Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model
Researchers benchmark MiniMax-H3 omni-modal generation on physical world reasoning across 517 multimodal tasks, finding 41.97% overall success and weak audio-based disambiguation.
The paper introduces an evaluation framework for physical world reasoning in Omni-Modal Generative Models, applied to MiniMax-H3, which combines multimodal understanding with joint audio-visual generation. Tasks span four scenarios: implicit prompts paired with multiple frames, audio-image, prefix-videos, and audio-video inputs, with each modality providing only partial evidence. Across 517 evaluation instances, MiniMax-H3 achieves a 41.97% overall success rate, peaking at 56.00% on video-based decision reasoning and dropping to 27.40% on audio-based disambiguation. The project is available on GitHub.
- Evaluation spans four scenarios: implicit prompts with frames, audio-image, prefix-videos, and audio-video inputs.
- MiniMax-H3 scores 41.97% overall; video-based decision reasoning 56.00%, audio-based disambiguation 27.40%.
- Vendors
- MiniMax
- AI models
- MiniMax-H3
From the original article
1 min read at huggingface.co
Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework.
By Haoyu Zhao, Zihao Zhao, Tianyu Deng · huggingface.co
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