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arxiv: 2606.04701 · v1 · pith:J7U4NC36new · submitted 2026-06-03 · 💻 cs.CV · cs.CL

Benchmarking Living-Screen-Native GUI Agents on Short-Video Platforms

Jiashu Yao , Heyan Huang , Daiqing Wu , Wangke Chen , Huaxi Ai , Haoyu Wen , Zeming Liu , Yuhang Guo This is my paper

Pith reviewed 2026-06-28 07:17 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords GUI agentsshort-video platformsliving screensbenchmarkobservation controldynamic interfacesaccuracy-efficiency metrics
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0 comments X

The pith

Current GUI agents cannot handle living screens on short-video platforms because none reach human cost-accuracy performance due to failures in observation control.

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

GUI agents today assume screens stay frozen between actions. Short-video platforms violate this by keeping content in motion, so competent agents must choose what to watch and for how long. The paper formalizes this setting as Living-Screen-Native GUI agents and releases LivingScreen, the first benchmark that supplies a browser-based environment, a three-tier task suite, and metrics that score both accuracy and information efficiency together. Frontier models tested on the benchmark all fall short of human performance, with over-observation and under-observation as the dominant errors.

Core claim

The paper establishes that living-screen-native tasks on short-video platforms expose a gap in current GUI agents, with the LivingScreen benchmark demonstrating that models' dominant errors stem from improper observation lengths, preventing them from attaining human cost-accuracy performance.

What carries the argument

LivingScreen benchmark that supplies a browser-based environment, three-tier task suite, and joint accuracy-efficiency metrics to evaluate agents on dynamic short-video interfaces.

If this is right

  • Observation control becomes a required capability axis for future GUI agents.
  • Models must learn to decide when to stop observing to close the efficiency gap with humans.
  • Over- and under-observation errors can be used as diagnostic signals for targeted agent improvements.
  • The benchmark supplies a concrete standard for comparing agents on real dynamic interfaces.

Where Pith is reading between the lines

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

  • Agents could gain from internal timers or predictive models that estimate content relevance over time.
  • The same evaluation approach could apply to other continuous interfaces such as live streams or interactive media.
  • Joint accuracy-efficiency metrics may become standard for measuring agent performance in time-sensitive apps.

Load-bearing premise

The browser-based environment together with the three-tier task suite and joint accuracy-efficiency metrics faithfully represent real short-video platform interactions without simulation artifacts that distort model comparisons.

What would settle it

A model equipped with explicit observation control that reaches or exceeds human cost-accuracy scores on the LivingScreen tasks.

Figures

Figures reproduced from arXiv: 2606.04701 by Daiqing Wu, Haoyu Wen, Heyan Huang, Huaxi Ai, Jiashu Yao, Wangke Chen, Yuhang Guo, Zeming Liu.

Figure 1
Figure 1. Figure 1: Positioning living-screen-native GUI agents. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of LIVINGSCREEN. Feed construction To mimic the mixture of top￾ical consistency and diversity produced by a real recommender, we use the keyword sets from each video and cluster the corpus. For each task, we uni￾formly choose a ratio r% between 50% − 200%, draw an anchor cluster and combine it with r% as many randomly-sampled unrelated videos, then shuffle the combined pool to form a feed. Thi… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of per-video observation be [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: shows the full LIVINGSCREEN interface together with annotations of the major UI compo￾nents agents can interact with. The viewport is fixed at a typical mobile resolution ( XX×XX px) and reproduces the layout of a modern short-video application, including the autoplaying video region, the progress bar, the side action rail (like, comment, collect, share), the comment drawer, and the swipe￾to-next-video ges… view at source ↗
read the original abstract

GUI agents today assume a static screen, where the world is frozen between two actions. However, real interfaces such as short-video applications violate this assumption, as their content keeps playing, and a competent user must decide what to watch and for how long. We formalize this task as Living-Screen-Native GUI agents and introduce LivingScreen, the first benchmark instantiating it on short-video platforms, with a faithful browser-based environment, a three-tier task suite, and metrics that jointly score accuracy and information efficiency. Evaluating extensive frontier models, we find that none reaches the human cost-accuracy performance, and that their dominant failure mode is over- and under-observation, pointing to observation control as a missing capability axis for future GUI agents. All data and code will be available at https://github.com/BITHLP/LivingScreen.

