1 citations · 2 across the 12 of their papers we have counts for
12 papers
Dr. Claw: An AI Scientist Workspace for Vibe Research
Dingjie Song, Hanrong Zhang, Dawei Liu +10
Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, ID…
COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows
Zongxia Li, Dawei Liu, Fuxiao Liu +6
Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently. As these workflows become repeated and domain-specific, agent…
Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
Dawei Liu, Zongxia Li, Hongyang Du +4
As LLM agents act across personal applications, web browsers, and other interfaces, their reusable skill libraries can scale to thousands of skills. This scale introduces two chall…
MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
Zhengqing Yuan, Hanchi Sun, Lichao Sun +1
We present MegaTrain, a memory-centric system that efficiently trains 100B+ parameter large language models at full precision on a single GPU. Unlike traditional GPU-centric system…
MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine
Kai Zhang, Zhengqing Yuan, Cheng Peng +10
Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are ei…
CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era
Kaiwen Shi, Weixiang Sun, Zheyuan Zhang +3
Scientific research relies on citation integrity, yet large language models (LLMs) have introduced a critical risk: fabricated references that appear plausible but correspond to no…
Horizon-LM: A RAM-Centric Architecture for LLM Training
Zhengqing Yuan, Lichao Sun, Yanfang Ye
The rapid growth of large language models (LLMs) has outpaced the evolution of single-GPU hardware, making model scale increasingly constrained by memory capacity rather than compu…
BLURR: A Boosted Low-Resource Inference for Vision-Language-Action Models
Xiaoyu Ma, Zhengqing Yuan, Zheyuan Zhang +3
Vision-language-action (VLA) models enable impressive zero shot manipulation, but their inference stacks are often too heavy for responsive web demos or high frequency robot contro…
3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation
Yunhong He, Zhengqing Yuan, Zhengzhong Tu +2
We introduce 3D4D, an interactive 4D visualization framework that integrates WebGL with Supersplat rendering. It transforms static images and text into coherent 4D scenes through f…
EfficientLLM: Efficiency in Large Language Models
Zhengqing Yuan, Weixiang Sun, Yixin Liu +13
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We intr…
Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?
Yue Huang, Zhengqing Yuan, Yujun Zhou +8
Large Language Models (LLMs) are increasingly employed for simulations, enabling applications in role-playing agents and Computational Social Science (CSS). However, the reliabilit…
MLP-KAN: Unifying Deep Representation and Function Learning
Yunhong He, Yifeng Xie, Zhengqing Yuan +1
Recent advancements in both representation learning and function learning have demonstrated substantial promise across diverse domains of artificial intelligence. However, the effe…