5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.CL2026
The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models
Bohang Sun, Max Zhu, Francesco Caso +5
Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tok…
q-bio.BM2024
Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies
Tianyuan Zheng, Alessandro Rondina, Gos Micklem +1
Deep generative models show promise for protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally…
cs.LG2024★ 5 cited
FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models
Max Zhu, Adrián Bazaga, Pietro Liò
Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations. Recently, large language models (LLMs) hav…