arXiv:2605.28093v3 Announce Type: replace
Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering...
By Yikai Zhu, Kunfeng Chen, Qihuang Zhong, Juhua Liu, Bo Du
CacheWeaver is a lightweight prompt‑layer technique that orders evidence for Retrieval‑Augmented Generation (RAG) to improve cache reuse in serving engines like vLLM. By maintaining a prefix tree of recently served evidence sequences and greedily placing the most reusable prefix first, it reduces median time‑to‑first‑token by 20‑33 % across three vLLM configurations without harming answer quality. The greedy policy achieves 97.5 % of the gain possible with oracle ordering, showing that most reusable prefix locality can be recovered with a simple scheduling layer.
By Kaizhen Tan, Rong Gu, Mingyuan Li
MiniVer‑V is a new benchmark for short‑video fact‑checking that contains 195 videos, each annotated with a supported, refuted, or insufficient verdict and 5,510 multimodal evidence units (visual keyframes, speech transcripts, and web‑retrieved sources). The authors propose a two‑layer verification framework that first checks claim‑video consistency using internal evidence, then determines the factual verdict with external corroboration, and employs a sufficiency‑driven greedy search to assemble only the minimal evidence needed for a confident decision. Using Claude Sonnet 4, the method achieves a Macro‑F1 of 0.510 with an average of 4.5 evidence units—about 16 % of the full set—while matching the full‑evidence baseline and improving detection of insufficient cases.
By Leran Chen, Lingnan Kong, Zile Cai
arXiv:2606. 00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence.
By Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, Anuj Sharma
arXiv:2608.23011v1 Announce Type: cross
Abstract: Graph-based retrieval-augmented generation (RAG) provides a scalable paradigm for long-video understanding, but existing systems typically inherit a...
By Zhe Jin, Zhimin Lin, Bin Zheng, Junhua Fang, Huihua Yang
Coverage Under Contradiction: A Benchmark for Negation-Aware Retrieval introduces CoverageBench-Negate, a new benchmark that augments the existing CoverageBench with controlled contradictions. For each evidence nugget, a negated counterpart is created and its supporting documents are edited to support the negation while preserving context, resulting in about 63,000 nugget‑document pairs across six datasets. The benchmark includes rich construction metadata, LLM judgments, and a human audit, and baseline experiments show BM25 can distinguish original from edited evidence better than dense retrievers.
By Pasha Abrishamchian, Eugene Yang, Andrew Yates, Nazli Goharian, Ophir Frieder