54 citations · 56 across the 17 of their papers we have counts for
18 papers
Not Every User Says It All: Training Tool-Using Agents Beyond Ideal Simulated Users
Han Bao, Zheyuan Zhang, Kaiwen Shi +1
Tool-using agents are trained and evaluated almost exclusively with strong simulated users who state every detail clearly, yet real users are often weaker. We construct weak users…
PreDiff-LM: Pretrained Discrete Masked Diffusion Language Modeling with Hybrid Attention
Zhengtao Yao, Runhao Li, Xupeng Chen +12
Discrete masked diffusion language models support bidirectional generation and infilling, but adapting pretrained autoregressive (AR) transformers requires reconciling causal pretr…
PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models
Han Bao, Penghao Zhang, Yue Huang +9
Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason abou…
EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering
Jiatan Huang, Zheyuan Zhang, Kaiwen Shi +2
Large language model agents often exhibit complementary strengths, making routing a promising approach for multi-agent question answering. However, existing routing methods remain…
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…
W2T: LoRA Weights Already Know What They Can Do
Xiaolong Han, Ferrante Neri, Zijian Jiang +4
Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In princ…
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…
Drift-Bench: Diagnosing Cooperative Breakdowns in LLM Agents under Input Faults via Multi-Turn Interaction
Han Bao, Zheyuan Zhang, Pengcheng Jing +3
As Large Language Models transition to autonomous agents, user inputs frequently violate cooperative assumptions (e.g., implicit intent, missing parameters, false presuppositions,…
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…
Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use
Yiyang Li, Zehong Wang, Zhengqing Yuan +4
Illicit drug use among teenagers and young adults (TYAs) remains a pressing public health concern, with rising prevalence and long-term impacts on health and well-being. To detect…
Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding
Yiyang Li, Weixiang Sun, Tianyi Ma +3
Food assistance referral requires conversational agents to translate underspecified, often noisy help-seeking dialogues into locally valid resource recommendations. We present Food…
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…
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
Rong Zhou, Zhengqing Yuan, Zhiling Yan +7
Biomedical image segmentation is crucial for accurately diagnosing and analyzing various diseases. However, Convolutional Neural Networks (CNNs) and Transformers, the most commonly…
ViT-1.58b: Mobile Vision Transformers in the 1-bit Era
Zhengqing Yuan, Rong Zhou, Hongyi Wang +3
Vision Transformers (ViTs) have achieved remarkable performance in various image classification tasks by leveraging the attention mechanism to process image patches as tokens. Howe…
TrustLLM: Trustworthiness in Large Language Models
Yue Huang, Lichao Sun, Haoran Wang +67
Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs prese…