7 citations · 11 across the 13 of their papers we have counts for
4 papers · 1 filter
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models
Quentin Fruytier, Anton Xue, Sanjay Shakkottai +1
Diffusion language models accelerate generation by decoding multiple masked positions per forward pass. However, accuracy is known to drop when parallel decoding trajectories devia…
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Deqing Fu, Huangyuan Su, Rajat Sen +4
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliar…
dQwen3.5: Hybrid-Attention Diffusion Language Models
Anton Xue, Litu Rout, Aditya Akella +3
Adapting a pretrained autoregressive (AR) model is a cost-efficient route to a diffusion language model (DLM). While nearly all such adaptations start from a full-attention transfo…
Token Time Continuous Diffusion for Language Modeling
Parikshit Bansal, Sujay Sanghavi
In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise t…