6 papers
Difficulty-Estimated Policy Optimization
Yu Zhao, Fan Jiang, Tianle Liu +4
Recent advancements in Large Reasoning Models (LRMs), exemplified by DeepSeek-R1, have underscored the potential of scaling inference-time compute through Group Relative Policy Opt…
A Looming of phantoms
Kimoi Kemboi, Daniel Krashen, Tianle Liu +6
Following Krah's method, we construct new examples of phantom categories as semiorthogonal components of the derived categories of two types of rational surfaces: the blowup of the…
Generative AI for subgrid turbulence in large-eddy simulations
Yu Cheng, Tianle Liu
Turbulence governs the transport of momentum, energy, and scalars in many geophysical and engineering flows. In large-eddy simulations (LES), parameterizing subgrid-scale (SGS) str…
Test-Time Discovery via Hashing Memory
Fan Lyu, Tianle Liu, Zhang Zhang +2
We introduce Test-Time Discovery (TTD) as a novel task that addresses class shifts during testing, requiring models to simultaneously identify emerging categories while preserving…
Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models
Morgane Austern, Yuanchuan Guo, Zheng Tracy Ke +1
Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextua…
A Heavily Right Strategy for Statistical Inference with Dependent Studies in Any Dimension
Tianle Liu, Xiao-Li Meng, Natesh S. Pillai
We leverage recent advances in heavy-tail approximations for global hypothesis testing with dependent studies to construct approximate confidence regions without modeling or estima…