Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models

ArXi:2511.18123v2 Announce Type: replace-cross Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify graphic biases, resulting in biased associations and misaligned predictions in downstream tasks. Such behavior undermines fairness and distorts the intended alignment between vision and language. Recent post-hoc approaches attempt to mitigate bias by replacing the most attribute-correlated embedding coordinates with neutral values.