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FailSAE: Towards Interpretable Failure Prediction for Vision-Language Models via Sparse Autoencoders
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failure predictioninterpretabilitymultimodalautoencoders
2609.04276
Builder Relevance
7h ago70%
Abstract
This paper investigates the use of Sparse Autoencoders for interpretable failure prediction in Vision-Language Models.
Reality Card
Core Claim
The proposed framework outperforms existing methods in failure prediction while providing improved interpretability.
Method / Result
The framework shows improved performance in failure prediction compared to evaluated baselines.
Limitations
The paper does not specify the authors or provide detailed experimental setups, which may hinder reproducibility.
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