1 citations · 1 across the 3 of their papers we have counts for
4 papers
Generative Frontiers: Why Evaluation Matters for Diffusion Language Models
Patrick Pynadath, Jiaxin Shi, Ruqi Zhang
Diffusion language models have seen exciting recent progress, offering far more flexibility in generative trajectories than autoregressive models. This flexibility has motivated a…
Why Any-Order Autoregressive Models Need Two-Stream Attention: A Structural-Semantic Tradeoff
Patrick Pynadath, Ruqi Zhang
Any-order autoregressive models (AO-ARMs) offer a promising path toward efficient masked diffusion by enabling native key-value caching, but competitive performance has so far requ…
Single-Step Consistent Diffusion Samplers
Pascal Jutras-Dubé, Patrick Pynadath, Ruqi Zhang
Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterat…
Controlled LLM Decoding via Discrete Auto-regressive Biasing
Patrick Pynadath, Ruqi Zhang
Controlled text generation allows for enforcing user-defined constraints on large language model outputs, an increasingly important field as LLMs become more prevalent in everyday…