Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation

• URL: http://arxiv.org/abs/2604.10741v2
• Date: Sun, 19 Apr 2026 17:04:51 GMT
• Title: Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation
• Abstract summary: Deep-Reporter is a unified agentic framework for grounded multimodal long-form generation.<n>We develop a rigorous curation pipeline producing 8K high-quality agentic traces for model optimization.<n>M2LongBench is a comprehensive testbed comprising 247 research tasks across 9 domains and a stable multimodal sandbox.
• Score: 42.800747398935044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centric, overlooking the multimodal evidence that characterizes real-world expert reports. We introduce a pressing task: multimodal long-form generation. Accordingly, we propose Deep-Reporter, a unified agentic framework for grounded multimodal long-form generation. It orchestrates: (i) Agentic Multimodal Search and Filtering to retrieve and filter textual passages and information-dense visuals; (ii) Checklist-Guided Incremental Synthesis to ensure coherent image-text integration and optimal citation placement; and (iii) Recurrent Context Management to balance long-range coherence with local fluency. We develop a rigorous curation pipeline producing 8K high-quality agentic traces for model optimization. We further introduce M2LongBench, a comprehensive testbed comprising 247 research tasks across 9 domains and a stable multimodal sandbox. Extensive experiments demonstrate that long-form multimodal generation is a challenging task, especially in multimodal selection and integration, and effective post-training can bridge the gap.

Related papers

• DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning [58.09293442211248]
  Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks.<n>This report presents DuMate-DeepResearch, a multi-agent DR framework built on the Qianfan Agent Foundry.
  arXiv  Detail & Related papers  (2026-06-05T14:10:48Z)
• 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)
• POINTS-Seeker: Towards Training a Multimodal Agentic Search Model from Scratch [84.73366911912512]
  We introduce Agentic Seeding, a dedicated phase designed to weave the precursors necessary for eliciting agentic behaviors.<n>We propose V-Fold, an adaptive history-aware compression scheme that preserves recent dialogue turns in high fidelity while folding historical context into the visual space via rendering.<n>We develop POINTS-Seeker-8B, a state-of-the-art multimodal agentic search model that consistently outperforms existing models across six diverse benchmarks.
  arXiv  Detail & Related papers  (2026-04-15T16:09:37Z)
• Towards Long-horizon Agentic Multimodal Search [109.0092257657625]
  We propose a novel Long-horizon MultiModal deep search framework, named LMM-Searcher, centered on a file-based visual representation mechanism.<n>We equip the agent with a tailored fetch-image tool, enabling a progressive, on-demand visual loading strategy for active perception.<n>Our method successfully scales to 100-turn search horizons, achieving state-of-the-art performance among open-source models.
  arXiv  Detail & Related papers  (2026-04-14T15:40:28Z)
• MTA-Agent: An Open Recipe for Multimodal Deep Search Agents [51.180338423927985]
  Multi-hop large language models (MLLMs) have demonstrated strong capabilities in visual understanding, yet they remain limited in complex, multi-step reasoning.<n>We propose a Multi-hop Tool-Augmented Agent for Evidence-based QA Synthesis (MTA-Agent)<n>MTA-Agent automatically selects tools and their parameters to retrieve and validate evidence from both visual and textual sources.
  arXiv  Detail & Related papers  (2026-04-07T19:01:45Z)
• VSearcher: Long-Horizon Multimodal Search Agent via Reinforcement Learning [22.27364585438247]
  VSearcher is a multimodal search agent capable of long-horizon, multi-turn tool use in real-world web environments.<n>We introduce Iterative Injection Data Synthesis pipeline to generate large-scale, complex multimodal QA questions.<n>We then adopt an SFT-then-RL training pipeline to turn base multimodal models to agent capable of multi-turn tool calling in real-world web environments.
  arXiv  Detail & Related papers  (2026-03-03T09:33:22Z)
• WideSeek: Advancing Wide Research via Multi-Agent Scaling [29.02742625120584]
  Wide Research is a paradigm essential for synthesizing and synthesizing comprehensive information under complex constraints in parallel.<n>We take a deep dive into Wide Research from two perspectives: Data Pipeline and Agent Optimization.<n>First, we produce WideSeekBench, a benchmark constructed via a rigorous multi-phase data pipeline to ensure diversity across the target information volume.<n>Second, we introduce WideSeek, a dynamic hierarchical multi-agent architecture that can autonomously fork parallel sub-agents based on task requirements.
  arXiv  Detail & Related papers  (2026-02-02T18:32:48Z)
• 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)
• M$^3$Searcher: Modular Multimodal Information Seeking Agency with Retrieval-Oriented Reasoning [8.546005018618713]
  M$3$Searcher is a modular multimodal information-seeking agent.<n>M$3$Searcher is optimized with a retrieval-oriented multi-objective reward.<n> MMSearchVQA is a multimodal multi-hop dataset to support retrieval centric RL training.
  arXiv  Detail & Related papers  (2026-01-14T08:27:40Z)
• FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation [57.577843653775]
  We propose textbfFindRec (textbfFlexible unified textbfinformation textbfdisentanglement for multi-modal sequential textbfRecommendation)<n>A Stein kernel-based Integrated Information Coordination Module (IICM) theoretically guarantees distribution consistency between multimodal features and ID streams.<n>A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance.
  arXiv  Detail & Related papers  (2025-07-07T04:09:45Z)
• Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines [63.22096609916707]
  Multi-modal Retrieval Augmented Multi-modal Generation (M$2$RAG) is a novel task that enables foundation models to process multi-modal web content.<n>Despite its potential impact, M$2$RAG remains understudied, lacking comprehensive analysis and high-quality data resources.
  arXiv  Detail & Related papers  (2024-11-25T13:20:19Z)
• PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents [93.55219461948529]
  PIN (Paired and INterleaved multimodal documents) is a novel data format designed to foster a deeper integration of visual and textual knowledge.<n>We construct and release two large-scale, open-source datasets: PIN-200M (200 million documents) and PIN-14M (14 million)
  arXiv  Detail & Related papers  (2024-06-20T01:43:08Z)

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.