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arxiv: 2606.08979 · v1 · pith:DC2VUGFUnew · submitted 2026-06-08 · 💻 cs.IR

EviProp: Seeded Relevance Diffusion on Chunk-Page Graphs for Long Multimodal Document Retrieval

Hongwei Zhang , Xiaoman Wang , Zehui Ling , Ruicheng Zhu , Yue Zhang , Pinlong Cai , Fuke Shen , Botian Shi
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Tongquan Wei Guohang Yan
This is my paper

Pith reviewed 2026-06-27 15:02 UTC · model grok-4.3

classification 💻 cs.IR
keywords document retrievalmultimodal retrievalevidence retrievalgraph diffusionchunk-page graphpersonalized pageranklong documentsvisual retrieval
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The pith

Seeded relevance diffusion on multimodal chunk-page graphs recovers evidence pages missed by independent scoring.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing retrievers score each page in isolation against the query, which under-ranks pages whose evidence sits only in fine-grained chunks or depends on connections inside the document. EviProp builds a graph for every document that connects chunks and pages through hierarchical, sequential, and similarity links. It starts with dense visual scores on pages plus sparse seeds from chunks, then runs Personalized PageRank to spread relevance across those links. When the graph links reflect the associations that matter, the diffusion step surfaces the missed pages. The paper reports that these retrieval gains improve downstream question-answering accuracy while adding almost no online cost.

Core claim

EviProp recovers evidence pages whose signals are localized in fine-grained chunks or depend on document-internal associations by modeling each document as a multimodal Chunk-Page graph with hierarchical, sequential, and similarity links, combining dense visual page priors with sparse chunk seeds, and running Personalized PageRank to diffuse relevance over the graph.

What carries the argument

The multimodal Chunk-Page graph whose hierarchical, sequential, and similarity links carry relevance diffusion from combined page priors and chunk seeds via Personalized PageRank.

If this is right

  • Evidence-page retrieval improves over independent visual retrieval and text-visual fusion baselines.
  • The retrieval improvement carries through to higher answer accuracy in downstream question answering.
  • The added diffusion step imposes negligible overhead during online retrieval.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same seeded-diffusion pattern could be applied to other retrieval settings that already have natural chunk-page or section hierarchies.
  • If the graph links prove reliable, future systems might replace some hand-crafted fusion rules with learned or fixed diffusion operators.
  • Testing the method on documents whose cross-chunk dependencies are explicitly annotated would directly measure how much of the gain comes from the diffusion step rather than the initial seeds.

Load-bearing premise

The hierarchical, sequential, and similarity links in the Chunk-Page graph accurately capture the document-internal associations that determine which evidence pages are under-ranked.

What would settle it

A controlled test on long documents where all relevant associations are removed from the graph links would show whether the diffusion step still produces retrieval gains.

Figures

Figures reproduced from arXiv: 2606.08979 by Botian Shi, Fuke Shen, Guohang Yan, Hongwei Zhang, Pinlong Cai, Ruicheng Zhu, Tongquan Wei, Xiaoman Wang, Yue Zhang, Zehui Ling.

Figure 1
Figure 1. Figure 1: Motivating example on MMLongBench￾Doc. Independent page ranking retrieves only the con￾straint page and misses the evidence page. EviProp recovers the evidence page by diffusing relevance over a Chunk–Page graph initialized with page priors and chunk seeds. become the standard practice in LVLM-based doc￾ument QA (Cho et al., 2024; Han et al., 2025), providing concise visual context for downstream reasoning… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of EviProp. EviProp constructs a Chunk–Page graph offline and performs seeded relevance diffusion online to retrieve evidence pages. The downstream LVLM uses the retrieved pages as visual context for answer generation, but the generation module is not modified by EviProp. Vpi into a single vector and computing cosine similarity: w(pi , pj ) = w(pj , pi) = max 0, v¯ ⊤ pi v¯pj  , (4) where neg… view at source ↗
Figure 3
Figure 3. Figure 3: compares end-to-end latency and down￾stream QA accuracy on MMLongBench-Doc. EviProp achieves the best accuracy under both backbones while maintaining latency compara￾ble to M3DocRAG. Compared with M3DocRAG, Direct (30p) M3DocRAG MDocAgent EviProp 0 2 4 6 8 10 12 14 Total time (s) 12.4s 2.3s 8.1s 2.5s Qwen2.5-VL-7B Inference time Retrieval time Avg. accuracy (%) Direct (30p) M3DocRAG MDocAgent EviProp 0.0 2… view at source ↗
Figure 4
Figure 4. Figure 4: Hyperparameter sensitivity analysis on MMLongBench-Doc. We report Recall@5 and NDCG@5 under [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Case study on MMLongBench-Doc. EviProp retrieves the correct evidence page by leveraging seeded relevance diffusion, enabling the LVLM to provide the correct answer. In contrast, LVLM Direct Inference and baseline retrieval methods fail due to limited input context or irrelevant retrieved pages. precise grounding enables the LVLM to read the visual content and answer the question correctly. F Prompts for V… view at source ↗
read the original abstract

