LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning

• URL: http://arxiv.org/abs/2605.22012v1
• Date: Thu, 21 May 2026 05:18:57 GMT
• Title: LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning
• Abstract summary: 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.<n>Based on this insight, we propose textbfLatent Omni, a cross-modal reasoning framework that interleaves textual reasoning with audio-visual latent states.
• Score: 31.98142661908727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \textbf{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 \textbf{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.

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