From Brittle LLM Code Reasoning to MaxSAT-Based Verified Repairs @ UCL
In this talk, we examine the limitations of Large Language Models (LLMs) in semantic code reasoning, showing that their predictions may change under semantics-preserving code …
Towards Assessing and Repairing LLM-Generated Code via Model Checking and MaxSAT-Based Fault Localisation @ Dagstuhl 2026
LLMs for code often lack true semantic understanding, evidenced by their instability under semantics-preserving transformations, and we address this by integrating formal methods …
PyVeritas: On Verifying Python via LLM-Based Transpilation and Bounded Model Checking for C @ P-AI-FM@AAAI 2026
In this talk I will present PyVeritas, a novel framework that leverages Large Language Models (LLMs) for high-level transpilation from Python to C, followed by bounded model …
PyVeritas: On Verifying Python via LLM-Based Transpilation and Bounded Model Checking for C
In this paper, we propose PyVeritas, a novel framework that leverages Large Language Models (LLMs) for high-level transpilation from Python to C, followed by bounded model checking …
📄 Paper accepted @ the Post-AI Formal Methods Workshop @ AAAI 2026! 🎉
I’m excited to share that our paper on PyVeritas has been accepted to the P-AI-FM-26 Workshop @ AAAI 2026! 📄