arXiv:2607. 17290v1 Announce Type: cross Abstract: In this work, we investigate the effect of lookahead branching strategies in neural network verification.
By Liam Davis, Duo Zhou, Huan Zhang, Guy Katz, Clark Barrett, Haoze Wu
arXiv:2607. 29051v1 Announce Type: cross Abstract: State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism.
By Liam Davis, Haoze Wu
arXiv:2607. 28954v1 Announce Type: new Abstract: Branch and Bound (BaB) aims to achieve complete verification of neural networks by adaptively partitioning the problem and applying off-the-shelf verifiers to subproblems.
By Jiawei Ren, Guanqin Zhang, Zhenya Zhang, Yulei Sui
arXiv:2609.25962v1 Announce Type: new
Abstract: Neural network verification has become a key tool for providing formal guarantees on the behaviour of neural networks. However, many verification probl...
By Annelot Bosman, Minghao Liu, Marta Kwiatkowska, Holger Hoos, Jan van Rijn
arXiv:2603. 23878v3 Announce Type: replace-cross Abstract: The parameterized CROWN analysis, a.
By Henry LeCates, Haoze Wu
TreeThink is an open‑source Python library that provides modular, fully asynchronous tree search for neural theorem proving. It integrates established tree‑search methods with vLLM inference pipelines and supports a range of node evaluation techniques, from lightweight heuristics to neural evaluators. The library connects directly to the REPL servers of Lean 4, Rocq, and Isabelle/HOL, enabling real‑time verification and proof‑state extraction, and it has been evaluated on miniF2F and MATH500, achieving up to an 8.0× wall‑clock speedup from asynchronous execution.
By Burak S. Akbudak, Zeynel A. Ulu\c{s}an, Can S. Erer, G\"ozde G\"ul \c{S}ahin