Abstract Code Large Language Models (Code LLMs) have excelled at tasks like code
completion but often miss deeper semantics such as execution effects and dynamic states …

Code large language models (LLMs) have made significant progress in code debugging by
directly generating the correct code based on the buggy code snippet. Programming …

In the context of the rising interest in code language models (code LMs) and vulnerability
detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our analysis …

Recent advancements in large language models (LLMs) have showcased impressive code
generation capabilities, primarily evaluated through language-to-code benchmarks …

Large Language Models (LLMs) have recently demonstrated remarkable coding
capabilities. However, assessing code generation based on well-formed properties and …

AI for Mathematics (AI4Math) is not only intriguing intellectually but also crucial for AI-driven
discovery in science, engineering, and beyond. Extensive efforts on AI4Math have mirrored …

We investigate how combinations of Large Language Models (LLMs) and symbolic analyses
can be used to synthesise specifications of C programs. The LLM prompts are augmented …

Self-correction has emerged as a promising solution to boost the reasoning performance of
large language models (LLMs), where LLMs refine their solutions using self-generated …

Code Large Language Models (Code LLMs) have excelled at tasks like code completion but
often miss deeper semantics such as execution effects and dynamic states. This paper aims …

Recent decades have seen the unprecedented success of Artificial Intelligence (AI), with its
impact resonating beyond the confines of the technology sector to influence fields as diverse …