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

2 major / 1 minor

Summary. The paper introduces 'Living-Screen-Native GUI agents' to address dynamic, continuously updating interfaces (e.g., short-video platforms) that violate the static-screen assumption of prior GUI agents. It presents the LivingScreen benchmark, consisting of a browser-based environment, a three-tier task suite, and joint accuracy-efficiency metrics. Evaluation of frontier models shows none reach human cost-accuracy performance, with dominant failures in over- and under-observation, and concludes that observation control is a missing capability axis. All data and code are to be released.

Significance. If the benchmark environment and tasks are shown to faithfully reproduce native short-video dynamics, the work identifies a concrete, previously unmeasured limitation in GUI agents and supplies an open benchmark for future progress on observation control. The explicit release of data and code strengthens reproducibility.

major comments (2)
  1. [Abstract] Abstract: The central empirical claim that 'none reaches the human cost-accuracy performance' and that 'dominant failure mode is over- and under-observation' rests on the assertion of a 'faithful browser-based environment.' No validation (timing logs, content-equivalence checks, or latency comparisons against native apps) is described, which directly affects whether the reported observation failures can be attributed to model limitations rather than simulation artifacts.
  2. [Methodology / Evaluation] Task suite and human baseline (methodology section): Details on task construction, human baseline collection protocol, exclusion criteria, and statistical tests for the 'no model matches human' result are absent from the abstract and not verifiable here; without them the cross-model comparison and the claim that observation control is the primary gap cannot be assessed for robustness.
minor comments (1)
  1. [Abstract] The three-tier task suite is referenced but its precise definitions, difficulty progression, and relation to the joint accuracy-efficiency metric are not summarized in the abstract.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments correctly identify areas where additional evidence and detail would strengthen the manuscript. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central empirical claim that 'none reaches the human cost-accuracy performance' and that 'dominant failure mode is over- and under-observation' rests on the assertion of a 'faithful browser-based environment.' No validation (timing logs, content-equivalence checks, or latency comparisons against native apps) is described, which directly affects whether the reported observation failures can be attributed to model limitations rather than simulation artifacts.

    Authors: We agree that the abstract asserts faithfulness without accompanying validation evidence. The full manuscript describes the browser-based environment but does not include the requested checks. In revision we will add an explicit validation subsection (or appendix) reporting timing logs, content-equivalence metrics, and latency comparisons against native apps to support the claim that observed failures are attributable to model behavior rather than simulation artifacts. revision: yes

  2. Referee: [Methodology / Evaluation] Task suite and human baseline (methodology section): Details on task construction, human baseline collection protocol, exclusion criteria, and statistical tests for the 'no model matches human' result are absent from the abstract and not verifiable here; without them the cross-model comparison and the claim that observation control is the primary gap cannot be assessed for robustness.

    Authors: The full manuscript contains a methodology section that describes the three-tier task suite and human baseline collection. However, these details are not summarized in the abstract and the statistical tests are not explicitly highlighted. We will revise by expanding the abstract or adding a concise methodology summary, and by reporting the human-collection protocol, exclusion criteria, and statistical tests used for the cross-model comparisons. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark evaluation with no derivations or self-referential reductions

full rationale

The paper is a benchmark study introducing LivingScreen for GUI agents on short-video platforms. It contains no equations, derivations, fitted parameters, or mathematical claims that could reduce to inputs by construction. Central claims rest on empirical evaluation of models in a described environment rather than any self-definitional, fitted-prediction, or self-citation-load-bearing steps. No load-bearing uniqueness theorems or ansatzes are invoked. This is a standard honest non-finding for an empirical benchmark paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

Benchmark paper whose central claim rests on the untested premise that the constructed environment and metrics capture the essential dynamics of real short-video use; no free parameters or invented physical entities.

axioms (2)
  • domain assumption Human cost-accuracy performance constitutes the appropriate reference point for evaluating agent competence on this task.
    Invoked when stating that no model reaches human performance.
  • domain assumption Joint scoring of accuracy and information efficiency is the right way to measure success for living-screen agents.
    Used to define the metrics that support the main claim.
invented entities (1)
  • Living-Screen-Native GUI agents no independent evidence
    purpose: New category of agent that must actively control observation duration on continuously changing screens.
    Introduced as the central object of study; no independent evidence outside the benchmark itself.

pith-pipeline@v0.9.1-grok · 5693 in / 1376 out tokens · 32588 ms · 2026-06-28T07:17:43.134136+00:00 · methodology

discussion (0)

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Reference graph

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