Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition

Researchers introduce Partial Information Decomposition (PID), a framework for analyzing how multimodal language models integrate vision and language inputs by separating unique, redundant, and synergistic contributions. The analysis reveals distinct modality-use patterns across task types and identifies visual dominance as a bottleneck in audio-visual fusion systems.

This research addresses a critical gap in understanding how multimodal language models actually process and combine different sensory inputs. Rather than relying on representation alignment metrics or outcome-based evaluation, PID operates at the decision level to decompose the specific contributions each modality makes to model outputs. This methodological advancement enables more granular diagnosis of model behavior beyond black-box performance metrics.

The findings establish empirically what practitioners have suspected: reasoning and grounding tasks benefit from high synergy between vision and language, while knowledge-oriented tasks rely more heavily on language inputs. This heterogeneity across task types suggests one-size-fits-all approaches to multimodal optimization are suboptimal. The extension to tri-modal systems through Sensory PID reveals a significant architectural limitation—visual information dominates even in audio-visual tasks, suggesting current omni-modal models may not be effectively leveraging audio channels.

For the AI development community, these insights translate into actionable improvements. The PID-guided reweighting approach demonstrates that understanding modality interaction directly enables performance gains in reasoning and grounding. This framework provides engineers with diagnostic tools to identify which modalities genuinely contribute to specific tasks versus which are mere redundancy.

Looking forward, this research sets a foundation for more intentional multimodal architecture design. Future work should focus on whether task-specific modality reweighting can be automated and how these insights apply to emerging modality combinations beyond vision-language-audio.

• →Partial Information Decomposition reveals that reasoning tasks show high vision-language synergy while knowledge tasks rely more on language alone
• →Visual information dominates audio-visual fusion tasks in current omni-modal models, indicating a potential architectural bottleneck
• →PID-guided reweighting demonstrates measurable performance improvements in multimodal reasoning and grounding capabilities
• →Modality interaction patterns generalize across different model families, suggesting fundamental principles rather than implementation artifacts
• →The framework enables decision-level analysis beyond representation metrics, providing more precise diagnostic tools for multimodal model development