The Quadratic Geometry of Flow Matching: Semantic Granularity Alignment for Text-to-Image Synthesis
In this work, we analyze the optimization dynamics of generative fine-tuning. We observe that under the Flow Matching framework, the standard MSE objective can be formulated as a Quadratic Form governed by a dynamically evolving Neural Tangent Kernel (NTK). This geometric perspective reveals a latent Data Interaction Matrix, where diagonal terms represent independent sample learning and off-diagonal terms encode residual correlation between heterogeneous features. Although standard training implicitly optimizes these cross-term interferences, it does so without explicit control; moreover, the prevailing data-homogeneity assumption may constrain the model's effective capacity. Motivated by...
| Authors | Zhinan Xiong, Shunqi Yuan |
|---|---|
| Primary category | cs.CV |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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