@inproceedings{chang-etal-2026-flowswitch,
title = "{F}low{S}witch: A State-Aware Framework for Workflow Transitions in Adaptive Dialogue Agents",
author = "Chang, Wen Yu and
Qiu, Luning and
Liu, Yi-Hung and
Chen, Yun-Nung",
editor = "Riccardi, Giuseppe and
Mousavi, Seyed Mahed and
Torres, Maria Ines and
Yoshino, Koichiro and
Callejas, Zoraida and
Chowdhury, Shammur Absar and
Chen, Yun-Nung and
Bechet, Frederic and
Gustafson, Joakim and
Damnati, G{\'e}raldine and
Papangelis, Alex and
D{'}Haro, Luis Fernando and
Mendon{\c{c}}a, John and
Bernardi, Raffaella and
Hakkani-Tur, Dilek and
Di Fabbrizio, Giuseppe {''}Pino{''} and
Kawahara, Tatsuya and
Alam, Firoj and
Tur, Gokhan and
Johnston, Michael",
booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
month = feb,
year = "2026",
address = "Trento, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.iwsds-1.2/",
pages = "18--33",
abstract = "To enhance large language models ({LLM}s) with real-world task-solving capabilities, integrating workflow knowledge into {LLM}s has emerged as a promising direction. However, real-world conversations are inherently dynamic{---}users often shift intents or request actions beyond the scope of the current workflow. Existing systems struggle to detect such transitions and to decide when to retrieve or switch to a new workflow. This paper presents {F}low{S}witch, a state-aware framework that learns when to search for relevant workflows and switch between them during multi-turn dialogues. A policy module determines whether to continue within the current workflow or transition to a new one based on contextual representations. When searching, a retriever identifies the most relevant workflow knowledge given the dialogue state. We conduct comprehensive experiments to explore the optimal configuration of {F}low{S}witch, including workflow format, retrieval input type, and retrieval method. Experimental results show that our framework, when using the agent{'}s self-generated search queries, achieves the highest Top-1 accuracy and Mean Average Precision ({MAP}). Moreover, {F}low{S}witch reduces nearly 50{\%} of search operations, substantially lowering computational cost and response time."
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<abstract>To enhance large language models (LLMs) with real-world task-solving capabilities, integrating workflow knowledge into LLMs has emerged as a promising direction. However, real-world conversations are inherently dynamic—users often shift intents or request actions beyond the scope of the current workflow. Existing systems struggle to detect such transitions and to decide when to retrieve or switch to a new workflow. This paper presents FlowSwitch, a state-aware framework that learns when to search for relevant workflows and switch between them during multi-turn dialogues. A policy module determines whether to continue within the current workflow or transition to a new one based on contextual representations. When searching, a retriever identifies the most relevant workflow knowledge given the dialogue state. We conduct comprehensive experiments to explore the optimal configuration of FlowSwitch, including workflow format, retrieval input type, and retrieval method. Experimental results show that our framework, when using the agent’s self-generated search queries, achieves the highest Top-1 accuracy and Mean Average Precision (MAP). Moreover, FlowSwitch reduces nearly 50% of search operations, substantially lowering computational cost and response time.</abstract>
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%0 Conference Proceedings
%T FlowSwitch: A State-Aware Framework for Workflow Transitions in Adaptive Dialogue Agents
%A Chang, Wen Yu
%A Qiu, Luning
%A Liu, Yi-Hung
%A Chen, Yun-Nung
%Y Riccardi, Giuseppe
%Y Mousavi, Seyed Mahed
%Y Torres, Maria Ines
%Y Yoshino, Koichiro
%Y Callejas, Zoraida
%Y Chowdhury, Shammur Absar
%Y Chen, Yun-Nung
%Y Bechet, Frederic
%Y Gustafson, Joakim
%Y Damnati, Géraldine
%Y Papangelis, Alex
%Y D’Haro, Luis Fernando
%Y Mendonça, John
%Y Bernardi, Raffaella
%Y Hakkani-Tur, Dilek
%Y Di Fabbrizio, Giuseppe ”Pino”
%Y Kawahara, Tatsuya
%Y Alam, Firoj
%Y Tur, Gokhan
%Y Johnston, Michael
%S Proceedings of the 16th International Workshop on Spoken Dialogue System Technology
%D 2026
%8 February
%I Association for Computational Linguistics
%C Trento, Italy
%F chang-etal-2026-flowswitch
%X To enhance large language models (LLMs) with real-world task-solving capabilities, integrating workflow knowledge into LLMs has emerged as a promising direction. However, real-world conversations are inherently dynamic—users often shift intents or request actions beyond the scope of the current workflow. Existing systems struggle to detect such transitions and to decide when to retrieve or switch to a new workflow. This paper presents FlowSwitch, a state-aware framework that learns when to search for relevant workflows and switch between them during multi-turn dialogues. A policy module determines whether to continue within the current workflow or transition to a new one based on contextual representations. When searching, a retriever identifies the most relevant workflow knowledge given the dialogue state. We conduct comprehensive experiments to explore the optimal configuration of FlowSwitch, including workflow format, retrieval input type, and retrieval method. Experimental results show that our framework, when using the agent’s self-generated search queries, achieves the highest Top-1 accuracy and Mean Average Precision (MAP). Moreover, FlowSwitch reduces nearly 50% of search operations, substantially lowering computational cost and response time.
%U https://aclanthology.org/2026.iwsds-1.2/
%P 18-33
Markdown (Informal)
[FlowSwitch: A State-Aware Framework for Workflow Transitions in Adaptive Dialogue Agents](https://aclanthology.org/2026.iwsds-1.2/) (Chang et al., IWSDS 2026)
ACL