Re:CAP is a reference‑free audit loop for retrieval‑augmented generation (RAG) pipelines that probes for missing documents instead of enumerating all relevant ones. It identifies covered topics, generates probing questions, retrieves candidate documents, and uses an LLM judge to keep only those that add new information. On several benchmarks, Re:CAP recovers a significant portion of gold documents that flat BM25 or hybrid retrieval misses, and human evaluation shows most of these documents add new information.
By Aviral Joshi, Hanoz Bhathena, Max Nelson, Saket Sharma
arXiv:2607. 04088v1 Announce Type: cross Abstract: LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time.
By Yingdong Yang, Haijian Wu
arXiv:2609.37911v1 Announce Type: cross
Abstract: Scientific queries are often brief, while relevant papers use specialized vocabulary. Generated query expansion can bridge this mismatch, but earlier...
By Ryan C. Barron, Cade W. Trotter, Maksim E. Eren, Kim {\O}. Rasmussen, Liz D. Miller, Benjamin J. Migliori
Friday's big release was Qwen 3. 8 27B , an Apache 2 licensed 27B parameter vision-capable LLM from Alibaba's Qwen research lab.
The article reports on a benchmark that reduced 1,000 Apache Iceberg files to just six, then measured how this consolidation affected query performance across three SQL workloads. It details the methodology and results of the experiment, highlighting changes in execution speed and resource usage. The findings illustrate the trade‑offs between file count and query efficiency in large data systems.
By Thomas Reid
arXiv:2603. 26815v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by similarity.
By Zhiyuan Cheng, Longying Lai, Yue Liu
RIT‑RAG is a retrieval‑augmented generation framework that combines content retrieval with structural navigation of documents. It builds a tree for each document from its table of contents or sitemap, then uses retrieved chunk positions to induce manageable sub‑trees that an LLM agent can navigate, read selectively, and reformulate queries. Experiments on financial, scientific, and customer‑support benchmarks show RIT‑RAG outperforms vanilla, graph‑based, and agentic baselines, achieving up to 11.4‑point accuracy gains on the new EntQABench of 2.84 million technical‑documentation webpages.
By Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan, Yatin Nandwani, Sachindra Joshi, Dinesh Raghu
KnowForge is a modular pipeline for open‑domain multimodal document question answering that separates retrieval, judging, and reading into distinct agents. It uses a hybrid‑retrieval knowledge‑point index, a Locator agent for searching, a Bridge Judge to enforce a second hop, a Deep Reader that handles text, images, and tables, and a synthesizer with format checking. On the M3DocVQA benchmark it achieves an F1 of 47.5, outperforming prior systems by 11 points, and shows strong gains on MultiHop‑RAG and MMLongBench‑Doc as well.
By Wenlin Lin, Fanzhe Wei
arXiv:2604.27724v2 Announce Type: replace
Abstract: Medical retrieval-augmented generation (RAG) systems typically operate on text chunks extracted from biomedical literature, discarding the rich vis...
By Xupeng Chen, Binbin Shi, Chenqian Le, Jiaqi Zhang, Kewen Wang, Ran Gong, Jinhan Zhang, Chihang Wang
Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.
By Kezhan Shi
arXiv:2607. 18603v1 Announce Type: cross Abstract: We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the representations exposed to a retrieval system.
By Sam O'Nuallain, Nithya Rajkumar, Ramya Narayanasamy, Hanna Jiang, Shreyas Chaudhari, Andrew Drozdov
Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.
By Kezhan Shi