2 citations · 3 across the 14 of their papers we have counts for
4 papers · 1 filter
When Models Know but Can't Linearly Say: Relational Encoding Limits Linear Vulnerability Classification in Code LLMs
Rui Melo, Andre Catarino, Claudia Mamede +2
Large language models can detect vulnerable code, yet the form of vulnerability encoding in their representations remains unclear. We analyze 8 code models spanning 6.7B-15B parame…
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
Nusrat Jahan Mozumder, Divya Gopinath, Corina Pasareanu +1
Deep neural networks are increasingly deployed in safety-critical domains as perception modules, where failures are often caused due to rare and under-represented scenarios. This n…
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu +1
*Concept-based explanations* offer a promising approach for explaining the predictions of deep neural networks in terms of high-level, human-understandable concepts. However, exist…
SecCodePRM: A Process Reward Model for Code Security
Weichen Yu, Ravi Mangal, Yinyi Luo +4
Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detectio…