Recent advances in reasoning techniques have substantially improved the performance of large language models (LLMs), raising expectations for their ability to p...
Debugging is one of the most time-consuming and expensive tasks in software development and circuit design. Several formula-based fault localisation (FBFL) meth...
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterat...
Test-time policy optimization enables large language models (LLMs) to adapt to distribution shifts by leveraging feedback from self-generated rollouts. However,...
Majority voting has proven effective for close-ended question answering by aggregating parallel reasoning traces. However, it is not directly applicable to open...
Hate speech recognition in low-resource languages remains a difficult problem due to insufficient datasets, orthographic heterogeneity, and linguistic variety. ...
High Performance Computing (HPC) centers provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with hardware ...
Code-generating LLMs are trained largely on static artifacts (source, comments, specifications) and rarely on materializations of run-time behavior. As a result...
Digital sound synthesis presents the opportunity to explore vast parameter spaces containing millions of configurations. Quality diversity (QD) evolutionary alg...
Emerging alongside generative AI and the broader trend of AI-assisted coding, the term 'vibe coding' refers to creating software via natural language prompts ra...
Data science initiatives frequently exhibit high failure rates, driven by technical constraints, organizational limitations and insufficient risk management pra...
Migrating a set of software variants into a software product line (SPL) is an expensive and potentially challenging endeavor. Indeed, SPL engineering can signif...
