CacheWeaver: Cache-Aware Evidence Ordering for Efficient Grounded RAG Inference
- URL: http://arxiv.org/abs/2606.19667v1
- Date: Thu, 18 Jun 2026 00:38:46 GMT
- Title: CacheWeaver: Cache-Aware Evidence Ordering for Efficient Grounded RAG Inference
- Abstract summary: Prefix caching in serving engines such as vLLM reduces this cost only when requests share the same token prefix.<n>We present CacheWeaver, a lightweight prompt-layer method for cache-aware evidence ordering.
- Score: 4.3361375690943795
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Tail-Replay: Escaping the Curse of Linear Attention in Prefix Caching for Hybrid LLMs [5.5444120195759785]
We present Tail-Replay, a prefix caching mechanism that enables unconstrained token-level prefix reuse.<n>We evaluate Tail-Replay on three Gated DeltaNet-based hybrid models using the LongBench and RULER benchmarks.
arXiv Detail & Related papers (2026-08-31T06:27:07Z) - CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion [21.692254412768936]
Link construction should instead prioritize a sparse set of retriever-complementary associations.<n>We present CABLE, a plug-in augmentation that constructs links to extend the host retriever's direct semantic reach.<n>CABLE yields higher mean LLM-judge scores in every evaluated system-level setting.
arXiv Detail & Related papers (2026-08-18T15:40:29Z) - Back from the Future: Key-Value Cache Management by Counter-Causal Surprise [67.1056509495879]
Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years.<n>We propose a simple yet effective KV eviction scheme motivated by the insight that past tokens which can be well-predicted from more recent tokens are redundant.<n>We evaluate our strategy on various open-source LLMs and benchmark datasets showing competitive or improved performance over other state-of-the-art methods.
arXiv Detail & Related papers (2026-07-30T02:42:51Z) - Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer? [0.0]
We propose GroundedCache, an evidence-validated cache router that admits a cached answer only when 4 cheap gates simultaneously hold.<n>We build a six-regime workload that stress-tests cache safety rather than only hit rate, and introduce an operator-facing metric, the unsafe-served rate (USR)<n>Across 2 datasets and 12,000 real-LLM generations(Qwen2.5-7B-Instruct on vLLM with Automatic Prefix Caching), GroundedCache drives USR to 0.0% on every HotpotQA regime and to 1.5% on mtRAG document drift.
arXiv Detail & Related papers (2026-05-26T16:50:02Z) - A Parametric Memory Head for Continual Generative Retrieval [52.66674234249913]
Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries.<n>We show that sequential adaptation improves retrieval on newly added documents but substantially degrades performance on earlier slices.<n>We propose post-adaptation memory tuning (PAMT), a memory-only stabilization stage that augments an adapted model with a modular parametric memory head.
arXiv Detail & Related papers (2026-04-25T17:38:51Z) - Sparse Prefix Caching for Hybrid and Recurrent LLM Serving [0.7284556903703034]
We formalize sparse prefix caching as checkpoint placement under a distribution over overlap depths.<n>For use cases where requests share a non-trivial prefix, we show that our method consistently improves the frontier traced by standard asymmetry on real-world data.<n>It preserves exact outputs, does not change the recurrent computation itself or require new recurrent update kernels.
arXiv Detail & Related papers (2026-04-17T09:24:58Z) - Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation [54.61034867177997]
Caching inference responses allows them to be retrieved without another forward pass through the Large Language Models.<n>Traditional exact-match caching overlooks the semantic similarity between queries, leading to unnecessary recomputation.<n>We present a principled, learning-based framework for semantic cache eviction under unknown query and cost distributions.
arXiv Detail & Related papers (2025-08-11T06:53:27Z) - vCache: Verified Semantic Prompt Caching [95.16654660556975]
This paper proposes vCache, the first verified semantic cache with user-defined error rate guarantees.<n>It employs an online learning algorithm to estimate an optimal threshold for each cached prompt, enabling reliable cache responses without additional training.<n>Our experiments show that vCache consistently meets the specified error bounds while outperforming state-of-the-art static-threshold and fine-tuned embedding baselines.
arXiv Detail & Related papers (2025-02-06T04:16:20Z) - MeanCache: User-Centric Semantic Caching for LLM Web Services [8.350378532274405]
Caching is a natural solution to reduce inference costs on repeated queries.<n>This paper introduces MeanCache, a user-centric semantic cache for LLM-based services.<n>MeanCache identifies semantically similar queries to determine cache hit or miss.
arXiv Detail & Related papers (2024-03-05T06:23:50Z) - Accelerating Deep Learning Classification with Error-controlled
Approximate-key Caching [72.50506500576746]
We propose a novel caching paradigm, that we named approximate-key caching.
While approximate cache hits alleviate DL inference workload and increase the system throughput, they however introduce an approximation error.
We analytically model our caching system performance for classic LRU and ideal caches, we perform a trace-driven evaluation of the expected performance, and we compare the benefits of our proposed approach with the state-of-the-art similarity caching.
arXiv Detail & Related papers (2021-12-13T13:49:11Z) - ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and
Gradient Accumulation [106.04777600352743]
Differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory.
The single-path DARTS comes in, which only chooses a single-path submodel at each step.
While being memory-friendly, it also comes with low computational costs.
We propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure.
arXiv Detail & Related papers (2020-11-23T06:34:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.