M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models
The article discusses a new method called M3DocDep for processing long, multi-page documents using large vision-language models. This method aims to improve the chunking of documents by recovering block-level dependencies before creating retrieval units. The results indicate significant improvements in retrieval and answer quality metrics compared to existing methods.
- ▪M3DocDep is designed to enhance retrieval-augmented generation in long, multi-page industrial documents.
- ▪The method addresses issues with existing chunkers that fail to capture cross-page relationships and other structural cues.
- ▪M3DocDep shows improvements in various benchmarks, including a 28.5 to 39.6 percent increase in STEDS and a 1.1 to 15.3 percent increase in retrieval nDCG.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18774 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| 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 | Tb4rD2PrSbtn |
| 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:2605.18774 (cs) [Submitted on 17 Apr 2026] Title:M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models Authors:Joongmin Shin, Jeongbae Park, Jaehyung Seo, Heuiseok Lim View a PDF of the paper titled M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models, by Joongmin Shin and 3 other authors View PDF HTML (experimental) Abstract:In long, multi-page industrial documents, retrieval-augmented generation (RAG) depends heavily on whether chunk boundaries follow the document's true structure.
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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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