8 citations · 9 across the 19 of their papers we have counts for
22 papers
RAIN: Agentic Proof Generation for Rocq-based Software Verification
Jinyi Zhou, Zenan Li, Yuan Yao +3
Interactive theorem provers can provide strong correctness guarantees for software, but constructing the proofs remains a challenging and labor-intensive task. Inspired by the rece…
P: Joint Program-and-Proof Planning for Verified Code Generation
Zenan Li, Ziran Yang, Peiyang Song +2
Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promi…
Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry
Zhaoyu Li, Hangrui Bi, Youyuan Zhang +5
Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and algebraic computation. Yet,…
Planning to Hammer: Difficulty-Aware Decomposition for Automating Rocq Proofs
Ning Zhang, Nongyu Di, Zenan Li +2
As AI-generated code proliferates, formal verification, particularly through interactive theorem provers such as Rocq and Isabelle, becomes increasingly important for ensuring soft…
Euclean: Automated Geometry Problem Formalization with Unified Verification in Lean
Linbin Tang, Jingyan You, Zilin Kang +8
Recent formal reasoning systems have reached IMO-level performance, yet they leave a fragmented landscape: algebra and number theory are handled in Lean, while geometry still relie…
Task Abstention for Large Language Models in Code Generation
Yanke Zhou, Yuhao Tan, Senrong Xu +4
Large language models (LLMs) have revolutionized automated code generation. One serious concern, however, is the so-called ``hallucination'', i.e., LLMs may generate seemingly plau…
Uncertainty Quantification for LLM-based Code Generation
Senrong Xu, Yuhao Tan, Yanke Zhou +6
Prediction sets provide a theoretically grounded framework for quantifying uncertainty in machine learning models. Adapting them to structured generation tasks, in particular, larg…
Fair Conformal Classification via Learning Representation-Based Groups
Senrong Xu, Yanke Zhou, Yuhao Tan +5
Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, th…
Learning to Disprove: Formal Counterexample Generation with Large Language Models
Zenan Li, Zhaoyu Li, Kaiyu Yang +2
Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones. Howeve…
Neuro-Symbolic Proof Generation for Scaling Systems Software Verification
Baoding He, Zenan Li, Wei Sun +4
Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual a…
Goedel-Code-Prover: Hierarchical Proof Search for Open State-of-the-Art Code Verification
Zenan Li, Ziran Yang, Deyuan He +7
Large language models (LLMs) can generate plausible code but offer limited guarantees of correctness. Formally verifying that implementations satisfy specifications requires constr…
AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms
Haoyu Zhao, Ziran Yang, Jiawei Li +6
Vericoding refers to the generation of formally verified code from rigorous specifications. Recent AI models show promise in vericoding, but a unified methodology for cross-paradig…
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…
Neuro-Symbolic Data Generation for Math Reasoning
Zenan Li, Zhi Zhou, Yuan Yao +5
A critical question about Large Language Models (LLMs) is whether their apparent deficiency in mathematical reasoning is inherent, or merely a result of insufficient exposure to hi…
Decoupling Training-Free Guided Diffusion by ADMM
Youyuan Zhang, Zehua Liu, Zenan Li +3
In this paper, we consider the conditional generation problem by guiding off-the-shelf unconditional diffusion models with differentiable loss functions in a plug-and-play fashion.…
Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency
Zenan Li, Yifan Wu, Zhaoyu Li +4
Autoformalization, the task of automatically translating natural language descriptions into a formal language, poses a significant challenge across various domains, especially in m…
A Survey on Deep Learning for Theorem Proving
Zhaoyu Li, Jialiang Sun, Logan Murphy +5
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in natural language to rigorous derivations in formal systems. In recent years, the advance…