πππ 3 Papers accepted @ FLoC 2026!! πππ
Image credit: FLoC 2026I’m very excited to share that three of our papers have been accepted at FLoC 2026! π
Two papers were accepted to the 42nd International Conference on Logic Programming (ICLP 2026), while a third paper was accepted to the LLMs meet Constraint Solving (LLM-Solve 2026) Workshop.
π Can Automated Feedback Turn Students into Happy Prologians?
Providing personalised feedback is crucial for effective learning, but delivering it at scale remains challenging. In this paper, we present ProHelp, an automated assessment platform for Prolog built on top of GitSEED, and evaluate it through a large-scale deployment in an undergraduate logic programming course. Our results show that students perceive automated feedback as highly valuable, with automatic testing, open-choice-point warnings, and predicate-level scoring emerging as the most useful feedback mechanisms.
- (2026). Can Automated Feedback Turn Students into Happy Prologians?. In ICLP 2026.
π What Bugs Do Prolog Students Write? An Empirical Taxonomy and Data-Driven Mutation Framework
Automated feedback and repair systems require realistic bug datasets that reflect the mistakes students actually make. In this paper, we analyse 7,201 Prolog submissions from 265 students to construct a detailed taxonomy of Prolog programming errors. Based on this empirical study, we introduce LogMorph, a data-driven mutation framework that generates realistic buggy Prolog programs according to the observed distribution of student mistakes, enabling more representative evaluation of debugging, repair, and educational tools.
π€ Solving MaxSAT Problems from Natural Language Descriptions with LLMs and PySAT
Can large language models make formal optimisation technologies accessible through natural language? In this workshop paper, we explore a neuro-symbolic approach in which an LLM translates a natural language problem description into executable PySAT code that constructs and solves a Maximum Satisfiability (MaxSAT) instance. By combining the flexibility of LLMs for semantic interpretation with the reliability of exact MaxSAT solvers, the approach significantly improves over direct LLM-based problem solving while maintaining formal correctness guarantees.
- (2026). Solving MaxSAT Problems from Natural Language Descriptions with LLMs and PySAT. In LLM-Solve @ FLoC 2026.
I’m very grateful to all my collaborators!
See you in Lisbon @ FLoC 2026!!
