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arxiv: 2606.12817 · v1 · pith:RNG35ORS · submitted 2026-06-11 · cs.AI

Teach-and-Repeat: Accurately Extracting Operational Knowledge from Mobile Screen Demonstrations to Empower GUI Agents

Yudong Zhang (1) , Lei Hu (1) , Daoyang Liu (2) , Jiawei Liu (1) , Yangfan Luo (1) , Xingyu Liu (1) , Zuojian Wang (1) , Zhilin Gao (1) ((1) Honor Device Co.
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Ltd (2) The Chinese University of Hong Kong Hong Kong China)
This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-27 07:18 UTCgrok-4.3pith:RNG35ORSrecord.jsonopen to challenge →

classification cs.AI
keywords GUI agentsoperational knowledgemobile screen demonstrationsvision-language modelsTeach-and-Repeat paradigmtask automationAndroid WorldUI perception
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The pith

Teach VLM extracts step-wise operational knowledge from mobile screen demonstrations to guide GUI agents more effectively.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that a specialized vision-language model called Teach VLM can accurately convert mobile screen demonstration videos into short natural-language descriptions of operations, including action types, targets, and orders. This is needed because standard VLMs struggle with the variety of UI designs across apps. By using a data flywheel to create training data and a new benchmark for testing, Teach VLM achieves better performance in predicting these operations. The Teach-and-Repeat approach then feeds this knowledge to agents as a reference, leading to better task execution in environments like Android World. A sympathetic reader would care because this turns raw demos into reusable, interpretable instructions that could make automated GUI agents more reliable without constant retraining.

Core claim

Teach VLM translates mobile screen trajectories into operational knowledge by extracting operation-related keyframes from demonstration videos. Trained with data from a systematic flywheel, it outperforms strong VLM baselines on operation semantics prediction using the Chinese Mobile Screen Teach Benchmark. The Teach-and-Repeat paradigm employs this knowledge as an interpretable procedural reference, resulting in consistent Task Success Rate improvements for downstream screen-based execution agents in Android World experiments.

What carries the argument

Teach VLM, a model that extracts and analyzes operation-related keyframes from demonstration videos to generate operational knowledge in natural language.

If this is right

  • Downstream GUI agents achieve higher task success rates when guided by the operational knowledge.
  • The model sets a new state-of-the-art in predicting operation semantics from screen trajectories.
  • Operational knowledge extracted this way provides an interpretable reference that agents can follow without additional adaptation.
  • The data flywheel enables scalable creation of aligned training data for such models.
  • A dedicated benchmark supports fine-grained evaluation of operation extraction accuracy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could extend to desktop or web interfaces using similar demonstration videos if the keyframe extraction generalizes.
  • The generated operational knowledge might serve as additional training signals for improving other vision-language models on UI tasks.
  • Combining knowledge from multiple demonstrations of the same task could produce more robust references for agents.

Load-bearing premise

The extracted operational knowledge is sufficiently accurate and generalizes across heterogeneous UI designs to serve as a reliable procedural reference that improves agent execution without introducing errors or requiring further adaptation.

What would settle it

If experiments show that Teach VLM does not outperform strong VLM baselines on the operation semantics prediction task in the Chinese Mobile Screen Teach Benchmark, or if the Teach-and-Repeat paradigm does not yield Task Success Rate improvements in Android World.

Figures

Figures reproduced from arXiv: 2606.12817 by (2) The Chinese University of Hong Kong, China), Daoyang Liu (2), Hong Kong, Jiawei Liu (1), Lei Hu (1), Ltd, Xingyu Liu (1), Yangfan Luo (1), Yudong Zhang (1), Zhilin Gao (1) ((1) Honor Device Co., Zuojian Wang (1).

Figure 1
Figure 1. Figure 1: Comparison of operational knowledge extraction performance and downstream task execution effectiveness. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed Teach-and-Repeat paradigm. Compared with instruction-driven execution, which directly predicts actions from task instructions and the current screen, our framework converts a user demonstration into keyframe screenshots and uses Teach VLM to generate editable operational knowledge. The generated knowledge can be injected as an external procedural reference to guide downstream task … view at source ↗
Figure 3
Figure 3. Figure 3: Data flywheel for iterative Teach VLM improvement. The pipeline collects mobile demonstrations, extracts [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Teach VLM ablation of keyframe extraction [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Trajectory comparison on NotesRecipeIngredientCount. Operational knowledge guides the executor to use search and retrieve the target recipe, while the failure trajectory enters a browsing loop through Favorite Recipes [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Trajectory comparison on MarkorMoveNote. After the shared Move prefix, operational knowledge helps the executor switch navigation context and select MeetingMinutes, while the failure trajectory repeatedly scrolls within the same dialog [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Trajectory comparison on ExpenseAddMultipleFromGallery. Operational knowledge guides the executor from gallery search to the Pro Expense entry form, but the task still fails because the executor cannot reliably transcribe multi-field expense information from the image into the form [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Trajectory comparison on SportsTrackerTotalDistanceForCategoryOverInterval. Operational knowledge changes the trajectory from manual browsing to targeted search, but the executor still produces an incorrect answer due to temporal filtering and distance aggregation errors [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
read the original abstract

