Automating Complex Document Workflows via Stepwise and Rollback-Enabled Operation Orchestration

TL;DR

AutoDW is a new framework for automating complex, multi-step document workflows that uses stepwise planning and rollback mechanisms to improve accuracy, fault tolerance, and alignment with user intent.

Contribution

It introduces AutoDW, a novel execution framework with rollback capabilities for orchestrating multi-step document workflows, outperforming existing baselines in realistic scenarios.

Findings

Achieves 90% instruction-level completion rate

Outperforms baselines by 40% on instruction tasks

Remains robust across different LLMs and task difficulties

Abstract

Workflow automation promises substantial productivity gains in everyday document-related tasks. While prior agentic systems can execute isolated instructions, they struggle with automating multi-step, session-level workflows due to limited control over the operational process. To this end, we introduce AutoDW, a novel execution framework that enables stepwise, rollback-enabled operation orchestration. AutoDW incrementally plans API actions conditioned on user instructions, intent-filtered API candidates, and the evolving states of the document. It further employs robust rollback mechanisms at both the argument and API levels, enabling dynamic correction and fault tolerance. These designs together ensure that the execution trajectory of AutoDW remains aligned with user intent and document context across long-horizon workflows. To assess its effectiveness, we construct a comprehensive benchmark of 250 sessions and 1,708 human-annotated instructions, reflecting realistic document processing scenarios with interdependent instructions. AutoDW achieves 90% and 62% completion rates on instruction- and session-level tasks, respectively, outperforming strong baselines by 40% and 76%. Moreover, AutoDW also remains robust for the decision of backbone LLMs and on tasks with varying difficulty. Code and data will be open-sourced. Code: https://github.com/YJett/AutoDW