The Geometry of Compromise: Unlocking Generative Capabilities via Controllable Modality Alignment
Vision-Language Models (VLMs) such as CLIP learn a shared embedding space for images and text, yet their representations remain geometrically separated, a phenomenon known as the modality gap. This gap limits tasks requiring cross-modal interchangeability, such as captioning and joint clustering. Existing post-processing approaches can partially improve cross-modal compatibility; however, we show through geometric analysis that they primarily reduce the global centroid offset while leaving the u
Record details
Published: 31 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (31 March 2026), “The Geometry of Compromise: Unlocking Generative Capabilities via Controllable Modality Alignment,” evidence record 6518, https://ethics.ai/record/6518 (originally published by arXiv).
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