@inproceedings{yang-etal-2026-attribution,
title = "Attribution-Based Analysis and Optimization of Modular Agentic Workflows",
author = "Yang, Yingxuan and
Huang, Bo and
Qi, Siyuan and
Feng, Chao and
Hu, Haoyi and
Zhu, Yuxuan and
Hu, Jinbo and
Zhao, Haoran and
He, Ziyi and
Liu, Xiao and
Wang, ZongYu and
Wen, Muning and
Qiu, Lin and
Cao, Xuezhi and
Cai, Xunliang and
Yu, Yong and
Zhang, Weinan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.359/",
pages = "7247--7264",
ISBN = "979-8-89176-395-1",
abstract = "Agentic workflows solve complex tasks by orchestrating modular components (e.g., planning, reasoning, action, reflection) built on top of LLM backbones. A practical but underexplored question is model allocation: given a fixed workflow decomposition and a pool of candidate LLMs, which components should be upgraded (and with which models) to upgrade task performance, and how can we attribute gains to individual upgrades and their interactions?We present ShapleyFlow, a cooperative game theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. This yields interaction-aware attribution and supports Shapley-guided configuration recommendation for model allocation under a fixed workflow structure.We further introduce CapaBench, a benchmark of 1,500+ tasks across seven domains (shopping, navigation, ticketing, mathematics, operating systems, robotic coordination, and automated theorem proving).Across 9 representative LLMs and all $2^4$ upgrade coalitions in a 4-component workflow, ShapleyFlow provides (i) principled, interaction-aware attribution for modular workflows and (ii) actionable model-allocation recommendations that improve over strong single-model baselines."
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<abstract>Agentic workflows solve complex tasks by orchestrating modular components (e.g., planning, reasoning, action, reflection) built on top of LLM backbones. A practical but underexplored question is model allocation: given a fixed workflow decomposition and a pool of candidate LLMs, which components should be upgraded (and with which models) to upgrade task performance, and how can we attribute gains to individual upgrades and their interactions?We present ShapleyFlow, a cooperative game theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. This yields interaction-aware attribution and supports Shapley-guided configuration recommendation for model allocation under a fixed workflow structure.We further introduce CapaBench, a benchmark of 1,500+ tasks across seven domains (shopping, navigation, ticketing, mathematics, operating systems, robotic coordination, and automated theorem proving).Across 9 representative LLMs and all 2⁴ upgrade coalitions in a 4-component workflow, ShapleyFlow provides (i) principled, interaction-aware attribution for modular workflows and (ii) actionable model-allocation recommendations that improve over strong single-model baselines.</abstract>
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%0 Conference Proceedings
%T Attribution-Based Analysis and Optimization of Modular Agentic Workflows
%A Yang, Yingxuan
%A Huang, Bo
%A Qi, Siyuan
%A Feng, Chao
%A Hu, Haoyi
%A Zhu, Yuxuan
%A Hu, Jinbo
%A Zhao, Haoran
%A He, Ziyi
%A Liu, Xiao
%A Wang, ZongYu
%A Wen, Muning
%A Qiu, Lin
%A Cao, Xuezhi
%A Cai, Xunliang
%A Yu, Yong
%A Zhang, Weinan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-attribution
%X Agentic workflows solve complex tasks by orchestrating modular components (e.g., planning, reasoning, action, reflection) built on top of LLM backbones. A practical but underexplored question is model allocation: given a fixed workflow decomposition and a pool of candidate LLMs, which components should be upgraded (and with which models) to upgrade task performance, and how can we attribute gains to individual upgrades and their interactions?We present ShapleyFlow, a cooperative game theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. This yields interaction-aware attribution and supports Shapley-guided configuration recommendation for model allocation under a fixed workflow structure.We further introduce CapaBench, a benchmark of 1,500+ tasks across seven domains (shopping, navigation, ticketing, mathematics, operating systems, robotic coordination, and automated theorem proving).Across 9 representative LLMs and all 2⁴ upgrade coalitions in a 4-component workflow, ShapleyFlow provides (i) principled, interaction-aware attribution for modular workflows and (ii) actionable model-allocation recommendations that improve over strong single-model baselines.
%U https://aclanthology.org/2026.findings-acl.359/
%P 7247-7264
Markdown (Informal)
[Attribution-Based Analysis and Optimization of Modular Agentic Workflows](https://aclanthology.org/2026.findings-acl.359/) (Yang et al., Findings 2026)
ACL
- Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, and Weinan Zhang. 2026. Attribution-Based Analysis and Optimization of Modular Agentic Workflows. In Findings of the Association for Computational Linguistics: ACL 2026, pages 7247–7264, San Diego, California, United States. Association for Computational Linguistics.