1 citations · 1 across the 7 of their papers we have counts for
7 papers
From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models
Seunggeun Kim, Jaeyeon Kim, Taekyun Lee +4
Many discrete reasoning tasks, such as code generation, are inherently non-causal: programmers move between high-level structure and local details, a process we call any-order infe…
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…
PPAAS: PVT and Pareto Aware Analog Sizing via Goal-conditioned Reinforcement Learning
Seunggeun Kim, Ziyi Wang, Sungyoung Lee +4
Device sizing is a critical yet challenging step in analog and mixed-signal circuit design, requiring careful optimization to meet diverse performance specifications. This challeng…
UniMoCo: Unified Modality Completion for Robust Multi-Modal Embeddings
Jiajun Qin, Yuan Pu, Zhuolun He +3
Current vision-language models have been explored for multi-modal embedding tasks like information retrieval. However, they face significant challenges in real-world queries and ta…
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…
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling
Youngmin Oh, Jinje Park, Seunggeun Kim +3
Recent advancements in reinforcement learning (RL) for analog circuit optimization have demonstrated significant potential for improving sample efficiency and generalization across…
LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
Guojin Chen, Keren Zhu, Seunggeun Kim +4
Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimi…