An inprocessing framework for neural network verification driven by the lookahead procedure is introduced, which derives new lemmas over the phases of unstable ReLUs, which are collected into an implication graph that is used to prune the search space and vivify boolean cuts.
Abstract
State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism. We introduce an inprocessing framework for neural network verification driven by the lookahead procedure. Under this framework, lookahead derives new lemmas over the phases of unstable ReLUs, which are collected into an implication graph that is used to prune the search space and vivify boolean cuts. We instantiate the framework in two state-of-the-art verifiers, Marabou and $\alpha$-$\beta$-CROWN, and demonstrate that it improves performance in both, proving up to 34% more instances unsatisfiable.
This work presents a general recipe to integrate lookahead into any branch-and-bound verifier and demonstrates how one of the current state-of-the-art branching heuristics, FSB, can be viewed as a special instantiation of the lookahead branching strategy.
Liam Davis, Duo Zhou, Huan Zhang et al.· Proceedings of the Thirty-Fi...· 1 citation
A branch-and-bound framework to mitigate the issue of verifying relational specifications against relational specifications, which iteratively splits the problem until all sub-problems are verified, and devise a relational neuron selection strategy based on the dual formulation of the verification problem.
Kota Fukuda, Zhen-Ya Zhang, Guan-Qin Zhang et al.· 0 citations
This study shows that template subsumption rates can vary widely across scenarios, and presents FastCert, a novel technique for automatically distributing templates across neural network layers to increase performance impact, eschewing templates entirely if they are unlikely to produce a speedup.
Kanak Das, Shubham Ugare, B. E. Chang et al.· 0 citations
This work designs an SCA-based proof engine that exploits a conflict-aware learning approach that compresses the intermediate polynomial expression and successfully verifies circuits that remained unverifiable using previous approaches.
Jan Kleinekath ̈ofer, Lennart Weingarten, Kamalika Datta et al.· 1 citation
VeRe is proposed, a verification-guided repair framework that leverages linear relaxation to precisely and efficiently estimate the repair significance of neurons and synthesizes ideal intervals that provide sound guarantees for correct behaviors, thereby facilitating surgical and targeted adjustments of neuron paramet...
Jia-Nan Ma, Wei Chen, Pengfei Yang et al.· ACM Transactions on Software...· 0 citations
Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities across diverse tasks, their outputs often lack accuracy and provide no clear measure of confidence. This uncertainty compounds in flows of multiple calls to LLMs and other...
Louis Mandel, Guillaume Baudart, M. Vaziri et al.· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
