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TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning — arXiv:2610.09360

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09360
License (as archived): CC0-1.0
Archived: 2026-10-08T11:09:14.412Z
SHA-256 of the archived text: 7f594069777f9af7ce62a74f43ee52468b9e750881942edb87df029fd2188e2a
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How to cite

TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning — arXiv:2610.09360 — arXiv (en), https://arxiv.org/abs/2610.09360 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ec24eeedf57e (Metraipolis archive dc_ec24eeedf57e, sha256 7f594069777f9af7ce62a74f43ee52468b9e750881942edb87df029fd2188e2a)

BibTeX

@misc{dcec24eeedf57e,
  title = {TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning — arXiv:2610.09360},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09360},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ec24eeedf57e; sha256 7f594069777f9af7ce62a74f43ee52468b9e750881942edb87df029fd2188e2a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09360. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Real-world documents distribute evidence across text, tables, figures, and captions within complex page layouts. Answering complex questions over such documents therefore requires more than retrieving relevant passages: systems must recover the evidence topology that connects heterogeneous evidence units. Existing GraphRAG evaluations remain largely text-centered, while multimodal document RAG benchmarks assess cross-modal retrieval and generation without directly evaluating recovery of the intended evidence topology. We introduce TOPOGRAPHRAG-BENCH, a layout-grounded benchmark for multimodal evidence reasoning in GraphRAG, comprising 2,024 questions over 201 long, visually rich documents. Questions are constructed bottom-up from text, figure, and table evidence units under three controlled topologies: single-hop retrieval, bridge-chain reasoning, and multi-source synthesis. To ensure that questions preserve their intended structure, we apply counterfactual validation for shortcut resistance, modality necessity, and evidence necessity. We evaluate text-only GraphRAG, page-level visual retrieval, and multimodal GraphRAG systems using retrieval, generation, and topology-aware reasoning metrics. Multimodal GraphRAG systems achieve the strongest overall performance, but still fail when visual-textual evidence alignment or multi-unit composition is incomplete. Text-only GraphRAG struggles when key dependencies are grounded in figures or tables, while page-level visual retrieval lacks the fine-grained structure needed for topology recovery. These findings motivate GraphRAG systems that move beyond text-derived entity relation graphs to explicitly model document layouts, cross-modal evidence alignment, and the reasoning roles of evidence units. Code and data are available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.09360 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T10:14:05.929Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).