Title: MINT: Causally Tracing Information Fusion in Multimodal Large Language Models
Primary area: interpretability and explainable AI
Keywords: Vision-Language Models, Interpretability, Hidden State Patching, Multimodal Fusion, Explainable AI

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on tasks that involve understanding and integrating information across different modalities, particularly vision and language. Despite their effectiveness, the internal representations of these Vision Language Models (VLMs) remain poorly understood, making it difficult to interpret their predictions or identify the causes of common errors. A crucial step toward improved interpretability is understanding how visual and textual signals fuse within the language decoder of these models. This integration process is particularly important since failures to properly combine modalities frequently lead to errors such as object hallucinations and incorrect spatial descriptions. In this paper, we systematically investigate the internal mechanisms of multimodal fusion in three representative VLMs: LLaVA-1.5-7B, DeepSeek-VL2-Tiny, and Qwen2-VL-7B. We propose MINT (Multimodal INtervention Tracing), a method that builds on the principle of hidden state patching to create a causal map of multimodal processing by systematically intervening at each layer of the language decoder. From these maps, we identify a critical region we term the `fusion band'—the decisive window of layers where visual and linguistic signals are actively fused to guide the model's output. Our analysis reveals that the location and width of this band are not uniform across models; they highlight fundamental differences in their fusion mechanisms that directly correlate with a model's ability to resolve contradictions, ground language, and perform complex spatial reasoning. This causal mapping offers a diagnostic framework to explain common VLM failures and inform future architectural design.

TL;DR: We use hidden state patching to locate a "fusion band" in mid-decoder layers where VLMs integrate vision and text. This causal map explains common errors like hallucinations and guides future model improvement.