3 papers
cs.CL2026
HumanLM: Simulating Users with State Alignment Beats Response Imitation
Shirley Wu, Evelyn Choi, Arpandeep Khatua +7
Large Language Models (LLMs) are increasingly used to simulate how specific users respond to a given context, enabling more user-centric applications that rely on user feedback. Ho…
cs.DC2025
Verify Distributed Deep Learning Model Implementation Refinement with Iterative Relation Inference
Zhanghan Wang, Ding Ding, Hang Zhu +2
Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed m…
cs.DC2025
Understanding Stragglers in Large Model Training Using What-if Analysis
Jinkun Lin, Ziheng Jiang, Zuquan Song +13
Large language model (LLM) training is one of the most demanding distributed computations today, often requiring thousands of GPUs with frequent synchronization across machines. Su…