Retrieving evidence pages from visually rich long documents is a key challenge in document question answering. Existing page-level visual retrievers operate under an independent matching paradigm: each page is scored in isolation based on query-page similarity. This paradigm can under-rank evidence pages whose signals are localized in fine-grained chunks or depend on document-internal associations. We propose EviProp, a retrieval method that recovers such pages via seeded relevance diffusion. EviProp models each document as a multimodal Chunk-Page graph with hierarchical, sequential, and similarity links. Given a query, it combines dense visual page priors with sparse chunk seeds, then runs Personalized PageRank to diffuse relevance over the graph. Experiments on MMLongBench-Doc and LongDocURL show consistent gains in evidence-page retrieval over independent visual retrieval and text-visual fusion baselines. Downstream QA results further show that improved retrieval translates into better answer accuracy, with negligible online retrieval overhead. Our code is released at https://github.com/Flyecnu/EviProp.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper proposes EviProp, a retrieval method for evidence pages in long multimodal documents. It models each document as a multimodal Chunk-Page graph incorporating hierarchical, sequential, and similarity links; seeds relevance using dense visual page priors combined with sparse chunk matches; and diffuses scores via Personalized PageRank. Experiments on MMLongBench-Doc and LongDocURL report consistent gains over independent visual retrieval and text-visual fusion baselines in evidence-page retrieval, with corresponding improvements in downstream QA accuracy and negligible online overhead. Code is released.

Significance. If the reported gains hold under scrutiny, the approach demonstrates how graph-based diffusion can surface evidence pages whose signals are localized in chunks or rely on document-internal associations, extending beyond independent page-level matching. The explicit code release supports reproducibility and verification of the seeded diffusion implementation.

minor comments (2)
  1. [Experiments] The abstract and results summary claim 'consistent gains' without reporting magnitudes, error bars, or statistical tests; adding these in the experimental section would strengthen the evaluation.
  2. [Method] Ablation details on the contribution of each graph link type (hierarchical, sequential, similarity) and the seeding strategy are not referenced in the provided description; including them would clarify the method's components.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary, recognition of the significance of seeded relevance diffusion on multimodal graphs, and recommendation for minor revision. We appreciate the note on reproducibility via code release.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces EviProp as a seeded relevance diffusion method on a newly constructed multimodal Chunk-Page graph, evaluated via experiments on external benchmarks (MMLongBench-Doc, LongDocURL) against independent baselines. No load-bearing equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided abstract or description that reduce the claimed gains to inputs by construction. The central proposal (graph modeling + PPR diffusion) is presented as a distinct algorithmic contribution with downstream QA validation, making the derivation self-contained against external evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the modeling assumption that the proposed graph edges reflect retrieval-relevant associations and that Personalized PageRank diffusion from mixed visual and chunk seeds improves ranking over independent scoring. No free parameters or invented physical entities are named in the abstract.

axioms (1)
  • domain assumption Personalized PageRank diffusion on the Chunk-Page graph surfaces pages whose evidence is localized or context-dependent
    Invoked when the abstract states that the method recovers pages missed by independent matching.
invented entities (1)
  • Chunk-Page graph with hierarchical, sequential, and similarity links no independent evidence
    purpose: To enable relevance diffusion across document structure
    Introduced as the core modeling choice; no independent evidence outside the paper is mentioned.

