# Investigating the Potential of Using Large Language Models for Scheduling

arXiv: https://arxiv.org/abs/2406.07573 · HTML: https://mlsanity.com/paper/2406.07573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference](https://mlsanity.com/paper/2310.03184/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Sample-Efficient Optimization over Generative Priors via Coarse Learnability](https://mlsanity.com/paper/2503.06917/) (2025)

## Abstract (arXiv)

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.