Author

Seunghoon Hong

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#artificial intelligence Apr 2026

Training-Free Refinement of Flow Matching with Divergence-based Sampling

The Flow Divergence Sampler is proposed, a training-free framework that refines intermediate states before each solver step that consistently improves fidelity across various generation tasks including text-to-image synthesis, and inverse problems.

Yeonwoo Cha, Jaehoon Yoo, Semin Kim et al. · arXiv.org · 4 citations

Preprint Aug 2026

Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models

It is shown that MVRD makes visual representations more geometric while retaining language alignment, and generalizes to 3D scene understanding tasks such as object grounding, dense captioning, and question answering, while approaching feature fusion methods with considerably fewer added parameters and lower latency.

K. T. Nguyen, Hanbo Shim, Jinwoo Kim et al. · 0 citations