How Vision Becomes Language: A Layer-wise Information-Theoretic Analysis of Multimodal Reasoning
arXiv:2602.15580v1 Announce Type: new Abstract: When a multimodal Transformer answers a visual question, is the prediction driven by visual evidence, linguistic reasoning, or genuinely fused cross-modal computation -- and how does this structure evolve across layers? We address this question with a layer-wise framework based on Partial Information Decomposition (PID) that decomposes the predictive information at each Transformer layer into redundant, vision-unique, language-unique, and synergistic components. To make PID tractable for high-dimensional neural representations, we introduce \emph{PID Flow}, a pipeline combining dimensionality reduction, normalizing-flow Gaussianization, and closed-form Gaussian PID estimation. Applying this framework to LLaVA-1.5-7B and LLaVA-1.6-7B across six GQA reasoning tasks, we uncover a consistent \emph{modal transduction} pattern: visual-unique information peaks early and decays with depth, language-unique information surges in late layers to acc
arXiv:2602.15580v1 Announce Type: new Abstract: When a multimodal Transformer answers a visual question, is the prediction driven by visual evidence, linguistic reasoning, or genuinely fused cross-modal computation -- and how does this structure evolve across layers? We address this question with a layer-wise framework based on Partial Information Decomposition (PID) that decomposes the predictive information at each Transformer layer into redundant, vision-unique, language-unique, and synergistic components. To make PID tractable for high-dimensional neural representations, we introduce \emph{PID Flow}, a pipeline combining dimensionality reduction, normalizing-flow Gaussianization, and closed-form Gaussian PID estimation. Applying this framework to LLaVA-1.5-7B and LLaVA-1.6-7B across six GQA reasoning tasks, we uncover a consistent \emph{modal transduction} pattern: visual-unique information peaks early and decays with depth, language-unique information surges in late layers to account for roughly 82\% of the final prediction, and cross-modal synergy remains below 2\%. This trajectory is highly stable across model variants (layer-wise correlations $>$0.96) yet strongly task-dependent, with semantic redundancy governing the detailed information fingerprint. To establish causality, we perform targeted Image$\rightarrow$Question attention knockouts and show that disrupting the primary transduction pathway induces predictable increases in trapped visual-unique information, compensatory synergy, and total information cost -- effects that are strongest in vision-dependent tasks and weakest in high-redundancy tasks. Together, these results provide an information-theoretic, causal account of how vision becomes language in multimodal Transformers, and offer quantitative guidance for identifying architectural bottlenecks where modality-specific information is lost.
Executive Summary
This article presents a novel layer-wise information-theoretic analysis of multimodal reasoning in Transformers. The authors employ Partial Information Decomposition (PID) to decompose predictive information at each layer, introducing the PID Flow pipeline to make PID tractable for high-dimensional neural representations. The study reveals a consistent modal transduction pattern across six GQA reasoning tasks, where visual-unique information peaks early and decays, language-unique information surges in late layers, and cross-modal synergy remains low. The authors establish causality through targeted attention knockouts, demonstrating predictable effects on trapped visual-unique information, compensatory synergy, and total information cost. This research provides an information-theoretic, causal account of how vision becomes language in multimodal Transformers, offering quantitative guidance for identifying architectural bottlenecks.
Key Points
- ▸ Layer-wise framework for analyzing multimodal reasoning in Transformers
- ▸ Introduction of PID Flow for high-dimensional neural representations
- ▸ Consistent modal transduction pattern across six GQA reasoning tasks
- ▸ Establishment of causality through targeted attention knockouts
Merits
Strength
The article provides a comprehensive and systematic analysis of multimodal reasoning in Transformers, employing a novel layer-wise framework and introducing a tractable method for PID. The results offer quantitative insights into the information flow and architectural bottlenecks in multimodal Transformers.
Demerits
Limitation
The study is limited to analyzing multimodal Transformers on six GQA reasoning tasks, which may not generalize to other architectures or tasks. Additionally, the authors assume a fixed Transformer architecture, which may not be representative of all multimodal models.
Expert Commentary
The article presents a rigorous and systematic analysis of multimodal reasoning in Transformers, leveraging a novel layer-wise framework and introducing a tractable method for PID. The results offer quantitative insights into the information flow and architectural bottlenecks in multimodal Transformers, which can inform the design of more efficient and effective models. The study's use of targeted attention knockouts to establish causality is a significant contribution to the field, as it provides a mechanism for understanding the causal relationships between different components of the model. However, the study's limitations, such as its focus on a specific architecture and tasks, may limit its generalizability. Nonetheless, this research is a significant step forward in understanding the information-theoretic underpinnings of multimodal Transformers and has the potential to inform the development of more effective and efficient models.
Recommendations
- ✓ Future studies should investigate the generalizability of the results to other architectures and tasks.
- ✓ The authors should explore the application of their framework to other multimodal models, such as sequence-to-sequence models or graph neural networks.
