Multimodal DeepResearcher Framework

• Multimodal DeepResearcher is an agent-based framework that automates research by integrating text, images, charts, and videos using large language models.
• It employs specialized agents for reasoning, retrieval, and content generation, utilizing formal visualization representations like FDV for synthesis.
• The framework’s modular, iterative pipeline enhances output quality and performance, outperforming traditional systems in multimodal research benchmarks.

Multimodal DeepResearcher encompasses a class of agent-based frameworks and methodologies that enable automated, end-to-end research and composition over multimodal data sources—including text, images, charts, and, in emerging instances, long videos—by leveraging recent advances in LLMs, multimodal retrieval and perception, coordinated planning, and the seamless integration of multiple content-generation and analytic modules. These systems are designed to autonomously gather, analyze, and synthesize complex information across modalities, frequently producing interleaved textual, visual, and data-driven outputs, such as comprehensive reports and answers to open-ended queries.

1. Agentic Architectures for Multimodal Research

Multimodal DeepResearcher frameworks operationalize the concept of an "agentic" research assistant by employing LLM-based agents that orchestrate problem-solving across multiple specialized tools and modalities. Core architectural elements include:

• Reasoning Agent: Typically an LLM that manages explicit multi-step planning, decomposing primary tasks (e.g., generating a research report) into subtasks: researching, planning, drafting, and coordinating visualization.
• Web/Corpus Search Tool: Structured search modules retrieve relevant text, images, tables, or video segments from the open web or large internal corpora using keyword-based or semantic search (e.g., top-k retrieval using transformer embeddings).
• Browsing and Perception Agents: Specialized modules process rich, unstructured modalities (webpages, visualizations, videos) by extracting relevant structured representations (e.g., Formal Description of Visualization [FDV] or video frames).
• Memory and Iteration: A short-term memory tracks acquired information and incremental outputs, enabling iterative refinement of research outputs through cycles of reasoning, retrieval, and analysis.
• Coordinated Generation: Text-chart report writing, chart code generation, and iterative error correction are orchestrated via an actor-critic agentic loop, with LLMs and multimodal evaluators (LLM-Vision modules) collaborating for quality control.

This modular, multi-agent design is exemplified by the frameworks described in "DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments" (2504.03160), "Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework" (2506.02454), and "VideoDeepResearch: Long Video Understanding With Agentic Tool Using" (2506.10821).

2. Multistage Research and Synthesis Pipeline

Multimodal DeepResearcher systems consistently decompose the research process into well-defined stages, each tailored for robust multimodal synthesis:

1. Researching: The LLM-agent generates initial search queries based on the target topic, retrieves data from multimodal sources (text, images, videos), and synthesizes preliminary findings. Iterative querying and aggregation ensure greater coverage and accuracy of retrieved information.
2. Exemplar Ingestion and Planning: Exemplar multimodal documents (e.g., human-written reports with charts) are transformed into structured representations (e.g., FDVs for visualizations), enabling LLMs to learn multimodal composition patterns. The system plans section structure, visualization placement, and stylistic guidelines based on both the research objectives and provided exemplars.
3. Content and Visualization Generation: The agentic system generates interleaved text and multimodal content. Visualizations are represented in standardized formats (FDVs), which are further refined through code generation (e.g., D3.js, Python/Matplotlib), rendering, and critique loops.
4. Evaluation and Iterative Refinement: Automated and human-in-the-loop evaluation mechanisms critique output quality along multiple axes (informativeness, coherence, verifiability, visualization quality, and consistency). Feedback is integrated to iteratively improve and finalize the multimodal report or research product.
5. Aggregation for Structured Answering: For structured queries or analytic tasks, the system aggregates intermediate answers using aggregation modules (e.g., summing, maximal selection over multiple retrieved modalities), as in multimodal neural database architectures (2305.01447).

3. Multimodal Content Integration: Representation and Formalization

Key to the success of Multimodal DeepResearcher is the formalization and unification of content across modalities:

• Formal Description of Visualization (FDV): A structured, four-part textual representation aligned with the Grammar of Graphics, capturing the complete structure, styling, data, and semantics of visualizations. FDV enables LLMs to parse, generate, and refine visualizations as natively as narrative text, and to integrate visual content in in-context learning and generative chains (2506.02454).
• Multimodal Retrieval: Modular retrievers (text, image, subtitle, video clip retrievers) identify contextually relevant information. In long-video understanding, specialized retrievers enable the system to focus attentively on salient segments, mitigating context window limitations (2506.10821).
• Tool-Oriented, Decoupled Processing: Rather than endowing a single model with all reasoning and perception, agentic frameworks decouple tasks—allowing, for example, GPT-class models to drive reasoning while specialized vision-LLMs process images, video, or chart code.

