MultimodalReportBench Evaluation Framework

• MultimodalReportBench is a benchmark that defines a dataset and framework for generating integrated text and chart reports from scratch.
• It employs a Formal Description of Visualization (FDV) to convert visual elements into structured text for effective multimodal learning.
• The framework uses multi-dimensional evaluation metrics to assess report coherence, informativeness, and chart design with both LLM and human judgment.

A benchmark in the context of multimodal report generation, MultimodalReportBench refers specifically to the evaluation framework and dataset introduced in "Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework" (2506.02454). This resource is designed to rigorously assess the capabilities of automated systems to produce high-quality, text-chart interleaved reports from scratch, reflecting tasks essential to research, journalism, science, business, and policy communication.

1. Framework and Structure of MultimodalReportBench

MultimodalReportBench is attached to the agentic Multimodal DeepResearcher framework, which decomposes end-to-end report generation into four stages: researching, exemplar report textualization, planning, and multimodal report generation. The benchmark serves not only as a target evaluation set but also as a diagnostic resource that captures the challenges unique to integrating textual and visual information in document-scale outputs.

The benchmark consists of 100 diverse real-world topics covering 10 domains (e.g., technology, population, economy, education, social issues). Each topic is annotated and serves as a prompt for a system tasked with generating a comprehensive report including both explanatory text and charts.

2. Formal Description of Visualization (FDV) and Methodological Innovation

A key innovation underlying both the benchmark and report generation agent is the "Formal Description of Visualization" (FDV) format. FDV is a structured, semantically rich representation that encodes charts as textual entities, making them compatible with LLMs in both learning and generation. FDVs capture overall layout, plotting scales, data, and marks (the grammar of visualization construction), thereby bridging the gap between visual and language modalities.

During benchmark construction and evaluative generation, image charts in human-written reports are replaced with their FDV representations, enabling in-context learning by LLMs. When generating new reports, the system produces text interleaved with new FDV blocks, which are subsequently transformed into rendered figures (typically via D3.js and iterative code refinement).

This design directly addresses and evaluates a system’s grasp of not just factual information but also chart choice, design quality, and integration fidelity between narrative and visuals—elements that are critical for effective scientific and technical reporting.

3. Evaluation Criteria and Metrics

MultimodalReportBench introduces a multi-dimensional, five-metric evaluation protocol for holistic, granular assessment of generated reports. For each system output on a benchmark topic, reports are scored (most often by an LLM judge or human annotator) across:

1. Informativeness and Depth: Coverage and detail in both text and visualization, indicating thorough treatment of the topic.
2. Coherence and Organization: Logical structure, clarity of exposition, integration between narrative and charts, and effective visual placement within the flow.
3. Verifiability: Traceability of all claims and visualizations to credible references, preventing hallucination.
4. Visualization Quality: Design quality, including choice of chart types, clarity, readability (axes, labels, color), and correctness.
5. Visualization Consistency: Stylistic and informational uniformity across charts (color palettes, font hierarchy, thematic coherence).

Scores are given per metric on a 1–5 scale (with full rubric prompts in the appendix), with both automatic (LLM-based) and human evaluation. Aggregated win rates provide comparative summary statistics.

4. Benchmarked Results and State-of-the-Art Comparison

Experiments with Multimodal DeepResearcher and the DataNarrative baseline reveal the benchmark’s diagnostic strength:

• Using the same model backbone (Claude 3.7 Sonnet), Multimodal DeepResearcher achieved an 82% win rate over the baseline, with even larger margins in verifiability, visualization quality, and consistency.
• Ablation studies demonstrate the importance of each framework stage (researching, FDV-based in-context learning, planning, refinement loops): removing any stage markedly reduces report quality.
• Human judge experiments on a sample subset show 100% preference for DeepResearcher output on all criteria, indicating alignment between LLM-based and expert judgment.
• The system not only produces more informative text but also generates a greater diversity and quality of charts (including multi-panel, dashboard, and infographic formats), compared to simpler baselines often limited to bar/line plots.

5. Technical Implementation and Algorithmic Details

The generation pipeline and evaluation process in MultimodalReportBench involve several algorithmic components:

• Iterative Web Search and Learning Synthesis: Given a topic $t$, the framework performs multi-breath iterative searches, extracting learnings $L$ and relevant citations.
• Exemplar Textualization via FDV: Each chart from a set of exemplars is mapped to FDV via MLLM $M_v$, enabling structured transfer from human-authored reports.
• Outline and Style Guide Creation: Before generation, the agent plans narrative structure and visualization style, guided by exemplars.
• Actor–Critic Chart Refinement: For each FDV block, code is iteratively refined using an LLM (actor), rendered, and evaluated (critic) until quality objectives are met.
• LaTeX-style Pseudocode (simplified):

  \text{For each topic %%%%3%%%%:} L \gets \text{Research}(t) O, G \gets \text{Plan}(L, \text{FDV-Egs}) S \gets \text{Generate-Text-Report}(L, O, G) \{\text{FDV}_i\} \gets \text{Chart-Specs}(S) \text{For each FDV}_i: \text{Iterative Refinement (actor-critic loop)} \text{Output: Final text-chart report}

• Evaluation formulas: Reports are scored per metric, with system win rates aggregated as majority or mean over metrics.

6. Benchmark Significance, Limitations, and Future Directions

MultimodalReportBench establishes a new standard for agentic, professional-quality multimodal report evaluation by:

• Demonstrating the centrality of structured visualization knowledge transfer (FDV) for high-fidelity and stylistically consistent chart generation.
• Enabling rigorous, multidimensional scoring that captures not only text relevance but also visualization informativeness, chart design, and narrative-to-visual integration.
• Supporting both LLM-based and human evaluation, enabling reproducible large-scale assessment as well as qualitative expert analysis.

Limitations and potential areas for future work include:

• Addressing residual challenges in automatic chart layout (e.g., overlapping elements in dense FDVs), and further reducing hallucination in chart content.
• Improving efficiency and scaling frameworks to larger, more varied corpora, and extending support to more types of visual elements or templates.
• Enhancing systems’ ability for user-guided customization over both report structure and visualization (beyond fixed style guides).
• Integrating context-efficient in-context learning and fine-tuning for improved sample efficiency when handling large or diverse exemplars.
• Providing ongoing benchmark expansion with greater topic diversity, internationalization, and real-world deployment scenarios.

7. Access, Data, and Reproducibility

MultimodalReportBench is publicly released as both a dataset and evaluation framework, including:

• 100 benchmark topics, exemplars, FDV schemas, and prompts for both generation and evaluation.
• Human evaluation protocols, automatic GPT-4.1 judgment scripts, and ablation pipelines.
• All data, code, and auxiliary tools are made available to ensure reproducibility and encourage further research and comparison in agentic multimodal report generation.

References and details can be found in the original paper and its appendix. MultimodalReportBench thus provides an actionable and rigorous foundation for the next generation of multimodal document-generation systems and their evaluation.