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

2026-05-31 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new decision-level framework, Partial Information Decomposition (PID), is introduced to understand modality interaction in multimodal large language models (MLLMs). PID separates unique, redundant, and synergistic contributions from sensory and linguistic inputs, moving beyond representation alignment. Across vision-language benchmarks, PID reveals that reasoning and grounding-oriented tasks exhibit high synergy, while expert and knowledge-oriented tasks show stronger language-unique reliance. These modality-use profiles generalize across model families and predict sensitivity to interventions. The framework extends to tri-modal systems with Sensory PID, which, applied to omni-modal models, identifies a sensory synergy bottleneck dominated by visual information even in audio-visual fusion tasks. Initial evidence suggests PID-guided reweighting can improve multimodal reasoning and grounding performance.

Key takeaway

For AI Scientists and Machine Learning Engineers deploying or optimizing multimodal large language models, understanding modality interaction is crucial for reliable performance. You should consider applying the Partial Information Decomposition (PID) framework to diagnose how different sensory and linguistic inputs contribute to model decisions. This can guide targeted interventions, such as PID-guided reweighting, to improve your models' reasoning and grounding capabilities on specific tasks.

Key insights

Partial Information Decomposition (PID) quantifies unique, redundant, and synergistic modality contributions in MLLMs to understand interaction.

Principles

• Reasoning and grounding tasks exhibit high modality synergy.
• Expert and knowledge tasks show stronger language-unique reliance.
• Modality-use profiles generalize across MLLM families.

Method

PID is a decision-level framework separating unique, redundant, and synergistic contributions of sensory and linguistic inputs. Sensory PID extends this to tri-modal systems, treating language as a control variable to decompose video-audio information gain.

In practice

• Use PID to diagnose modality interaction in MLLMs.
• Apply PID-guided reweighting to improve multimodal reasoning.

Topics

• Multimodal Language Models
• Partial Information Decomposition
• Modality Interaction
• Vision-Language
• Sensory PID
• Multimodal Reasoning

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.