pith-pipeline@v0.9.1-grok · 5737 in / 1365 out tokens · 21973 ms · 2026-06-27T15:02:50.605457+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

48 extracted references · 12 linked inside Pith

  1. [1]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    V-doc: Visual questions answers with documents , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  2. [2]

    Advances in Neural Information Processing Systems , volume=

    Mmlongbench-doc: Benchmarking long-context document understanding with visualizations , author=. Advances in Neural Information Processing Systems , volume=

  3. [3]

    Visdom: Multi-document qa with visually rich elements using multimodal retrieval-augmented generation , author=. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    2025

  4. [4]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Ocr hinders rag: Evaluating the cascading impact of ocr on retrieval-augmented generation , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  5. [5]

    IEEE access , volume=

    Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR) , author=. IEEE access , volume=. 2020 , publisher=

    2020

  6. [6]

    arXiv preprint arXiv:2409.18839 , year=

    Mineru: An open-source solution for precise document content extraction , author=. arXiv preprint arXiv:2409.18839 , year=

    Pith/arXiv arXiv

  7. [7]

    arXiv preprint arXiv:2409.01704 , year=

    General ocr theory: Towards ocr-2.0 via a unified end-to-end model , author=. arXiv preprint arXiv:2409.01704 , year=

    Pith/arXiv arXiv

  8. [8]

    arXiv preprint arXiv:2411.04952 , year=

    M3docrag: Multi-modal retrieval is what you need for multi-page multi-document understanding , author=. arXiv preprint arXiv:2411.04952 , year=

    arXiv

  9. [9]

    arXiv preprint arXiv:2503.13964 , year=

    Mdocagent: A multi-modal multi-agent framework for document understanding , author=. arXiv preprint arXiv:2503.13964 , year=

    arXiv

  10. [10]

    International Conference on Learning Representations , volume=

    Colpali: Efficient document retrieval with vision language models , author=. International Conference on Learning Representations , volume=

  11. [11]

    2025 , eprint=

    Qwen2.5-VL Technical Report , author=. 2025 , eprint=

    2025

  12. [12]

    The pagerank citation ranking: Bring order to the web , author=. Proc. of the 7th International World Wide Web Conf.--1998 , year=

    1998

  13. [13]

    Proceedings of the 11th international conference on World Wide Web , pages=

    Topic-sensitive pagerank , author=. Proceedings of the 11th international conference on World Wide Web , pages=

  14. [14]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Longdocurl: a comprehensive multimodal long document benchmark integrating understanding, reasoning, and locating , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  15. [15]

    Advances in neural information processing systems , volume=

    Hipporag: Neurobiologically inspired long-term memory for large language models , author=. Advances in neural information processing systems , volume=

  16. [16]

    arXiv preprint arXiv:2502.14802 , year=

    From rag to memory: Non-parametric continual learning for large language models , author=. arXiv preprint arXiv:2502.14802 , year=

    Pith/arXiv arXiv

  17. [17]

    arXiv preprint arXiv:2510.10114 , year=

    Linearrag: Linear graph retrieval augmented generation on large-scale corpora , author=. arXiv preprint arXiv:2510.10114 , year=

    arXiv

  18. [18]

    International Conference on Learning Representations , volume=

    Visrag: Vision-based retrieval-augmented generation on multi-modality documents , author=. International Conference on Learning Representations , volume=

  19. [19]

    Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

    Docvqa: A dataset for vqa on document images , author=. Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

  20. [20]

    Pattern Recognition , volume=

    Hierarchical multimodal transformers for multipage docvqa , author=. Pattern Recognition , volume=. 2023 , publisher=

    2023

  21. [21]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Slidevqa: A dataset for document visual question answering on multiple images , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  22. [22]

    arXiv preprint arXiv:2312.10997 , volume=

    Retrieval-augmented generation for large language models: A survey , author=. arXiv preprint arXiv:2312.10997 , volume=

    Pith/arXiv arXiv

  23. [23]

    Advances in neural information processing systems , volume=

    Retrieval-augmented generation for knowledge-intensive nlp tasks , author=. Advances in neural information processing systems , volume=

