New AI framework Chain of Modality fixes multimodal model paradox where single-input systems outperform multi-sensory ones

Omni-MLLMs that integrate multiple sensory inputs underperform compared to unimodal baselines, revealing a critical flaw in current multimodal AI systems

Problem identified: static fusion topologies cause positional bias in sequential inputs and alignment traps in interleaved formats that distort attention processing

Chain of Modality (CoM) framework proposed as solution, dynamically switching between parallel, sequential, and interleaved input pathways to eliminate structural biases

CoM employs task-aligned cognitive execution with dual pathways for more flexible and context-aware multimodal processing