11 papers
Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry
Jakob Solberg Berntzen, Safia Fatima, Leon Moonen
The paper proposes a lightweight two‑stage recovery method that lets low‑cost ground robots follow painted floor lines using only a single camera, by first relaxing line detection…
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Safia Fatima, Kai Olav Ellefsen, Leon Moonen
Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenarios. RL policies often stall in…
Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair
Fernando Vallecillos-Ruiz, Giordano d'Aloisio, Max Hort +3
Large Language Models (LLMs) are powerful tools and have been increasingly adopted for complex software engineering tasks. As the number of parameters increases, results can often…
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
Sushant Gautam, Finn Schwall, Annika Willoch Olstad +6
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this sett…
Wisdom and Delusion of LLM Ensembles for Code Generation and Repair
Fernando Vallecillos-Ruiz, Max Hort, Leon Moonen
Today's pursuit of a single Large Language Model (LMM) for all software engineering tasks is resource-intensive and overlooks the potential benefits of complementarity, where diffe…
Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation
Fernando Vallecillos Ruiz, Anastasiia Grishina, Max Hort +1
Research shows that errors in natural language can be corrected by translating texts to another language and back using language models. We explore to what extent this latent corre…