FailSAE: Towards Interpretable Failure Prediction for Vision-Language Models via Sparse Autoencoders

Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by aligning visual and textual representations in a shared embedding space. As VLMs are increasingly used for high-stakes domains, failure prediction becomes critical for risk-aware deployment and h