Attending to Multimodal Generation One Token at a Time

Hugging Face Daily Papers Papers
multimodal large-language-models attention-mechanisms interpretability generation token-level causal-intervention

Summary

This paper investigates token-level attention shifts in multimodal large language models during generation, revealing consistent patterns and proposing a simple test-time intervention that significantly improves task performance.

Multimodal large language models (MLLMs) generate responses autoregressively, integrating visual and linguistic information in an evolving context. Prior work on interpretability has focused on individual layers and circuits (where), leaving the token-level dynamics of multimodal computation during generation (when) underexplored. We address this gap and study attention shifts as per semantic role; tracking model attention to image, text, instruction, and previously generated tokens, One Token at a Time (OTaT). We introduce multimodal tasks that require explicit switching between visual and textual context within a single response. Across two mainstream model families and four open-weight MLLMs of varying sizes, we establish consistent patterns: attention to image peaks at tokens requiring image-derived information, instruction tokens are revisited during task transitions, and attention to previously generated tokens increases as the generation progresses. Causal attention blocking interventions validate the functional role of these trends. We profile model behavior under disrupted attention and observe responses falling back to language priors, or exhibiting cross-modal leakage, denial, or recovery. Finally, informed of the attention dynamics through our novel analysis, we propose a simple test-time intervention to boost attention to the relevant modality at the right time, significantly improving multimodal task performance.
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Source: https://huggingface.co/papers/2607.03738

Abstract

Multimodal large language models exhibit distinct attention patterns during generation, with attention to visual and textual modalities shifting based on semantic requirements, and these patterns can be leveraged to improve task performance through targeted interventions.

Multimodal large language models(MLLMs) generate responses autoregressively, integrating visual and linguistic information in an evolving context. Prior work on interpretability has focused on individual layers and circuits (where), leaving the token-level dynamics of multimodal computation during generation (when) underexplored. We address this gap and studyattention shiftsas persemantic role; tracking model attention to image, text, instruction, and previously generated tokens, One Token at a Time (OTaT). We introduce multimodal tasks that require explicit switching between visual and textual context within a single response. Across two mainstream model families and four open-weight MLLMs of varying sizes, we establish consistent patterns: attention to image peaks at tokens requiring image-derived information, instruction tokens are revisited during task transitions, and attention to previously generated tokens increases as the generation progresses.Causal attention blockinginterventions validate the functional role of these trends. We profile model behavior under disrupted attention and observe responses falling back to language priors, or exhibitingcross-modal leakage, denial, or recovery. Finally, informed of the attention dynamics through our novel analysis, we propose a simpletest-time interventionto boost attention to the relevant modality at the right time, significantly improving multimodal task performance.

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