arXiv:2607. 25135v1 Announce Type: new Abstract: Recent advances in RAG aim to optimize for performance by paying high ingestion costs for knowledge ingestion: building knowledge graphs or extracting SQL tables.
By Hilaf Hasson, Aditya Chakravarty, Jayant Thomas, Krishna Gogineni
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
The paper introduces a retrieved‑span training approach for query‑focused meeting summarization on the QMSum benchmark. By fine‑tuning a 406 M Fusion‑in‑Decoder model on 2,000‑word retrieved spans, the authors recover a 6.30 ROUGE‑1 loss incurred when moving from capped long input and achieve a test score of 36.33 ROUGE‑1, comparable to a larger 1.2 B system. The smaller model uses roughly one‑third the parameters and less than half the peak inference memory, while span‑regime fine‑tuning adds significant gains over the baseline.
"whyItMatters":"The study demonstrates that efficient, smaller models can match or exceed larger systems on QMSum using span‑based fine‑tuning, offering a practical path for scalable query‑focused meeting summarization."
By Edward Xi Yang (Ertas AI)
The paper investigates how conversational Retrieval-Augmented Generation (RAG) benchmarks that rely on relevance judgments to determine context sufficiency can be misleading. By re‑running official baselines on the RETECO track and evaluating with both large‑language‑model judges and a blind audit, the authors show that stronger retrievers produce a higher proportion of contexts lacking gold documents yet still containing the answer, leading to a sharp drop in shortcut precision from 0.68 to 0.10. The study demonstrates that many false misses arise from hard negatives, gold content from other turns, or neighboring chunks, and that using retrieval scores alone as a sufficiency detector can underestimate answerability by up to 0.19 in AUROC.
By Ivan Kochergin, Vasily Konovalov
The paper argues that retrieval‑augmented question‑answering systems should perform semantic compilation at ingest time rather than re‑deriving meaning at query time. By building a maintained structure—incrementally updated embeddings and validated atomic claims—read operations become far cheaper, with experimental results showing higher accuracy and lower token usage compared to traditional chunk‑based retrieval. The authors present two proofs: cheaper incremental updates and superior performance on broadcast‑interview transcripts, suggesting a new systems agenda for compilation and read planning.
By Kyle Wild, Yusuke Takahashi, Asako Uraki
arXiv:2611.03179v1 Announce Type: cross
Abstract: People often attach instructions to their search queries, asking for pages written for beginners, documents in a certain language or official sources...
By S. M. Mahbub Uddin Ahmed, Abdullah Al Musaddiq