22 papers
Occupancy-based Quantile Risk Control
Zihao Shi, Huajun Xi, Bingyi Jing +1
Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, qu…
Robust Conformalized Selection with Noisy Responses
Chengyao Yu, Hongxin Wei, Bingyi Jing
Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug disc…
HSAP: A Hierarchical Sequence-aware Parallelism for Hybrid-Context Generative Models
Songxin Zhang, Zejian Xie, Zhuoyang Song +4
In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly…
StatABench: Dataset and Framework for Evaluating Statistical Analysis Capabilities of LLMs
Youxin Zhu, Yixuan Ding, Peng Lai +3
Statistical analysis is a broad, complex field requiring both domain knowledge and tool proficiency. While prior work has evaluated large language models (LLMs) in this domain, exi…
BatchWeave: A Consistent Object-Store-Native Data Plane for Large Foundation Model Training
Ting Sun, Junjie Zhang, Xiao Yan +7
Modern Large Foundation Model (LFM) training has transformed the data pipeline from a static ingestion layer into a dynamic component that must co-evolve with the training process.…
Toward Early Quality Assessment of Text-to-Image Diffusion Models
Huanlei Guo, Hongxin Wei, Bingyi Jing
Recent text-to-image (T2I) diffusion and flow-matching models can produce highly realistic images from natural language prompts. In practical scenarios, T2I systems are often run i…