  24. [24]

    Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

    Unifying multimodal retrieval via document screenshot embedding , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

    2024

  25. [25]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    Vdocrag: Retrieval-augmented generation over visually-rich documents , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  26. [26]

    Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages=

    Murag: Multimodal retrieval-augmented generator for open question answering over images and text , author=. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages=

    2022

  27. [27]

    arXiv preprint arXiv:2510.12323 , year=

    RAG-Anything: All-in-One RAG Framework , author=. arXiv preprint arXiv:2510.12323 , year=

    arXiv

  28. [28]

    arXiv preprint arXiv:2410.05779 , volume=

    Lightrag: Simple and fast retrieval-augmented generation , author=. arXiv preprint arXiv:2410.05779 , volume=

    Pith/arXiv arXiv

  29. [29]

    arXiv preprint arXiv:2404.16130 , year=

    From local to global: A graph rag approach to query-focused summarization , author=. arXiv preprint arXiv:2404.16130 , year=

    Pith/arXiv arXiv

  30. [30]

    arXiv preprint arXiv:2510.07233 , year=

    Lad-rag: layout-aware dynamic rag for visually-rich document understanding , author=. arXiv preprint arXiv:2510.07233 , year=

    arXiv

  31. [31]

    arXiv preprint arXiv:2508.05318 , year=

    mKG-RAG: Multimodal Knowledge Graph-Enhanced RAG for Visual Question Answering , author=. arXiv preprint arXiv:2508.05318 , year=

    Pith/arXiv arXiv

  32. [32]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Aligning vision to language: Annotation-free multimodal knowledge graph construction for enhanced llms reasoning , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  33. [33]

    arXiv preprint arXiv:2505.09388 , year=

    Qwen3 technical report , author=. arXiv preprint arXiv:2505.09388 , year=

    Pith/arXiv arXiv

  34. [34]

    2: Pushing the frontier of open large language models , author=

    Deepseek-v3. 2: Pushing the frontier of open large language models , author=. arXiv preprint arXiv:2512.02556 , year=

    Pith/arXiv arXiv

  35. [35]

    arXiv preprint arXiv:2410.21276 , year=

    Gpt-4o system card , author=. arXiv preprint arXiv:2410.21276 , year=

    Pith/arXiv arXiv

  36. [36]

    arXiv preprint arXiv:2407.21783 , year=

    The llama 3 herd of models , author=. arXiv preprint arXiv:2407.21783 , year=

    Pith/arXiv arXiv

  37. [37]

    arXiv preprint arXiv:2511.21631 , year=

    Qwen3-vl technical report , author=. arXiv preprint arXiv:2511.21631 , year=

    Pith/arXiv arXiv

  38. [38]

    2025 , eprint=

    Qwen2.5 Technical Report , author=. 2025 , eprint=

    2025

  39. [39]

    Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , pages=

    Colbert: Efficient and effective passage search via contextualized late interaction over bert , author=. Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , pages=

  40. [40]

    Transactions of the association for computational linguistics , volume=

    Lost in the middle: How language models use long contexts , author=. Transactions of the association for computational linguistics , volume=

  41. [41]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  42. [42]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Visa: Retrieval augmented generation with visual source attribution , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  43. [43]

    arXiv preprint arXiv:2507.05714 , year=

    Hirag: Hierarchical-thought instruction-tuning retrieval-augmented generation , author=. arXiv preprint arXiv:2507.05714 , year=

    arXiv

  44. [44]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    Molorag: Bootstrapping document understanding via multi-modal logic-aware retrieval , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    2025

  45. [45]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    Towards natural language-based document image retrieval: new dataset and benchmark , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  46. [46]

    Advances in Neural Information Processing Systems , volume=

    Uda: A benchmark suite for retrieval augmented generation in real-world document analysis , author=. Advances in Neural Information Processing Systems , volume=

  47. [47]

    Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=

    Colbertv2: Effective and efficient retrieval via lightweight late interaction , author=. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=

    2022

  48. [48]

    Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval , pages=

    Reciprocal rank fusion outperforms condorcet and individual rank learning methods , author=. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval , pages=