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

2026-05-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

LatentOmni is a novel cross-modal reasoning framework designed to enhance multimodal large language models' (MLLMs) ability to perform fine-grained audio-visual reasoning. It addresses the limitations of explicit text-based Chain-of-Thought (CoT), which often loses temporal grounding and relies on language priors by compressing continuous audio-visual signals into discrete tokens. LatentOmni proposes a unified latent space that interleaves textual reasoning with audio-visual latent states, preserving dense sensory information. Key innovations include feature-level supervision to align latent reasoning states with task-relevant sensory features and Omni-Sync Position Embedding (OSPE) for temporal consistency. The framework also introduces LatentOmni-Instruct-35K, a dataset of 35,000 audio-visual interleaved reasoning trajectories. Evaluations show LatentOmni achieves the best performance among open-source models on multiple audio-visual reasoning benchmarks, significantly outperforming the Explicit Text CoT baseline.

Key takeaway

For AI Scientists and Machine Learning Engineers developing advanced multimodal systems, LatentOmni demonstrates that shifting reasoning into a unified latent space significantly improves fine-grained audio-visual understanding. You should explore architectures that interleave textual and latent reasoning, leveraging feature-level supervision and temporal alignment techniques like OSPE. This approach can mitigate information loss from discrete tokenization, leading to more robust and grounded cross-modal inference in your models.

Key insights

Unified latent space reasoning preserves dense audio-visual information, outperforming text-centric Chain-of-Thought.

Principles

• Continuous latent space retains fine-grained sensory details.
• Interleaving text and latent states balances logic and evidence.
• Feature-level supervision grounds latent reasoning.

Method

LatentOmni alternates textual deduction with continuous latent reasoning phases, using special tokens to switch between discrete vocabulary and a unified latent space, supported by feature-level supervision and OSPE.

In practice

• Develop datasets with audio-visual interleaved reasoning trajectories.
• Integrate feature-level supervision for latent state alignment.
• Employ Omni-Sync Position Embedding for temporal consistency.

Topics

• Multimodal Large Language Models
• Audio-Visual Reasoning
• Latent Space Reasoning
• Chain-of-Thought
• Feature-Level Supervision
• Omni-Sync Position Embedding
• LatentOmni-Instruct-35K Dataset

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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• FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs — FutureOmni is the first benchmark and training strategy to evaluate and improve MLLM future forecasting from audio-visual context.
• Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective — A new theoretical perspective clarifies cross-modal contributions in continual vision-language models, addressing catastrophic forgetting.
• What is Holding Back Latent Visual Reasoning? — Latent tokens in VLMs currently offer minimal causal impact due to dataset limitations and poor inference-time generation.
• Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models — Multimodal XAI evaluation lacks standardization, hindering interpretable and responsible AI development.
• SeLaR: Selective Latent Reasoning in Large Language Models — SeLaR improves LLM reasoning by selectively applying soft embeddings and regularization to enhance exploration at low-confidence steps.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.