Unified Intelligence
Across Modalities.

Training models to perceive and reason through the world as humans do. We specialize in cross-modal alignment and high-fidelity video understanding.

Request Multimodal Data View Benchmarks

Our benchmarks test for temporal consistency in video, complex document layouts, and high-fidelity auditory grounding.

Datasets for complex
Multi-Sensor Fusion.

Expert-verified data designed to improve cross-modal reasoning and perception in the next generation of VLMs.

Detailed event-trace data for long-form video understanding, temporal consistency, and action recognition.

High-density layout data for scanned documents, technical charts, and complex scientific diagrams.

Synchronized data for audio-visual emotional analysis and environment sound grounding for robotic agents.

Evaluating the limits of cross-modal reasoning and perception in Vision-Language Models (VLMs).

Massive Multi-discipline Multimodal Understanding testing college-level logic across 30 subjects.

The industry standard for long-video evaluation, testing temporal consistency and multi-event reasoning.

Specialized suite for evaluating complex auditory reasoning, sound classification, and musical analysis.

FAQ

Understanding our multimodal evaluation protocols and data quality standards.

We use a hybrid approach combining expert human annotation with multi-agent consensus verification for every cross-modal data point.

MMMU focuses on college-level reasoning across 30 subjects, whereas standard VQA often focuses on basic object identification.