OmegaUse-SOP: SOP Engineering for Professional Computer Use from Human Demonstrations
Yixiong Xiao, Lang An, Hucheng Yang, Pinxue Ma, Yongquan Chen, Jingjia Cao, Yusai Zhao, Ting Wang, et al.
cs.HC · cs.AI
Quick verdict
OmegaUse-SOP converts expert GUI demos into reusable SOP skills, raising three VLMs to 5/5 on five PVsyst tasks, but the case study is tiny and best-of-3.
Summary
The paper presents OmegaUse-SOP, a human-in-the-loop system developed by Baidu with a power-sector client that converts human demonstrations of professional software workflows into reusable, parameterized 'SOP skills' for GUI agents. Four modules form the pipeline: Observe records expert mouse and keyboard actions together with pre-action screenshots and extracts visual targets using OmniParser and PaddleOCRv5; Reason uses a vision-language model to rewrite recorded coordinates and keystrokes as semantic step instructions; Configure lets domain experts add execution rules and task-specific parameters; Execute runs the skill step by step with progressive context disclosure, screen grounding, post-action visual verification against the demonstration, and optional human intervention. In a case study on five photovoltaic simulation tasks in PVsyst 7.2, three vision-language models (Qwen3-VL-235B-A22B-Instruct, GPT-5.5, Opus-4.7) improved from baseline pass rates of 1/5, 3/5, and 2/5 to 5/5 with SOP guidance. An ablation on Qwen3-VL shows that removing the Reason module drops completion from 5/5 to 2/5. Every reported entry is the best of three trials, judged manually by domain experts. Key limitations: evaluation covers a single proprietary application, five tasks, and one domain; best-of-three reporting hides variance; domain guidance was added after observing failures on the same test tasks; and simpler baselines such as text-only SOP instructions are not compared.
Review complete
Assessment at a glance
4
risk flags identified
● high 1
● medium 2
● low 1
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Human demonstrations of professional software procedures can be converted into reusable, parameterized GUI-agent SOP skills via the four-module Observe-Reason-Configure-Execute pipeline.
- Adding OmegaUse-SOP skills raises task completion for Qwen3-VL-235B-A22B-Instruct, GPT-5.5, and Opus-4.7 from baseline 1/5, 3/5, and 2/5 respectively to 5/5 on five PVsyst 7.2 tasks (Table 2).
- The Reason module is essential: without semantic abstraction of demonstrations, Qwen3-VL drops from 5/5 to 2/5 (Table 3).
- Iterative human-in-the-loop SOP Engineering — refining demonstrations, execution rules, domain knowledge, and task parameters — is a practical path to deploying GUI agents in domain-specific professional software.
Strengths
Real deployment problem, honestly scoped system
Targets domain SOPs that general computer-use benchmarks miss (§1), with the human-in-the-loop Execute mechanism stated as deployed in the client's environment and separately open-sourced (ethanyxx/co-work).
Consistent gains across heterogeneous models
Table 2: baseline pass rates of 1/5 (Qwen3-VL-235B-A22B-Instruct), 3/5 (GPT-5.5), 2/5 (Opus-4.7) all reach 5/5 with SOP skills — the mechanism is model-agnostic rather than tuned to one backbone.
Reason ablation isolates semantic abstraction
Grounding-oriented design discipline
Code and demo released
Weaknesses
Very narrow evaluation
Best-of-3 reporting with manual judgment
Skill tuned on the test tasks
Missing simpler baselines and partial ablation
Adoption cost unquantified
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Scope & Claims
OmegaUse-SOP (Baidu + Ningxia Electric Power Engineering) is a human-in-the-loop 'SOP Engineering' system that converts expert demonstrations of professional software workflows into reusable, parameterized GUI-agent skills. The claimed contributions are: (1) a four-module pipeline — Observe (event/screenshot recording with OmniParser + PaddleOCRv5 target extraction), Reason (VLM-based semantic abstraction of recorded actions), Configure (expert-authored domain guidance and task parameters), Execute (step-wise retrieval, grounding, action generation, and post-action visual verification with human intervention); (2) a case study on five PVsyst 7.2 photovoltaic-simulation tasks showing three VLMs improve from 1/5, 3/5, 2/5 baselines to 5/5 each with SOP skills (Table 2); and (3) an ablation attributing the gain largely to the Reason module (5/5 to 2/5 without it, Table 3). The 'prompt engineering for procedures' framing is intuitive, and the problem — domain SOPs beyond general computer-use benchmarks — is real and well motivated via SOP-Bench and Workflow-GYM.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 4 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is OmegaUse-SOP?
A human-in-the-loop system from Baidu and a power-sector client that records how an expert performs a procedure in professional software (e.g., PVsyst 7.2), converts the recording into semantic step-by-step instructions with screenshots, lets domain experts add execution rules and task parameters, and then has a GUI agent execute the resulting 'SOP skill' in the live application with per-step visual verification and optional human intervention.
How much does OmegaUse-SOP improve GUI agent success rates?
In the paper's case study on five PVsyst 7.2 photovoltaic simulation tasks, baseline pass rates were 1/5 for Qwen3-VL-235B-A22B-Instruct, 3/5 for GPT-5.5, and 2/5 for Opus-4.7; with SOP skills all three reached 5/5. Caveats: each number is the best of three trials, scored manually by domain experts, and some expert guidance was added after observing failures on these same tasks.
Is OmegaUse-SOP open source?
Yes. The paper links code at github.com/baidu-frontier-research/omegause-sop plus a YouTube demo; the human-in-the-loop execution mechanism has a separate open implementation at github.com/ethanyxx/co-work. Reproducing the experiments additionally requires a PVsyst 7.2 license and API access to the evaluated vision-language models.
What is 'SOP engineering'?
The paper's term for iteratively refining demonstrations, execution rules, domain knowledge, and task parameters — analogous to prompt engineering — until a GUI agent can reliably carry out a professional standard operating procedure in software that has no programmatic API.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1-4 and the Table 4 panels are images; the system diagram and PVsyst UI screenshots could not be inspected from the text extraction.
- The supplied metadata lists 8 authors, but the paper byline includes 14 (adding Ting Liu, Siqi Bao, Jingbo Zhou, Hua Wu); this review follows the byline.
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