In this talk, we will present a framework for solving MaxSAT problems expressed in natural language by combining large language models with the PySAT toolkit. We will show how LLMs …
Excited to share that two of our papers have been accepted at EPIA 2026, covering the robustness of LLMs for code understanding and neuro-symbolic feedback for Vision-Language …
In this paper, we propose a neuro-symbolic approach that integrates formal constraint reasoning into the VLM solving process via a Maximum Satisfiability (MaxSAT) oracle. Rather …
Excited to share that three of our papers have been accepted at FLoC 2026, covering automated feedback for Prolog education, data-driven mutation testing for Prolog, and …
In this paper, we study a neuro-symbolic approach in which an LLM translates a natural language description of an optimisation problem into executable Python code using PySAT. The …
This paper introduces MENTOR, a semantic automated program repair (APR) framework designed to fix faulty student programs. MENTOR validates repairs through execution on a test …
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 …
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 …
In this talk, I will present my PhD work on MENTOR, a semantic automated program repair framework designed to provide meaningful feedback for introductory programming assignments.
In this talk, I will present approaches that leverage the precision of formal logic and the adaptability of learning-based models to enable intelligent code generation, automated …
In this talk I will present a hybrid method to automated repair of C code, using Maximum Satisfiability (MaxSAT)-based fault localization, CFaults, to localize bugs and LLMs to …
In my talk, I will present MENTOR, a semantic automated program repair (APR) framework designed to provide Automated Feedback for Introductory Programming Exercises.
Nesta palestra, irei apresentar o sistema MENTOR, uma ferramenta de reparação automática de programas orientada para fornecer feedback automatizado para exercícios introdutórios de …
This PhD thesis presents MENTOR, a semantic automated program repair (APR) framework designed to provide Automated Feedback for Introductory Programming Exercises.
In this talk I will present our novel approach that combines the strengths of both FM-based fault localization and LLMs, via zero-shot learning, to enhance Automated Program …
In this paper, we propose a novel approach that combines the strengths of both FM-based fault localization and LLMs, via zero-shot learning, to enhance APR for IPAs. Our method …