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

arXiv: https://arxiv.org/abs/2602.15377 · HTML: https://mlsanity.com/paper/2602.15377/

## Key facts

- Cited by 1 papers 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 (most cited first, 16 of 16)

- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Rethinking LLM Memorization through the Lens of Adversarial Compression](https://mlsanity.com/paper/2404.15146/) (2024)
- [Training Data Extraction From Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2305.16157/) (2023)
- [Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning](https://mlsanity.com/paper/2305.11759/) (2023)
- [FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding](https://mlsanity.com/paper/2407.05183/) (2024)
- [End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs](https://mlsanity.com/paper/2109.07263/) (2021)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [R.R.: Unveiling LLM Training Privacy through Recollection and Ranking](https://mlsanity.com/paper/2502.12658/) (2025)
- [Dialogue Language Model with Large-Scale Persona Data Engineering](https://mlsanity.com/paper/2412.09034/) (2024)
- [ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction](https://mlsanity.com/paper/2412.03075/) (2024)
- [Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research](https://mlsanity.com/paper/2505.24354/) (2025)
- [Grounding in social media: An approach to building a chit-chat dialogue model](https://mlsanity.com/paper/2206.05696/) (2022)
- [Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks](https://mlsanity.com/paper/2504.17421/) (2025)
- [RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems](https://mlsanity.com/paper/2601.02917/) (2026)
- [CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction](https://mlsanity.com/paper/2505.13558/) (2025)

## Built on by (most cited first, 1 of 1)

- [Proactive Dialogue Model with Intent Prediction](https://mlsanity.com/paper/2604.27379/) (2026)

## 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.