ViDR: Grounding Multimodal Deep Research Reports in Source Visual Evidence

• URL: http://arxiv.org/abs/2605.13034v1
• Date: Wed, 13 May 2026 05:39:38 GMT
• Title: ViDR: Grounding Multimodal Deep Research Reports in Source Visual Evidence
• Abstract summary: We present ViDR, a framework that grounds long-form reports in source figures.<n>ViDR treats source figures as retrievable, interpretable, routable, and verifiable evidence objects.<n>We also introduce MMR Bench+, a benchmark for evaluating visual evidence use in deep research reports.
• Score: 1.6789785654745923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reasoning. However, most text-centered systems rely mainly on textual evidence, while multimodal systems often retrieve images only weakly or generate charts themselves, leaving source figures underused as evidence. We present ViDR, a multimodal deep research framework that grounds long-form reports in source figures. ViDR treats source figures as retrievable, interpretable, routable, and verifiable evidence objects, while still generating analytical charts when needed. It builds an evidence-indexed outline linking claims to textual and visual evidence, refines noisy web images into source-figure evidence atoms through context-aware filtering, outline-aware reranking, and VLM-based visual analysis, and generates each section with section-specific evidence. ViDR further validates visual references to reduce hallucinated or misplaced figures. We also introduce MMR Bench+, a benchmark for evaluating visual evidence use in deep research reports, covering source-figure retrieval, placement, interpretation, verifiability, and analytical chart generation. Experiments show that ViDR improves overall report quality, source-figure integration, and verifiability over strong commercial and open-source baselines. These results suggest that source visual evidence is important for multimodal deep research, as it strengthens evidential grounding, visual support, and report verifiability.

Related papers

• Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation [50.92223196290564]
  We propose a unified agent loop for explicit evidence selection and consolidation.<n>During exploration, SCoRE retains only query-relevant observations and their source pointers in a maintained textual ledger.<n>At termination, it reloads the referenced original images and consolidates the visual evidence for answering, arranging it into a logical sequence.
  arXiv  Detail & Related papers  (2026-09-14T16:11:35Z)
• HiEviDR-Bench: A Benchmark for Hierarchical Evidence Aggregation in Deep Research [52.87128503345243]
  We introduce HiEviDR-Bench, a benchmark for evaluating Hierarchical Evidence Aggregation in Deep Research.<n>HiEviDR-Bench represents each instance with an explicit evidence graph that captures evidence selection, cross-source linking, and aggregation.<n>We develop a traceability-oriented evaluation framework with five dimensions: report quality, evidence traceability, citation accuracy, claim verification, and answer correctness.
  arXiv  Detail & Related papers  (2026-07-27T23:50:46Z)
• TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation [63.89818340842003]
  TVIR (Text--Visual Interleaved Report Generation) is a benchmark of 100 expert-curated multimodal deep research tasks.<n>TVIR-Agent is a hierarchical multi-agent framework that serves as a strong baseline for constructing outlines and retrieving images.<n>We develop a dual-path evaluation framework that combines Textual Assessment and Visual Assessment.
  arXiv  Detail & Related papers  (2026-06-01T14:35:14Z)
• Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation [74.0621258662676]
  We propose Ptah, a multi-agent harness for interleaved report generation.<n>Ptah orchestrates the lifecycle from user query to rendered web report through planning, research, and writing stages.<n>A verifier agent serves as the harness's acceptance function, enforcing factual grounding, citation fidelity, and cross-modal consistency throughout the workflow.
  arXiv  Detail & Related papers  (2026-05-28T12:40:34Z)
• MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents [37.98503734345155]
  We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains.<n>Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use.<n>We propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity.
  arXiv  Detail & Related papers  (2026-01-18T10:41:33Z)
• DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection [6.225860651499494]
  Multimodal fake news detection is crucial for mitigating adversarial misinformation.<n>We propose DIVER (Dynamic Iterative Visual Evidence Reasoning), a framework grounded in a progressive, evidence-driven reasoning paradigm.<n>Experiments on Weibo, Weibo21, and GossipCop demonstrate that DIVER outperforms state-of-the-art baselines by an average of 2.72%.
  arXiv  Detail & Related papers  (2026-01-12T04:01:33Z)
• VisRAG 2.0: Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation [64.82775032985485]
  Visual retrieval-augmented generation (VRAG) augments vision-language models (VLMs) with external visual knowledge to ground reasoning and reduce hallucinations.<n>Yet current VRAG systems often fail to reliably perceive and integrate evidence across multiple images, leading to weak grounding and erroneous conclusions.<n>We propose EVisRAG, an end-to-end framework that learns to reason with evidence-guided multi-image to address this issue.
  arXiv  Detail & Related papers  (2025-10-10T13:34:23Z)
• WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research [73.58638285105971]
  This paper tackles textbfopen-ended deep research (OEDR), a complex challenge where AI agents must synthesize vast web-scale information into insightful reports.<n>We introduce textbfWebWeaver, a novel dual-agent framework that emulates the human research process.<n>Our framework establishes a new state-of-the-art across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and DeepResearchGym.
  arXiv  Detail & Related papers  (2025-09-16T17:57:21Z)
• Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework [22.366142327629486]
  Multimodal DeepResearcher decomposes the task into four stages: researching, report textualization, planning, and multimodal report generation.<n>It achieves an 82% overall win rate over the baseline method.
  arXiv  Detail & Related papers  (2025-06-03T05:18:19Z)
• Unstructured Evidence Attribution for Long Context Query Focused Summarization [53.08341620504465]
  We propose to extract unstructured (i.e., spans of any length) evidence in order to acquire more relevant and consistent evidence than in the fixed granularity case.<n>We show how existing systems struggle to copy and properly cite unstructured evidence, which also tends to be "lost-in-the-middle"
  arXiv  Detail & Related papers  (2025-02-20T09:57:42Z)
• VISA: Retrieval Augmented Generation with Visual Source Attribution [100.78278689901593]
  Existing approaches in RAG primarily link generated content to document-level references.<n>We propose Retrieval-Augmented Generation with Visual Source Attribution (VISA), a novel approach that combines answer generation with visual source attribution.<n>To evaluate its effectiveness, we curated two datasets: Wiki-VISA, based on crawled Wikipedia webpage screenshots, and Paper-VISA, derived from PubLayNet and tailored to the medical domain.
  arXiv  Detail & Related papers  (2024-12-19T02:17:35Z)
• Multimodal Misinformation Detection using Large Vision-Language Models [7.505532091249881]
  Large language models (LLMs) have shown remarkable performance in various tasks. Few approaches consider evidence retrieval as part of misinformation detection. We propose a novel re-ranking approach for multimodal evidence retrieval.
  arXiv  Detail & Related papers  (2024-07-19T13:57:11Z)
• RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection [17.107961913114778]
  We introduce a "Relevant Evidence Detection" (RED) module to discern whether each piece of evidence is relevant. RED-DOT achieves significant improvements over the state-of-the-art (SotA) on the VERITE benchmark by up to 33.7%. Our evidence re-ranking and element-wise modality fusion led to RED-DOT surpassing the SotA on NewsCLIPings+ by up to 3%.
  arXiv  Detail & Related papers  (2023-11-16T14:43:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.