This design supports breadth, allowing integration of arbitrarily many or new modalities as specialized perceivers and formal descriptions are developed.

4. Evaluation Measures and Empirical Findings

Multimodal DeepResearcher systems are evaluated through both automatic and human-centric metrics, typically on specialized benchmarks (e.g., MultimodalReportBench):

• Automatic Evaluation: LLM-as-a-judge systems (e.g., GPT-4.1, Claude 3.7) compare entire multimodal reports across dedicated criteria. Key metrics:
  • Informativeness & Depth
  • Coherence & Organization
  • Verifiability (evidence and citation quality)
  • Visualization Quality (design and labeling)
  • Consistency (style, clarity)
• Human Evaluation: Expert raters compare multistage framework outputs with strong baselines on the same criteria, reporting win rates and preference rankings.
• Benchmark Datasets: 100-topic collections with human-written multimodal exemplars, covering diverse domains.
• Empirical Outcomes: The agentic Multimodal DeepResearcher achieves:
  • 82% overall win rate over prior baselines using Claude 3.7 (LLM evaluation)
  • 100% preference in sample human evaluation scenarios
  • Increased diversity and quality of generated visualization types compared to prior DataNarrative and RAG systems

Results also show that removing key components (exemplar scheme, structured planning, chart refinement) substantially reduces win rates, indicating the critical role of these stages.

In long video QA and summarization (2506.10821), the agentic framework surpasses context-extended MLLMs (e.g., GPT-4o, Gemini-1.5-Pro) by up to 9.6% on prominent benchmarks, demonstrating the advantage of strategic, tool-based reasoning over domain scaling alone.

5. Innovations and Distinguishing Features

Multimodal DeepResearcher introduces several innovations that distinguish it from previous multimodal learning frameworks:

• Agentic Reasoning Paradigm: Explicit division of tasks—reasoning, planning, retrieval, content generation—mirroring the workflow of human researchers.
• Formal Representation for Visual Content: FDV and equivalent schemes enable standardized, in-context visualization handling.
• Iterative Co-Critique: Multi-round, agent-in-the-loop error detection and resolution for visualization correctness and integration, often employing “actor-critic” structures combining LLM text/code synthesis and VLM-based critique.
• Tool-Oriented Decoupling for Multimodal Analytics: Instead of end-to-end, context-hungry MLLMs, research tasks are decomposed for efficiency and performance, leveraging best-in-class modality-specific engines.

These innovations support both performance and extensibility to new tasks and modalities.

6. Applications and Future Directions

The Multimodal DeepResearcher framework is applicable across a range of domains:

• Professional and Scientific Reporting: Automated generation of analysis and presentation-ready materials, integrating narrative text, statistical charts, and supporting evidence.
• Long Video Understanding and Summarization: Agentic LVU (e.g., VideoDeepResearch) for benchmarks and real-world applications where only relevant video segments are retrieved and fully processed.
• Personal Knowledge Companions: Natural language querying over unstructured, multimodal lifelogs, combining retrieval, reasoning, and aggregation (as in Multimodal Neural Databases).
• Education, Journalism, Policy: Scalable production and evaluation of multimodal explanatory content.
• Human-Centric AI Assistants: Foundations for DST-based systems reasoning over, and synthesizing, multimodal information in interactive settings.

Future improvements highlighted in the literature include:

• Expanding FDV-style formalizations to cover more visualization and interactivity types
• Adapting to dynamic, online research environments, including data streaming
• Integrating stronger error detection, hallucination minimization, and safety/abstention protocols
• Automatic tool selection, scheduling, and multi-agent collaboration
• Scaling to broader modalities (audio, video, code, sensor data), and multimodal dialogue settings
• Standardizing evaluation via MultimodalReportBench and related benchmarks for complex, interleaved output

This suggests that Multimodal DeepResearcher frameworks will underpin future advances in both automated scientific discovery and robust, transparent AI knowledge work.