mlsanityData to 14 Oct 2026

Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

arXiv 2602.15377 ↗First posted 17 Feb 2026cs.CL cs.AIcited by 1

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Abstract (arXiv)

Customer service automation has seen growing demand within digital transformation. Existing approaches either rely on modular system designs with extensive agent orchestration or employ over-simplified instruction schemas, providing limited guidance and poor generalizability. This paper introduces an orchestration-free framework using Task-Oriented Flowcharts (TOFs) to enable end-to-end automation without manual intervention. We first define the components and evaluation metrics for TOFs, then formalize a cost-efficient flowchart construction algorithm to abstract procedural knowledge from service dialogues. We emphasize local deployment of small language models and propose decentralized distillation with flowcharts to mitigate data scarcity and privacy issues in model training. Extensive experiments validate the effectiveness in various service tasks, with superior quantitative and application performance compared to strong baselines and market products. By releasing a web-based system demonstration with case studies, we aim to promote streamlined creation of future service automation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 62 references that are in the map. First posted on arXiv 17 Feb 2026.

Builds on

16 of 62 references.

References · 16

  1. 2020A Simple Language Model for Task-Oriented Dialoguecited by 332
  2. 2016A Network-based End-to-End Trainable Task-oriented Dialogue Systemcited by 252
  3. 2024Rethinking LLM Memorization through the Lens of Adversarial Compressioncited by 94
  4. 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 45
  5. 2023Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuningcited by 40
  6. 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understandingcited by 27
  7. 2021End-to-End Learning of Flowchart Grounded Task-Oriented Dialogscited by 20
  8. 2023End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directionscited by 17
  9. 2025R.R.: Unveiling LLM Training Privacy through Recollection and Rankingcited by 10
  10. 2024Dialogue Language Model with Large-Scale Persona Data Engineeringcited by 9
  11. 2024ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correctioncited by 7
  12. 2025Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Researchcited by 5
4 more
  1. 2022Grounding in social media: An approach to building a chit-chat dialogue modelcited by 4
  2. 2025Towards Harnessing the Collaborative Power of Large and Small Models for Domain Taskscited by 4
  3. 2026RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systemscited by 2
  4. 2025CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Predictioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Proactive Dialogue Model with Intent Predictioncited by 0