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