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arxiv: 2601.08620 · v2 · submitted 2026-01-13 · 💻 cs.AI · cs.CV

Recognition: 2 theorem links

· Lean Theorem

ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios

Ant\'onio Loison , Quentin Mac\'e , Antoine Edy , Victor Xing , Tom Balough , Gabriel Moreira , Bo Liu , Manuel Faysse
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C\'eline Hudelot Gautier Viaud
Authors on Pith no claims yet

Pith reviewed 2026-05-16 14:41 UTC · model grok-4.3

classification 💻 cs.AI cs.CV
keywords multimodal RAGvisual retrievaldocument benchmarkretrieval-augmented generationvisual groundinglate-interaction modelshybrid retrievalnon-textual elements
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The pith

Visual retrievers outperform textual ones in complex multimodal RAG tasks on a new benchmark.

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

The paper introduces ViDoRe v3, a benchmark that tests retrieval-augmented generation systems on queries requiring interpretation of visual elements like tables and charts across multiple documents. It supplies 26,000 pages from 10 professional datasets along with 3,099 human-verified queries in six languages and detailed annotations for relevance and localization. Evaluations of current pipelines show that visual retrievers exceed textual retrievers, late-interaction models plus textual reranking raise scores, and hybrid or visual contexts improve generated answers. Systems still falter on non-textual content, open-ended questions, and precise visual references. The benchmark is released to drive progress on these gaps.

Core claim

ViDoRe v3 establishes that visual retrievers outperform textual ones, late-interaction models and textual reranking substantially improve performance, and hybrid or purely visual contexts enhance answer generation quality, while current models still struggle with non-textual elements, open-ended queries, and fine-grained visual grounding.

What carries the argument

ViDoRe v3 benchmark of 10 datasets containing visually rich documents paired with multi-type queries and human annotations for retrieval relevance and bounding-box localization.

Load-bearing premise

The 10 selected datasets and their human annotations accurately represent complex real-world multimodal RAG scenarios without significant selection or annotation bias.

What would settle it

Re-evaluating the same pipelines on an independent collection of real-world documents and finding that textual retrievers match or exceed visual retrievers would refute the performance ordering.

read the original abstract

Retrieval-Augmented Generation (RAG) pipelines must address challenges beyond simple single-document retrieval, such as interpreting visual elements (tables, charts, images), synthesizing information across documents, and providing accurate source grounding. Existing benchmarks fail to capture this complexity, often focusing on textual data, single-document comprehension, or evaluating retrieval and generation in isolation. We introduce ViDoRe v3, a comprehensive multimodal RAG benchmark featuring multi-type queries over visually rich document corpora. It covers 10 datasets across diverse professional domains, comprising ~26,000 document pages paired with 3,099 human-verified queries, each available in 6 languages. Through 12,000 hours of human annotation effort, we provide high-quality annotations for retrieval relevance, bounding box localization, and verified reference answers. Our evaluation of state-of-the-art RAG pipelines reveals that visual retrievers outperform textual ones, late-interaction models and textual reranking substantially improve performance, and hybrid or purely visual contexts enhance answer generation quality. However, current models still struggle with non-textual elements, open-ended queries, and fine-grained visual grounding. To encourage progress in addressing these challenges, the benchmark is released under a commercially permissive license at https://hf.co/vidore.

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

3 major / 2 minor

Summary. The paper introduces ViDoRe V3, a multimodal RAG benchmark comprising 10 datasets (~26k document pages) and 3,099 human-verified queries across 6 languages. Through 12,000 hours of annotation, it provides labels for retrieval relevance, bounding-box localization, and reference answers. Evaluation of SOTA RAG pipelines shows visual retrievers outperforming textual ones, benefits from late-interaction models and textual reranking, and improved generation from hybrid or visual contexts, while highlighting persistent struggles with non-textual elements, open-ended queries, and fine-grained visual grounding. The benchmark is released publicly.

Significance. If the dataset construction and annotations prove representative and unbiased, ViDoRe V3 would offer a valuable, large-scale resource for assessing multimodal RAG systems in professional domains, directly addressing gaps in existing textual or single-document benchmarks and providing concrete directions for improving visual grounding and multi-document synthesis.

