any-order inference 1code generation 1insertion-based generation 1latent-space generation 1masked diffusion models 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models
Seunggeun Kim, Jaeyeon Kim, Taekyun Lee +4
The paper investigates how to give language models a native ability to reason and generate text in any order, introducing insertion‑based and latent‑space masked diffusion methods…
cs.LG2026
Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee +4
A natural desideratum for generative models is self-correction--detecting and revising low-quality tokens at inference. While Masked Diffusion Models (MDMs) have emerged as a promi…
cs.LG2025
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
Sungyoung Lee, Ziyi Wang, Seunggeun Kim +3
Pretraining models with unsupervised graph representation learning has led to significant advancements in domains such as social network analysis, molecular design, and electronic…