arXiv:2602. 09464v2 Announce Type: replace-cross Abstract: Vericoding refers to the generation of formally verified code from rigorous specifications.
By Haoyu Zhao, Ziran Yang, Jiawei Li, Deyuan He, Zenan Li, Chi Jin, Venugopal V. Veeravalli, Aarti Gupta, Sanjeev Arora
arXiv:2610.17358v1 Announce Type: cross
Abstract: Formal verification proves that a program satisfies its specification, but says nothing about whether the specification captures what the author mean...
By Martin Amster, One An, Holly Baker, Fernanda Graciolli, Simon Henniger, Nada Amin
arXiv:2606. 13706v1 Announce Type: cross Abstract: We present HierSVA, an integrated suite that combines a pipeline, dataset, and benchmark for LLM-driven hierarchical hardware formal verification.
By Maohua Nie, Jiang Zhu, Jingqun Zhang, Zhichen Zeng, Jiayi Wang, Sibo Zhang, Jialin Wang, C. -J. Richard Shi
arXiv:2607. 12650v1 Announce Type: cross Abstract: Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny.
By Junyu Ren
arXiv:2609.21190v1 Announce Type: cross
Abstract: Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check corr...
By George Ma, Benjamin Mikek, Haoyu Li, Ferhat Erata, Yuhao Zhang, Zeren Shui, Behrooz Omidvar Tehrani, Jun Huan, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou, Anoop Deoras
SemLoc transforms free‑form reasoning from large language models into executable semantic constraints that are attached to specific code anchors such as variable definitions, branches, loops, or returns. These constraints are inserted as runtime checks, and the resulting semantic violation spectrum is used to guide counterfactual verification and patch generation. On the QuixBugs and new SemBugs datasets, SemLoc’s constraints achieve high ranking of faulty lines and a high proportion of verifiable, correct patches, matching the accuracy of the underlying LLMs while outperforming traditional spectrum‑based methods.
By Zhaorui Yang, Haichao Zhu, Qian Zhang, Rajiv Gupta, Ashish Kundu