AINeutralarXiv – CS AI · Mar 277/10
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Sparse Visual Thought Circuits in Vision-Language Models
Research reveals that sparse autoencoder (SAE) features in vision-language models often fail to compose modularly for reasoning tasks. The study finds that combining task-selective feature sets frequently causes output drift and accuracy degradation, challenging assumptions used in AI model steering methods.