@inproceedings{lee-etal-2025-compound,
title = "Compound {AI} Systems Optimization: A Survey of Methods, Challenges, and Future Directions",
author = "Lee, Yu-Ang and
Yi, Guan-Ting and
Liu, Mei-Yi and
Lu, Jui-Chao and
Yang, Guan-Bo and
Chen, Yun-Nung",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1463/",
doi = "10.18653/v1/2025.emnlp-main.1463",
pages = "28760--28775",
ISBN = "979-8-89176-332-6",
abstract = "Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field."
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%0 Conference Proceedings
%T Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions
%A Lee, Yu-Ang
%A Yi, Guan-Ting
%A Liu, Mei-Yi
%A Lu, Jui-Chao
%A Yang, Guan-Bo
%A Chen, Yun-Nung
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F lee-etal-2025-compound
%X Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field.
%R 10.18653/v1/2025.emnlp-main.1463
%U https://aclanthology.org/2025.emnlp-main.1463/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1463
%P 28760-28775
Markdown (Informal)
[Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions](https://aclanthology.org/2025.emnlp-main.1463/) (Lee et al., EMNLP 2025)
ACL
- Yu-Ang Lee, Guan-Ting Yi, Mei-Yi Liu, Jui-Chao Lu, Guan-Bo Yang, and Yun-Nung Chen. 2025. Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 28760–28775, Suzhou, China. Association for Computational Linguistics.