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

Source: arXiv - 2512.04445v1

Overview

The paper presents AutoDW, a new framework that lets large language models (LLMs) orchestrate complex, multi‑step document‑processing workflows with fine‑grained control and built‑in rollback capabilities. By breaking down a user’s high‑level request into a sequence of API calls that can be undone or corrected on the fly, AutoDW bridges the gap between “single‑shot” LLM assistants and the robust, session‑level automation needed in real‑world office software.

Key Contributions

• Stepwise planning engine that incrementally selects and conditions API actions on the user’s intent, filtered candidate APIs, and the current document state.
• Dual‑level rollback mechanism (argument‑level and API‑level) that automatically reverts erroneous operations, enabling fault‑tolerant long‑horizon execution.
• Comprehensive benchmark of 250 realistic document‑processing sessions (1,708 human‑annotated instructions) covering inter‑dependent tasks such as editing, formatting, data extraction, and version control.
• Strong empirical gains: 90 % instruction‑level completion and 62 % session‑level completion, outperforming the best baselines by 40 % and 76 % respectively.
• Backbone‑agnostic design that works with different LLMs and scales across task difficulty levels.

Methodology

1. Intent Extraction – The user’s natural‑language request is parsed by an LLM to infer the high‑level goal (e.g., “update the table of contents after adding a new chapter”).
2. Candidate API Filtering – A curated library of document‑manipulation APIs (e.g., insert_paragraph, apply_style, save_version) is filtered using the inferred intent, reducing the search space to the most relevant actions.
3. Stepwise Planning – For each step, the system prompts the LLM to generate a concrete API call together with its arguments, conditioned on the current document state (captured as a lightweight JSON snapshot). The plan is executed immediately, and the state is updated.
4. Rollback‑Enabled Execution –
   • Argument‑level rollback: If an argument is invalid (e.g., a non‑existent paragraph index), the system automatically revises it before the API call proceeds.
   • API‑level rollback: If an API call produces an unexpected document change, the framework reverts the document to the previous snapshot and asks the LLM to propose an alternative step.
5. Iterative Loop – The process repeats until the user’s high‑level goal is satisfied or a termination condition (max steps, timeout) is reached.

The entire pipeline is orchestrated by a lightweight controller that logs every action, making debugging and audit trails straightforward.

Results & Findings

Key takeaways

• The rollback mechanisms dramatically reduce error propagation, especially in long sessions where a single mistake could derail the entire workflow.
• Incremental state‑aware planning yields more precise API arguments than a single‑shot “plan‑then‑execute” approach.
• AutoDW’s modular API library makes it easy to extend to new document formats (Word, LaTeX, HTML) without retraining the LLM.

Practical Implications

• Productivity tools: Integrating AutoDW into office suites (e.g., Microsoft Office, Google Docs) could let users describe complex edits in plain language (“convert all headings to Title Case and renumber the figures”) and have the system execute them safely.
• Enterprise automation: Companies can encode SOPs (standard operating procedures) as reusable API libraries, letting non‑technical staff trigger multi‑step document pipelines (contract generation → compliance check → e‑signature) with a single chat command.
• Developer ergonomics: The framework’s clear action logs and rollback traces simplify debugging of LLM‑driven bots, reducing the need for manual guardrails.
• Compliance & audit: Because every step is recorded and reversible, organizations can maintain an immutable trail of document changes—critical for regulated industries.

Limitations & Future Work

• API coverage: The current prototype supports a curated set of document‑manipulation APIs; extending to niche formats (CAD drawings, legal PDFs) will require additional engineering.
• Scalability of state snapshots: For very large documents, maintaining full snapshots for rollback can be memory‑intensive; future work will explore diff‑based storage.
• User intent ambiguity: When instructions are vague, the system may generate sub‑optimal plans; incorporating clarification dialogs could improve robustness.
• Generalization to non‑document domains: While the authors hypothesize that the stepwise‑rollback paradigm applies to other workflow types (e.g., data pipelines), empirical validation is left for later studies.

AutoDW opens a promising path toward truly autonomous, error‑resilient document assistants—turning natural‑language commands into reliable, multi‑step operations that developers and end‑users can trust.

Authors

• Yanbin Zhang
• Hanhui Ye
• Yue Bai
• Qiming Zhang
• Liao Xiang
• Wu Mianzhi
• Renjun Hu

Paper Information

• arXiv ID: 2512.04445v1
• Categories: cs.SE, cs.AI
• Published: December 4, 2025
• PDF: Download PDF