3 citations · 4 across the 5 of their papers we have counts for
6 papers
Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models
Shengyin Sun, Yiming Li, Yingzhao Lian +7
Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation behavior. This motivates a com…
Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
Guoxuan Chen, Chufeng Xiao, Haoran Yang +30
We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers compet…
Scaling Up, Speeding Up: A Benchmark of Speculative Decoding for Efficient LLM Test-Time Scaling
Shengyin Sun, Yiming Li, Xing Li +8
Test-time scaling has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs) by allocating additional computational resources durin…
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation
Xinyi Zhou, Xing Li, Yingzhao Lian +6
We introduce SeaDAG, a semi-autoregressive diffusion model for conditional generation of Directed Acyclic Graphs (DAGs). Considering their inherent layer-wise structure, we simulat…
RTLRewriter: Methodologies for Large Models aided RTL Code Optimization
Xufeng Yao, Yiwen Wang, Xing Li +6
Register Transfer Level (RTL) code optimization is crucial for enhancing the efficiency and performance of digital circuits during early synthesis stages. Currently, optimization r…
DiLA: Enhancing LLM Tool Learning with Differential Logic Layer
Yu Zhang, Hui-Ling Zhen, Zehua Pei +4
Considering the challenges faced by large language models (LLMs) in logical reasoning and planning, prior efforts have sought to augment LLMs with access to external solvers. While…