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Multimodal Deep Research

Available Now · Sample Available

Multimodal Deep Research

Multimodal research trajectories with image analysis and tool calling.

Volume

0

Volume

Scalable Volume

Coverage

Research

Model Benchmark
0.0 %

Claude Opus 4.6 · Pass at k

Targets

N/A

Modality & Delivery

ImageText→Text

Formats

JSONL (.jsonl)Images

What You Receive

Complete multimodal multi-hop research trajectories combining image analysis, tool calling, and embedded image understanding with text-based search. Built for MM-BrowseComp and OmniGAIA.

Coverage

Package Structure

Differentiators

  • Combines image analysis and embedded-image understanding with text search and tool calling.

Record Schema

FieldDescription
final_reportThe reference research deliverable produced from the trajectory.
image_refsPaths to the figures, charts, or documents the task depends on.
rubricThe normalized scoring rubric used to grade reports.
taskThe multimodal research question or brief assigned to the agent.
trajectoryThe expert research trajectory: plans, searches, source evaluations, and resolutions.

Results

SubjectTypeMetricResult
Claude Opus 4.6Pass@50%
0% Pass@5 on both supplied tasks.
Reference trajectoriesTrajectory rewardMean outcome reward83.5%
Mean outcome reward across the two successful reference trajectories.

Methodology & Quality

Methodology

SME involvement

Domain-expert curated with up to L2 review

Stressors

Cross-modal evidence gathering and tool-mediated research.

Design criteria

  1. Capture complete multimodal multi-hop research trajectories.

Build assumptions

Quality approach

Known limitations

Customization & Scalability

Custom sets available. Additional volume in progress