Understanding the digital world on mobile devices is shifting from static UI perception to dynamic action comprehension. This capability enables models to convert visual state transitions into operational knowledge, defined as short natural-language sentences that describe action types, target UI elements, textual arguments, and execution orders. However, due to the highly diverse and heterogeneous UI designs across applications, existing vision-language models (VLMs) struggle to accurately infer these underlying operations. To bridge this gap, we introduce Teach VLM, a core model designed to translate mobile screen trajectories into step-wise operational knowledge by extracting and analyzing operation-related keyframes from demonstration videos. To address the scarcity of aligned training data, we develop a systematic data flywheel for scalable data acquisition. We further introduce a novel Chinese Mobile Screen Teach Benchmark for fine-grained evaluation. Building upon Teach VLM, we propose the Teach-and-Repeat paradigm, where the generated operational knowledge serves as an interpretable procedural reference to guide downstream screen-based execution agents. Extensive evaluations demonstrate that Teach VLM significantly outperforms strong VLM baselines, achieving state-of-the-art performance in operation semantics prediction. Furthermore, experiments in Android World show that our paradigm yields consistent Task Success Rate improvements for downstream agents. Together, Teach VLM and the Teach-and-Repeat paradigm offer a practical pathway from raw demonstrations to reusable task automation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript introduces Teach VLM, a VLM designed to extract step-wise operational knowledge (natural-language sentences describing action types, target UI elements, textual arguments, and execution order) from mobile screen demonstration trajectories by identifying operation-related keyframes. To address data scarcity it builds a systematic data flywheel for scalable acquisition of aligned training data, releases the Chinese Mobile Screen Teach Benchmark for fine-grained evaluation, and proposes the Teach-and-Repeat paradigm in which the extracted knowledge serves as an interpretable procedural reference to guide downstream screen-based execution agents. The paper claims that Teach VLM achieves SOTA performance on operation semantics prediction and that the paradigm yields consistent Task Success Rate gains for agents in Android World.

Significance. If the central claims hold after proper validation, the work would supply a practical route from raw demonstrations to reusable, human-readable procedural knowledge that improves GUI-agent reliability across heterogeneous UIs, addressing a recognized bottleneck in mobile automation.

major comments (1)
  1. [Abstract and §3 (data flywheel description)] The SOTA claim on operation semantics prediction and the downstream TSR improvements both rest on the assumption that the data flywheel produces high-quality aligned labels. No quantitative validation (inter-annotator agreement, spot-check accuracy, or error typology) of the automatically extracted operation sentences is described anywhere in the manuscript. Without such evidence it is impossible to rule out systematic mislabeling of action types, targets, or arguments on heterogeneous UIs, which would render the reported gains artifacts of noisy training data rather than modeling improvement.
minor comments (1)
  1. [Abstract] The abstract asserts “state-of-the-art performance” and “consistent Task Success Rate improvements” yet supplies no numerical metrics, baseline names, dataset sizes, or evaluation protocol, preventing even a high-level assessment of the results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract and §3 (data flywheel description)] The SOTA claim on operation semantics prediction and the downstream TSR improvements both rest on the assumption that the data flywheel produces high-quality aligned labels. No quantitative validation (inter-annotator agreement, spot-check accuracy, or error typology) of the automatically extracted operation sentences is described anywhere in the manuscript. Without such evidence it is impossible to rule out systematic mislabeling of action types, targets, or arguments on heterogeneous UIs, which would render the reported gains artifacts of noisy training data rather than modeling improvement.

    Authors: We agree that the absence of quantitative validation for the data flywheel outputs is a substantive limitation. The manuscript currently describes the flywheel mechanism but does not report inter-annotator agreement, spot-check accuracy, or error typology. In the revision we will add these analyses to §3, including agreement statistics on a held-out sample, manual verification accuracy on 500 extracted sentences across diverse apps, and a breakdown of error categories (e.g., target misidentification on custom UIs). This addition will directly address the concern that reported gains could stem from label noise. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pipeline with independent data collection and evaluation

full rationale

The paper describes an empirical pipeline: Teach VLM trained on data from a systematic data flywheel, evaluated on a new Chinese Mobile Screen Teach Benchmark, and applied in the Teach-and-Repeat paradigm for downstream agents. No equations, derivations, or self-referential definitions appear in the abstract or described structure. Performance claims rest on comparisons to VLM baselines and Android World TSR metrics rather than any fitted parameter renamed as prediction or self-citation chain. The data flywheel is presented as a scalable acquisition method without reducing to the model's own outputs by construction. This is a standard ML empirical setup with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5831 in / 979 out tokens · 17111 ms · 2026-06-27T07:18:57.981737+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

9 extracted references · 3 canonical work pages · 2 internal anchors

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    Android in the zoo: Chain-of-action-thought for GUI agents. InFindings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, pages 12016– 12031. Yuze Zhao, Jintao Huang, Jinghan Hu, Xingjun Wang, Yunlin Mao, Daoze Zhang, Zeyinzi Jiang, Zhikai Wu, Baole Ai, Ang Wang, Wenmeng Zhou, and Yingda Chen. 2024. Sw...

    work page Pith review arXiv 2024

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    6.3 Example of Injecting Operational Knowledge into Android World In Android World, operational knowledge is in- jected as an auxiliary reference strategy in the agent prompt

    stop and set the query as completed This example illustrates the form of procedural guidance used in Teach-and-Repeat: the knowledge is concise, ordered, and expressed in natural lan- guage, making it directly readable by both humans and downstream screen-based agents. 6.3 Example of Injecting Operational Knowledge into Android World In Android World, ope...

  5. [9]

    click on the Google app

  6. [10]

    click on the search bar

  7. [11]

    capital of Canada

    type "capital of Canada"

  8. [12]

    click on the search result

  9. [13]

    Do not copy the sequence blindly

    stop and set the query as completed Use these descriptions as high-level guidance. Do not copy the sequence blindly. Ground decisions in current screenshots. This prompt block is inserted before the agent makes the next action decision. The design sepa- rates perception-derived operation knowledge from closed-loop execution: Teach VLM supplies a reusable ...