5 citations · 7 across the 8 of their papers we have counts for
8 papers
Not All Code Is Equal: A Data-Centric Study of Code Complexity and LLM Reasoning
Lukas Twist, Shu Yang, Hanqi Yan +4
Large Language Models (LLMs) increasingly exhibit strong reasoning abilities, often attributed to their capacity to generate chain-of-thought-style intermediate reasoning. Recent w…
Evolving Excellence: Automated Optimization of LLM-based Agents
Paul Brookes, Vardan Voskanyan, Rafail Giavrimis +18
Agentic AI systems built on large language models (LLMs) offer significant potential for automating complex workflows, from software development to customer support. However, LLM a…
AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
Huanting Wang, Jingzhi Gong, Huawei Zhang +2
AI agentic programming is an emerging paradigm where large language model (LLM)-based coding agents autonomously plan, execute, and interact with tools such as compilers, debuggers…
Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi +7
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and…
Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
Jingzhi Gong, Rafail Giavrimis, Paul Brookes +8
There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critic…
Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design
Jingzhi Gong
With the rapid advancement of AI, software engineering increasingly relies on AI-driven approaches, particularly language models (LMs), to enhance code performance. However, the tr…