Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

Omni-modal Large Language Models (Omni-MLLMs) currently suffer from a performance paradox where unimodal baselines often outperform joint multimodal inference. This fragility stems from static fusion topologies, specifically positional bias in sequential inputs and alignment traps in interleaved formats, which distort attention. To address this, the Chain of Modality (CoM) framework introduces dynamic orchestration of multimodal fusion. CoM adaptively switches between parallel, sequential, and interleaved input pathways to mitigate structural biases. It also bifurcates cognitive execution into a "Direct-Decide" path for direct perception and a "Reason-Decide" path for analytical auditing. CoM operates in either a training-free or data-efficient SFT setting, demonstrating robust generalization across various benchmarks.

Key takeaway

For AI Engineers developing Omni-MLLMs, you should re-evaluate current static fusion approaches. Consider implementing dynamic orchestration frameworks like Chain of Modality to overcome performance paradoxes caused by positional bias and alignment traps. This shift can lead to more robust and consistent generalization, improving your model's real-world applicability without extensive retraining.

Key insights

Static fusion in Omni-MLLMs causes performance paradoxes, necessitating dynamic orchestration for robust multimodal integration.

Principles

• Static fusion topologies introduce perceptual fragility.
• Dynamic orchestration neutralizes structural biases.
• Bifurcated cognitive paths improve task alignment.

Method

CoM dynamically orchestrates input topologies (parallel, sequential, interleaved) and bifurcates cognitive execution into "Direct-Decide" and "Reason-Decide" paths for task-aligned processing.

In practice

• Implement dynamic input topology switching.
• Separate direct perception from analytical auditing.
• Explore training-free or SFT settings for CoM.

Topics

• Omni-MLLMs
• Chain of Modality
• Multimodal Fusion
• Dynamic Orchestration
• Cognitive Execution

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.