8 citations · 9 across the 19 of their papers we have counts for
6 papers · 1 filter
Conformal Correction for Efficiency May be at Odds with Entropy
Senrong Xu, Tianyu Wang, Zenan Li +4
Conformal prediction (CP) provides a comprehensive framework to produce statistically rigorous uncertainty sets for black-box machine learning models. To further improve the effici…
A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning
Zhi Zhou, Yuhao Tan, Zenan Li +4
Test-time scaling seeks to improve the reasoning performance of large language models (LLMs) by adding computational resources. A prevalent approach within the field is sampling-ba…
FormalML: A Benchmark for Evaluating Formal Subgoal Completion in Machine Learning Theory
Xiao-Wen Yang, Zihao Zhang, Jianuo Cao +7
Large language models (LLMs) have recently demonstrated remarkable progress in formal theorem proving. Yet their ability to serve as practical assistants for mathematicians, fillin…
Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models
Chenrui Cao, Liangcheng Song, Zenan Li +4
Recent advancements, such as DeepSeek-Prover-V2-671B and Kimina-Prover-Preview-72B, demonstrate a prevailing trend in leveraging reinforcement learning (RL)-based large-scale train…
Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
Zenan Li, Zhaoyu Li, Wen Tang +6
Large language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a.k.a.} tactics) within a proof system. However, the space of possible tact…
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
Zhi Zhou, Tan Yuhao, Zenan Li +4
Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, single-shot inference often yields unreliable results for complex…