arXiv:2607.03738v1 Announce Type: cross Abstract: 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 tha
This research addresses a critical gap in understanding how multimodal large language models process information sequentially, driven by the rapid evolution and deployment of these systems.
A strategic reader should care because deeper interpretability of MLLMs' token-level dynamics is crucial for improving their reliability, robustness, and ethical deployment in real-world applications.
This work introduces a novel methodology ('OTaT') to track attention shifts across different semantic roles during multimodal generation, providing a more granular understanding of model behaviour.
- · AI interpretability researchers
- · Multimodal LLM developers
- · AI safety auditors
- · Companies deploying MLLMs
- · Developers of black-box MLLMs
- · Users encountering unpredictable MLLM behavior
Improved understanding of MLLM internal workings leads to more targeted model debugging and performance enhancements.
Enhanced interpretability frameworks facilitate the development of more transparent and trustworthy AI systems, accelerating their integration into sensitive domains.
A clearer picture of 'when' and 'how' MLLMs attend to different inputs could inspire entirely new adversarial attack vectors or defense mechanisms.
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Read at arXiv cs.AI