3 papers
cs.LG2026
Task Inference Beyond Least Squares in Behavioral Foundation Models
Kuan-Hsun Tu, Chien-Sheng Chiang, Hsin-Wei Chen +2
Behavioral Foundation Models (BFMs) aim to solve a wide range of downstream tasks without test-time policy learning by inferring a task vector from the reward function. While effic…
cs.CV2026
Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching
Kuan-Hsun Tu, Hsuan-Chi Liu, Jia-Wei Liao +3
We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas standard conditional flow match…
cs.CV2026
COT-FM: Cluster-wise Optimal Transport Flow Matching
Chiensheng Chiang, Kuan-Hsun Tu, Jia-Wei Liao +2
We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved tra…