LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding
The paper introduces LFRAG, a novel framework for multimodal document understanding that enhances retrieval-augmented generation. It shifts from page-level to block-level retrieval to improve accuracy and reduce redundancy in downstream tasks. Extensive experiments show LFRAG achieves state-of-the-art performance in retrieval tasks and significantly lowers token consumption in generation tasks.
- ▪LFRAG advances multimodal retrieval-augmented generation from page-level to block-level retrieval.
- ▪The framework utilizes layout segmentation to create semantically coherent fine-grained retrieval units.
- ▪LFRAG outperforms the best baseline by 7.20% in answer accuracy and reduces token consumption by 73.07%.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22829 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | WlL-zn_7VZNR |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Information Retrieval arXiv:2605.22829 (cs) [Submitted on 18 Apr 2026] Title:LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding Authors:Yifan Zhu, Yu Mi, Yue Lu, Yanchu Guan, Zhixuan Chu View a PDF of the paper titled LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding, by Yifan Zhu and 4 other authors View PDF Abstract:Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledge.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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