major comments (3)
  1. [Section 3] Section 3 (Dataset Construction): The claim that the 10 datasets accurately represent complex real-world multimodal RAG scenarios lacks supporting evidence such as inter-annotator agreement statistics, query-type stratification, or comparison against external professional corpus distributions; without these, the reported outperformance of visual retrievers may reflect selection bias toward visually dense documents rather than general superiority.
  2. [Section 4] Section 4 (Annotation Process) and Evaluation: The 12,000-hour annotation effort is described but no details are given on quality controls, agreement metrics for relevance/bounding-box/answer labels, or statistical significance testing of the performance differences; this undermines verification of the central claims that visual retrievers and hybrid contexts enhance quality.
  3. [Evaluation Results] Evaluation Results (Table 2 or equivalent): The abstract states clear performance gaps between visual and textual retrievers, yet without access to full query construction details or variance estimates across the 3,099 queries, it is difficult to confirm that the gains are robust rather than artifacts of the specific 10-dataset mix.
minor comments (2)
  1. [Abstract] The abstract mentions 'late-interaction models' without defining the specific models or interaction mechanisms in the main text; add a brief clarification or reference.
  2. [Figures] Figure captions for retrieval and generation results should explicitly state the number of runs or seeds used to compute reported metrics.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough and constructive review. The comments highlight important areas where additional evidence and details can strengthen the manuscript's claims regarding dataset representativeness, annotation quality, and evaluation robustness. We address each major comment below and outline the revisions we will incorporate.

read point-by-point responses

  1. Referee: Section 3 (Dataset Construction): The claim that the 10 datasets accurately represent complex real-world multimodal RAG scenarios lacks supporting evidence such as inter-annotator agreement statistics, query-type stratification, or comparison against external professional corpus distributions; without these, the reported outperformance of visual retrievers may reflect selection bias toward visually dense documents rather than general superiority.

    Authors: We agree that stronger evidence for representativeness would bolster the claims. In the revised manuscript, we will add inter-annotator agreement statistics from the annotation process and include a breakdown of query types (e.g., by domain, query complexity, and visual element involvement) to demonstrate stratification. We will also expand the dataset selection rationale with details on how the 10 corpora were chosen to cover professional domains with multimodal content. A quantitative comparison against external professional corpus distributions is not feasible given the proprietary nature of many such resources, but we will discuss potential selection biases explicitly and note limitations in generalizability. revision: partial

  2. Referee: Section 4 (Annotation Process) and Evaluation: The 12,000-hour annotation effort is described but no details are given on quality controls, agreement metrics for relevance/bounding-box/answer labels, or statistical significance testing of the performance differences; this undermines verification of the central claims that visual retrievers and hybrid contexts enhance quality.

    Authors: We appreciate this observation and will rectify the omission. The revised manuscript will detail the quality control procedures, including multi-stage expert review and validation workflows. We will report agreement metrics such as Cohen's kappa for relevance labels, IoU-based agreement for bounding boxes, and exact match rates for reference answers. Additionally, we will include statistical significance tests (e.g., paired t-tests with p-values and effect sizes) for the key performance differences between visual, textual, and hybrid retrievers to support the central claims. revision: yes

  3. Referee: Evaluation Results (Table 2 or equivalent): The abstract states clear performance gaps between visual and textual retrievers, yet without access to full query construction details or variance estimates across the 3,099 queries, it is difficult to confirm that the gains are robust rather than artifacts of the specific 10-dataset mix.

    Authors: We concur that variance estimates and expanded query details are necessary for assessing robustness. The revision will add standard deviations and 95% confidence intervals for all reported metrics across the 3,099 queries. We will also provide a more comprehensive description of the query construction process, including the human verification steps, multi-language translation protocol, and criteria for ensuring diversity across the 10 datasets. These additions should help confirm that the observed gains are not artifacts of the dataset composition. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark evaluation with no fitted derivations

full rationale

The paper constructs a new multimodal RAG benchmark (ViDoRe v3) across 10 datasets with human annotations and reports direct empirical performance metrics for various retrievers and generators. No equations, parameter fits, or predictions are defined in terms of the target results. Claims about visual retriever superiority and model struggles are observational outcomes on the released corpus, not reductions to self-referential inputs or self-citations. Dataset selection bias is a validity concern but does not create circularity in the reported results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central contribution is empirical benchmark creation and evaluation rather than mathematical derivation, resting on assumptions about dataset representativeness and annotation reliability.

axioms (2)
  • domain assumption The 10 chosen datasets represent diverse professional domains and capture complex real-world RAG scenarios
    The benchmark construction and claims about real-world relevance depend on this selection.
  • domain assumption 12,000 hours of human annotation produce reliable ground truth for retrieval relevance, bounding box localization, and reference answers
    All reported performance numbers rest on the quality of these annotations.

pith-pipeline@v0.9.0 · 5556 in / 1420 out tokens · 60662 ms · 2026-05-16T14:41:57.294157+00:00 · methodology

discussion (0)

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Forward citations

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    cs.LG 2026-05 unverdicted novelty 6.0

    MINER fuses internal transformer layer representations via probing and adaptive sparse fusion to improve dense single-vector retrieval quality on visual documents by up to 4.5% nDCG@5 while preserving efficiency.