LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning
Abstract
Joint audio-visual reasoning is essential for omnimodal understanding, yet current multimodal large language models (MLLMs) still struggle when reasoning requires fine-grained evidence from both modalities. A central limitation is that explicit text-based chain-of-thought (CoT) compresses continuous audio-visual signals into discrete tokens, weakening temporal grounding and shifting intermediate reasoning toward language priors. We argue that a unified latent space is a better medium for such reasoning because it preserves dense sensory information while remaining compatible with autoregressive generation. Based on this insight, we propose LatentOmni, a cross-modal reasoning framework that interleaves textual reasoning with audio-visual latent states. LatentOmni introduces feature-level supervision to align latent reasoning states with task-relevant sensory features and uses Omni-Sync Position Embedding (OSPE) to maintain temporal consistency between latent audio and visual states. We further construct LatentOmni-Instruct-35K, a dataset of audio-visual interleaved reasoning trajectories for supervising latent-space reasoning. Comprehensive evaluation across multiple audio-visual reasoning benchmarks demonstrates that LatentOmni achieves the best performance among the evaluated open-source models and consistently outperforms the Explicit Text CoT baseline, supporting latent-space joint reasoning as a promising path toward stronger omnimodal understanding.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of LatentOmni, a framework that enhances audio-visual reasoning by integrating textual and latent representations, leading to improved performance on multimodal reasoning tasks. This work addresses critical limitations in existing models and sets a foundation for future research in unified multimodal understanding.
Methodology
The paper introduces LatentOmni, a novel framework that integrates audio-visual reasoning within a unified latent space. The methodology is well-structured, emphasizing the importance of maintaining temporal consistency and grounding reasoning in sensory evidence. The introduction of feature-level supervision and Omni-Sync Position Embedding (OSPE) represents a significant advancement in addressing the limitations of traditional text-based chain-of-thought (CoT) methods. The approach is innovative, as it combines textual reasoning with continuous latent states, allowing for a more nuanced understanding of multimodal interactions.
Results & evaluation
The authors conduct extensive experiments across multiple benchmarks, demonstrating that LatentOmni outperforms existing models, including both open-source and proprietary systems. The evaluation metrics are comprehensive, covering various reasoning tasks that highlight the framework's strengths in audio-visual alignment and commonsense reasoning. The results are statistically significant and provide strong evidence for the effectiveness of the proposed method.
Reproducibility
The paper provides detailed implementation details, including the training process, dataset construction, and hyperparameter settings. However, the absence of a publicly available project URL limits the reproducibility of the results, as external researchers cannot directly access the code or datasets used in the experiments.
Limitations
While the framework shows promise, it shares common limitations with current multimodal systems, particularly in terms of modality coverage. The authors acknowledge the challenge of extending the framework to incorporate additional sensory modalities beyond audio and visual, which could limit its applicability in more complex real-world scenarios.
Broader impact
The implications of this work are significant for fields requiring robust multimodal understanding, such as robotics, autonomous systems, and human-computer interaction. By improving the reasoning capabilities of models in a unified latent space, the research could enhance applications in video analysis, interactive AI systems, and assistive technologies. The main contribution of this paper is the development of LatentOmni, a framework that enhances audio-visual reasoning by integrating textual and latent representations, leading to improved performance on multimodal reasoning tasks. This work addresses critical limitations in existing models and sets a foundation for future research in unified multimodal understanding.
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