arXiv Computation and Language October 27, 2026 By Jinghong Chen, Jingbiao Mei, Guangyu Yang, Bill Byrne

BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering

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arXiv Computer Vision

Sep 4

Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity

The paper introduces Invoice Haystack, a benchmark of 1,500 anonymized invoices and 200 question‑answer pairs that tests document retrieval and visual question answering under strong visual homogeneity. It shows that existing benchmarks suffer from embedding collapse, with Invoice Haystack’s mean pairwise cosine similarity at 0.73 versus 0.38 and 0.31 in DocHaystack and InfoHaystack. The authors propose VL‑RAG, a hybrid retrieval‑augmented generation framework that combines text and visual embeddings and a VLM‑based verification filter, achieving 60.0% Recall@1 on Invoice Haystack‑500 and improving performance on other benchmarks.

By Heethanjan Kanagalingam, Thenukan Pathmanathan, Mokeeshan Vathanakumar, Basim Azam, Sarah Monazam Erfani, Naveed Akhtar

llms rag nlp multimodal benchmarks

arXiv Machine Learning

Sep 22

Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation

arXiv:2609.22162v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generatio...

By Can Peng, Yu Liu, Yingyu Yang, Anjie Le, Yuyuan Liu, Qianye Yang, J. Alison Noble

llms rag nlp multimodal benchmarks

arXiv Machine Learning

Aug 5

Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering

arXiv:2608. 02907v1 Announce Type: new Abstract: Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs.

By Jingchen Sun, Shaobo Han, Ruiyi Zhang, Naresh Kumar Devulapally, Ming Liu, Yitao Long, Vishnu Suresh Lokhande, Changyou Chen

nlp multimodal benchmarks

arXiv Computer Vision

Sep 1

Learning to Search: A Decision-Based Agent for Knowledge-Based Visual Question Answering

arXiv:2604.07146v3 Announce Type: replace Abstract: Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for...

By Zhuohong Chen, Zhenxian Wu, Yunyao Yu, Hangrui Xu, Zirui Liao, Zhifang Liu, Xiangwen Deng, Pen Jiao, Haoqian Wang

llms rag agents nlp fine-tuning multimodal benchmarks

arXiv AI

Oct 15

PixSearch: Region-Grounded Retrieval for Knowledgeable Large Multimodal Models

arXiv:2601.19060v2 Announce Type: replace-cross Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on...

By Jeonghwan Kim, Renjie Tao, Sanat Sharma, Jiaqi Wang, Kai Sun, Zhaojiang Lin, Seungwhan Moon, Lambert Mathias, Anuj Kumar, Heng Ji, Xin Luna Dong

rag computer-vision nlp fine-tuning efficiency multimodal benchmarks

arXiv AI

Jul 29

VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents

arXiv:2607. 24748v1 Announce Type: cross Abstract: Visually-rich documents such as reports, slides, and manuals often distribute the evidence needed to answer a question across multiple pages, mixing text with layout cues, tables, charts, and figures.

By Seonok Kim

rag agents nlp multimodal benchmarks