3 papers
cs.LG2026
LATS: Levy Adaptive Tree Sampling for Feedback-Driven Diverse Target Discovery
Binglin Ji, Anindya Sarkar, Hengchang Lu +3
While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharacterized regions that maximize…
cs.LG2026
Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search
Binglin Ji, Anindya Sarkar, Hengchang Lu +2
In many scientific and engineering domains, maximizing discovery within a limited sampling budget demands strategic, observation-guided exploration. While generative models have en…
cs.LG2026
Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search
Binglin Ji, Anindya Sarkar, Hengchang Lu +2
While generative models have enabled training-free reward alignment, current methods typically excel in local exploration within narrow regions of the underlying distribution. Thes…