arXiv:2605.22902v1 Announce Type: new Abstract: Generative Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs are transformed to text remains poorly understood. Existing interpretability work on VLMs uses Sparse Autoencoders (SAEs), which decompose static residual representations and miss the functional updates that drive cross-modal interaction. We adopt a function-centric framework based on Transcoders, sparse approximations of MLP sublayers that act as a causal proxy for layer-wise computation. Applied to Gemma 3-4B-IT, the framework decomposes the mod
The increasing complexity and opacity of large language models necessitate more advanced interpretability techniques to understand their internal mechanisms and address issues like hallucinations.
Understanding how VLMs process visual information and generate text is crucial for improving their reliability, mitigating risks, and accelerating their development and deployment in critical applications.
This research introduces a novel function-centric framework, 'Transcoders,' offering a more granular and causal understanding of VLM layer-wise computation compared to previous methods like Sparse Autoencoders.
- · AI developers
- · AI researchers
- · Model explainability platforms
- · Opaque VLM systems
- · AI models with unchecked hallucination issues
Improved interpretability tools lead to better debugging and optimization of Vision-Language Models.
Enhanced understanding of VLM mechanics enables the design of more robust and less hallucinatory multimodal AI.
Increased trust in AI systems due to greater transparency could accelerate their integration into sensitive applications and industries.
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Read at arXiv cs.LG