8 citations · 9 across the 19 of their papers we have counts for
12 papers · 1 filter
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…