@inproceedings{zhou-etal-2026-wise,
title = "{WISE}-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents",
author = "Zhou, Yuqing and
Wang, Zhuoer and
Jk and
Wang, Hong and
Koelle, Samson and
Zhu, Ziwei and
Niu, Wei",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.157/",
pages = "3124--3143",
ISBN = "979-8-89176-444-6",
abstract = "Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks, tend to repeat the same failure patterns, and show substantial run-to-run variability. Fixing failures via environment-specific training or manual patching is costly and hard to scale. To enable self-evolving agents in user-facing service environments, we propose WISE-Flow, a workflow-centric framework that converts historical service interactions into reusable procedural experience by inducing workflows with prerequisite-augmented action blocks. At deployment, WISE-Flow aligns the agent{'}s execution trajectory to retrieved workflows and performs prerequisite-aware feasibility reasoning to achieve state-grounded next actions. Experiments on ToolSandbox and $\tau^2$-bench show consistent improvement across base models."
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<abstract>Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks, tend to repeat the same failure patterns, and show substantial run-to-run variability. Fixing failures via environment-specific training or manual patching is costly and hard to scale. To enable self-evolving agents in user-facing service environments, we propose WISE-Flow, a workflow-centric framework that converts historical service interactions into reusable procedural experience by inducing workflows with prerequisite-augmented action blocks. At deployment, WISE-Flow aligns the agent’s execution trajectory to retrieved workflows and performs prerequisite-aware feasibility reasoning to achieve state-grounded next actions. Experiments on ToolSandbox and τ²-bench show consistent improvement across base models.</abstract>
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%0 Conference Proceedings
%T WISE-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents
%A Zhou, Yuqing
%A Wang, Zhuoer
%A Wang, Hong
%A Koelle, Samson
%A Zhu, Ziwei
%A Niu, Wei
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%A Jk
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F zhou-etal-2026-wise
%X Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks, tend to repeat the same failure patterns, and show substantial run-to-run variability. Fixing failures via environment-specific training or manual patching is costly and hard to scale. To enable self-evolving agents in user-facing service environments, we propose WISE-Flow, a workflow-centric framework that converts historical service interactions into reusable procedural experience by inducing workflows with prerequisite-augmented action blocks. At deployment, WISE-Flow aligns the agent’s execution trajectory to retrieved workflows and performs prerequisite-aware feasibility reasoning to achieve state-grounded next actions. Experiments on ToolSandbox and τ²-bench show consistent improvement across base models.
%U https://aclanthology.org/2026.findings-aacl.157/
%P 3124-3143
Markdown (Informal)
[WISE-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents](https://aclanthology.org/2026.findings-aacl.157/) (Zhou et al., Findings 2026)
ACL