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[2602.20330] Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking
Summary
This article presents a framework for circuit tracing in vision-language models (VLMs), aiming to enhance understanding of their internal mechanisms and multimodal reasoning capabilities.
Why It Matters
As VLMs become increasingly integral in AI applications, understanding their inner workings is crucial for improving their transparency and reliability. This research lays the groundwork for more explainable AI systems, which is vital for trust and safety in AI deployment.
Key Takeaways
- Introduces a novel framework for circuit tracing in VLMs.
- Demonstrates how VLMs integrate visual and semantic concepts hierarchically.
- Validates the framework through feature steering and circuit patching.
- Reveals that distinct circuits can manage mathematical reasoning.
- Sets the stage for developing more explainable and controllable VLMs.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.20330 (cs) [Submitted on 23 Feb 2026] Title:Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking Authors:Jingcheng Yang, Tianhu Xiong, Shengyi Qian, Klara Nahrstedt, Mingyuan Wu View a PDF of the paper titled Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking, by Jingcheng Yang and 4 other authors View PDF HTML (experimental) Abstract:Vision-language models (VLMs) are powerful but remain opaque black boxes. We introduce the first framework for transparent circuit tracing in VLMs to systematically analyze multimodal reasoning. By utilizing transcoders, attribution graphs, and attention-based methods, we uncover how VLMs hierarchically integrate visual and semantic concepts. We reveal that distinct visual feature circuits can handle mathematical reasoning and support cross-modal associations. Validated through feature steering and circuit patching, our framework proves these circuits are causal and controllable, laying the groundwork for more explainable and reliable VLMs. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.20330 [cs.CV] (or arXiv:2602.20330v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2602.20330 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submissi...