- 2026QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving
- 2026OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation
- 2025Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding
- 2026Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting