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Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model

Haoyu Zhao, Zihao Zhao, Tianyu Deng, Ziqin Xu, Zihao Zhang, Xudong Wang, Jinxiang Guo, Chen Gao, Ziyi Ye, Yeying Jin, Jiaxi Gu, Zuxuan Wu, Shuicheng Yan

arXiv:2609.18323v1cs.CV

TL;DR

Existing evaluations often specify target content in prompts, leaving limited insight into whether Omni-Models infer events from complementary evidence. This paper evaluates MiniMax-H3 with implicit multimodal prompts across four physical-world reasoning scenarios. It reports 41.97% overall success, with video-based decisions strongest and audio-based disambiguation weakest.

  • Problem

    Benchmarks with explicit target descriptions reveal limited evidence about whether models infer events from distributed multimodal evidence.

    Source: §1 · p. 2

  • Method

    The evaluation omits critical event semantics from prompts, distributes evidence across modalities, and assesses recovery through generated video across four reasoning settings.

    Source: §1 · pp. 2–3

  • Results

    41.97% overall success; Video-based Decision Reasoning reaches 56.00%, while Audio-based Disambiguation Reasoning reaches 27.40%.

    Source: §1 · p. 3

  • Takeaways & Limitations

    Multimodal input support does not ensure reliable evidence use, particularly for acoustic grounding or coordination across multiple views.

    Source: §4.5 · p. 14

    Generation-based evaluation combines input understanding, cross-modal integration, and video-generation performance, so failures may arise at different stages.

    Source: §4.5 · p. 14

Abstract

from arXiv · show

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.

1 Introduction

The paper asks whether Omni-Models can infer events from complementary multimodal evidence when each modality is incomplete, a question not resolved by benchmarks centered on explicit prompts. It evaluates MiniMax-H3 with implicit prompts and four physical-world reasoning settings, finding that multimodal input support does not guarantee reliable task completion.

  • Existing benchmarks often use prompts that specify the target, revealing less about whether models infer events from distributed multimodal evidence.The paper contrasts rendering a specified event with determining what to generate from incomplete observations.Source: §1 · p. 2
  • The framework omits critical event semantics from prompts and distributes the missing information across modalities, assessing recovery through generated-video consistency.The generated video is a behavioral readout of whether the model recovered semantics rather than merely followed an explicit instruction.Source: §1 · p. 2
  • The four settings test cross-view association, audio-based disambiguation, temporal decisions from prefix video, and integrated audiovisual reasoning.Together they examine spatial, semantic, temporal, and audiovisual evidence.Source: §1 · p. 3
  • The paper introduces a video-generation, continuation, and editing framework and an expert-verified set of 517 instances across four scenarios and 29 subcategories.Each instance pairs observations with an implicit prompt and annotated semantic target for human evaluation against task-specific success criteria.Source: §1 · p. 3
  • 41.97% overall success; Video-based Decision Reasoning leads at 56.00%, while Audio-based Disambiguation Reasoning is lowest at 27.40%.The results expose a gap between multimodal input support and reliable task completion.Source: §1 · p. 3

2 Related Work

Related work evaluates video generation, reasoning through generated sequences, and world models, but the paper frames its focus as inferring what event or response to generate from information distributed across inputs.

  • Video-generation evaluation covers perceptual quality, temporal compositionality, and physical faithfulness, while some work tests prediction from observed frames or audiovisual consistency.The paper positions its evaluation as connecting these directions by assessing whether observations support an inference expressed through video generation.Source: §2 · p. 3
  • Reasoning-through-video benchmarks probe visual, symbolic, planning, and implicit world-rule capabilities, while this work emphasizes task-relevant information distributed across inputs.Its prompts leave the target inference unstated, requiring cross-view correspondence and temporal inference.Source: §2 · p. 3
  • World-model benchmarks test spatial structure, control alignment, consistency, or action-dependent transitions; this framework asks how observations determine the event or response to generate.Success is whether the generated outcome satisfies semantic requirements supported by input evidence.Source: §2 · p. 4

3 Reasoning Evaluation with MiniMax-H3

The evaluation uses MiniMax-H3 to test whether complementary multimodal observations support physical-world inferences expressed through video generation, continuation, or editing. Its tasks distribute evidence across views, audio, and video dynamics, and its data pipeline pairs implicit prompts with annotated targets reviewed by experts.

3.1 What Do Omni-Models Enable?

  • Images, video, audio, and text contribute complementary scene information, enabling prompts to leave target behavior unspecified for inference from observations.Generated motion and state transitions make the model's interpretation observable.Source: §3.1 · p. 4

3.2 Evaluation on Physical-World Reasoning Tasks

  • Multi-view Spatial Reasoning requires cross-view correspondences and inference of spatial relationships not fully observable in any single image.The generated video is evaluated against spatial relationships jointly supported by the views.Source: §3.2 · p. 5
  • Audio-based Disambiguation Reasoning uses acoustic cues to resolve a static image's ambiguity and generate video consistent with the interpretation supported by both modalities.The task tests whether audio informs interpretation of an otherwise ambiguous visual scene.Source: §3.2 · p. 5
  • Video-based Decision Reasoning infers a response from motion, state changes, and interactions in a prefix, without an explicit action specification.The continuation should account for plausible consequences while remaining consistent with scene dynamics.Source: §3.2 · p. 5
  • Audiovisual Integrated Reasoning uses audio or spoken constraints with a video prefix or full sequence to guide continuation or corrective editing.Audio need not be temporally aligned; the output should incorporate relevant audio while remaining consistent with the video.Source: §3.2 · p. 5

3.3 Evaluation Data Construction and Human Annotation

  • The data pipeline selects sources, constructs task-specific instances and prompts, and iteratively reviews condition–prompt pairs for evidence sufficiency and implicitness.Accepted instances pair observations with an implicit prompt and annotated semantic target; the set contains 517 pairs across four domains and 29 subdomains.Source: §3.3 · pp. 6–7

4 Experiments

Experiments assess whether MiniMax-H3’s generated videos satisfy spatial, temporal, and audiovisual constraints implied by multimodal inputs. The evaluation combines quantitative success rates with qualitative analysis of successes and failures.

4.1 Evaluation Data and Protocol

  • The 517-instance evaluation set spans four scenarios and 29 subcategories, with MSR, VDR, ADR, and AVIR covering 200, 100, 146, and 71 instances, respectively.Source: §4.1 · p. 7, Figure 4 · p. 7
  • Three experts independently judge generated videos against task-specific requirements rather than a single reference, with success rate counting the percentage of successful samples.Source: §4.1 · pp. 7–9

4.2 Quantitative Results

  • 41.97% overall SR across 517 instances; VDR leads at 56.00%, followed by AVIR at 47.89%, MSR at 43.50%, and ADR at 27.40%.Source: Table 2 · p. 9, §4.2 · p. 9
  • Because scenarios differ in data, prompts, and generation targets, their scores primarily reflect task-level performance rather than a direct comparison between audio and video modalities.Source: §4.2 · p. 10
  • Threading reaches 75.00% and pouring 58.30% in MSR, while VDR puzzles and cartoons fall to 16.67% and 20.00%, respectively.Source: §4.2 · p. 10

4.3 Qualitative Analysis

  • Representative outputs include recognizable manipulation stages, plausible short-term dynamics, and audio-guided visual events such as cabinet opening and typewriter-related paper motion.Source: §4.3 · p. 11

4.4 Failure Cases

  • Recurring failures include incorrect evidence grounding, incomplete event realization, physical or configurational violations, and temporal state inconsistencies.Source: §4.4 · pp. 11–12

4.5 Discussion

  • Visual plausibility does not ensure physical-world consistency, and supported input modalities are not always used reliably, especially for acoustic grounding and coordination across views.Source: §4.5 · pp. 12, 14

5 Conclusion

The MiniMax-H3 evaluation finds both promise and limitations in physical-world reasoning: complementary multimodal evidence can support reasoning beyond explicit instructions, but overall performance remains limited. The authors propose expanding the evaluation set and developing automated assessment.

5 Conclusion

  • The evaluation finds that complementary multimodal evidence can support reasoning beyond explicit instructions, but overall performance remains limited.The results reveal a substantial gap between omni-modal input support and effective physical-world reasoning.Source: §5 · p. 14
  • Implicit condition–prompt pairs distribute critical information across modalities to test whether MiniMax-H3 integrates evidence to infer event states, physical dynamics, and outcomes.The evaluation examines physical-world reasoning through multimodal generation rather than fully specified prompts.Source: §5 · p. 14
  • The authors plan to expand the evaluation set across more scenarios, modality combinations, and reasoning requirements, and develop automated assessment for scalable, reproducible testing.Source: §5 · p. 14
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