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%.
Full article241 words · extracted from huggingface.co · click to collapse
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. Its unified architecture raises a fundamental question: Can multimodal alignment improve the model's world reasoning, and what new evaluation paradigms do omni-modal inputs enable? To investigate this question, this work introduces a comprehensive evaluation framework organized around four complementary dimensions of physical world reasoning. Unlike existing evaluation frameworks for video generation and world models, which are often constrained by limited input modalities and evaluation settings where prompts closely match the target video content, our evaluation is specifically designed to exploit the multimodal inputs of Omni-Model. We construct a diverse set of novel tasks that require models to integrate complementary information across modalities. Specifically, we consider four scenarios, including implicit prompts paired with multiple frames, audio-image, prefix-videos, and audio-video inputs. Every single modality provides only partial evidence about the underlying event, requiring the model to jointly reason over the complementary semantic cues to infer latent event states and future dynamics. Across 517 evaluation instances, MiniMax-H3 achieves an overall success rate of 41.97%. Video-based Decision Reasoning yields the highest success rate at 56.00%, while Audio-based Disambiguation Reasoning is the weakest, reaching only 27.40%. These results indicate that effective multimodal integration remains key to fully exploiting the benefits of diverse input modalities. The project is available at https://github.com/gulucaptain/MiniMax-H3-Reason.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.18323