  The inaugural ACM International Conference on AI-powered Software introduced
the AIware Challenge, prompting researchers to explore AI-driven tools for
optimizing conference programs through constrained optimization. We investigate
the use of Large Language Models (LLMs) for program scheduling, focusing on
zero-shot learning and integer programming to measure paper similarity. Our
study reveals that LLMs, even under zero-shot settings, create reasonably good
first drafts of conference schedules. When clustering papers, using only titles
as LLM inputs produces results closer to human categorization than using titles
and abstracts with TFIDF. The code has been made publicly available.
