6 citations · 11 across the 15 of their papers we have counts for
17 papers
Tactix: Towards a Programmable Strategy Interface for Flexible SAT Solving
Haoze Wu, André Schidler, Armin Biere +1
Modern SAT solvers combine a rich repertoire of proof rules with sophisticated strategies that determine when and how to apply them. Often, the internal implementations are tightly…
An AI Approach to Verified Production Cryptographic Libraries
Chuyue Sun, Su Fong, Zhiyi Kuang +5
Cryptographic code is critical infrastructure that must be correct, yet formally verifying production libraries remains difficult. Existing language-model proof systems solve isola…
Enhancing and Reporting Robustness Boundary of Neural Code Models for Intelligent Code Understanding
Tingxu Han, Wei Song, Weisong Sun +6
With the development of deep learning, Neural Code Models (NCMs) such as CodeBERT and CodeLlama are widely used for code understanding tasks, including defect detection and code cl…
Incremental Neural Network Verification via Learned Conflicts
Raya Elsaleh, Liam Davis, Haoze Wu +1
Neural network verification is often used as a core component within larger analysis procedures, which generate sequences of closely related verification queries over the same netw…
The Luna Bound Propagator for Formal Analysis of Neural Networks
Henry LeCates, Haoze Wu
The parameterized CROWN analysis, a.k.a., alpha-CROWN has emerged as a practically successful abstract interpretation method for neural network verification. However, existing impl…
Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu +2
The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show promise in mitigating this issue…
SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification
Rocky Klopfenstein, Yang He, Andrew Tremante +3
Community-driven Text-to-SQL evaluation platforms play a pivotal role in tracking the state of the art of Text-to-SQL performance. The reliability of the evaluation process is crit…
Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
Anjiang Wei, Tianran Sun, Tarun Suresh +3
Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge. We investigate whether large language models (LLM…
Cubing for Tuning
Haoze Wu, Clark Barrett, Nina Narodytska
We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive…
PICID: Proof-Driven Clause Learning in Neural Network Verification
Omri Isac, Idan Refaeli, Haoze Wu +2
Current Deep Neural Network (DNN) verifiers are typically designed to prioritize scalability over reliability. Reliability can be reinforced through the generation of proofs that a…
Efficiently Computing Compact Formal Explanations
Min Wu, Xiaofu Li, Haoze Wu +1
Building on VeriX (Verified eXplainability, arXiv:2212.01051), a system for producing optimal verified explanations for machine learning models, we present VeriX+, which significan…
Safe and Reliable Training of Learning-Based Aerospace Controllers
Udayan Mandal, Guy Amir, Haoze Wu +10
In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these mode…
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
Udayan Mandal, Guy Amir, Haoze Wu +9
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating agents that control autonomous systems. However, the ``black box'' nature of DRL agents lim…
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Haoze Wu, Omri Isac, Aleksandar Zeljić +14
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design a…
Towards Efficient Verification of Quantized Neural Networks
Pei Huang, Haoze Wu, Yuting Yang +4
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory. In t…
Satisfiability Modulo Theories for Verifying MILP Certificates
Kenan Wood, Runtian Zhou, Haoze Wu +2
Correctness of results from mixed-integer linear programming (MILP) solvers is critical, particularly in the context of applications such as hardware verification, compiler optimiz…
Lemur: Integrating Large Language Models in Automated Program Verification
Haoze Wu, Clark Barrett, Nina Narodytska
The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that demands high-level abstract r…