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arxiv: 2605.00551 · v1 · submitted 2026-05-01 · 💻 cs.CL · cs.AI

A11y-Compressor: A Framework for Enhancing the Efficiency of GUI Agent Observations through Visual Context Reconstruction and Redundancy Reduction

Michito Takeshita , Takuro Kawada , Takumi Ohashi , Shunsuke Kitada , Hitoshi Iyatomi This is my paper

Pith reviewed 2026-05-09 19:38 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords GUI agentsaccessibility treescompressionredundancy reductionOSWorld benchmarktoken efficiencysemantic structuringmodal detection
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The pith

A lightweight pipeline compresses accessibility trees to 22 percent of their original tokens while raising average GUI agent success rates by 5.1 points.

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

The paper introduces A11y-Compressor to improve how AI agents observe and ground actions in graphical user interfaces. Standard accessibility trees are linearized, redundant, and missing explicit spatial cues, which wastes tokens and can confuse decision-making. The proposed method applies modal detection, redundancy reduction, and semantic structuring to produce a compact representation called Compressed-a11y. Experiments on the OSWorld benchmark show the new format uses only 22 percent of the original tokens. The same agents achieve higher task completion rates, indicating that much of the removed content was not essential for reliable interaction.

Core claim

A11y-Compressor transforms linearized accessibility trees into compact and structured representations through a pipeline of modal detection, redundancy reduction, and semantic structuring, yielding Compressed-a11y that reduces input tokens to 22 percent of the original while improving task success rates by 5.1 percentage points on average on the OSWorld benchmark.

What carries the argument

The structured transformation pipeline of modal detection, redundancy reduction, and semantic structuring that converts verbose linearized accessibility trees into compact representations.

If this is right

  • Agents can complete more interaction steps or incorporate additional context within the same token budget.
  • Performance gains indicate that standard accessibility trees contain substantial redundant or low-value content for decision-making.
  • The method offers a practical route to lower inference costs for text-based GUI agents without sacrificing reliability.
  • Structured compression becomes a viable alternative to raw trees when scaling agent systems to complex interfaces.

Where Pith is reading between the lines

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

  • Similar compression steps could be applied to other text-based observation formats used by agents.
  • Hybrid systems that combine the compact text representation with selective visual patches might achieve further efficiency.
  • Benchmarks for GUI agents may need updated evaluation protocols that test robustness under compressed observations.

Load-bearing premise

The compressed representation keeps every piece of information the agent needs to correctly identify UI elements and their spatial relationships without introducing grounding errors.

What would settle it

Running the same agents on OSWorld tasks after deliberately stripping one spatial attribute from the compressed tree and checking whether success rates fall below the uncompressed baseline.

Figures

Figures reproduced from arXiv: 2605.00551 by Hitoshi Iyatomi, Michito Takeshita, Shunsuke Kitada, Takumi Ohashi, Takuro Kawada.

Figure 1
Figure 1. Figure 1: Examples of a linearized a11y tree, illustrating view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the A11y-Compresser framework. Given a linearized a11y tree, the pipeline applies view at source ↗
Figure 3
Figure 3. Figure 3: Average input token counts of observation representations across application domains. Compressed-a11y view at source ↗
Figure 4
Figure 4. Figure 4: Example of modal dialog handling with different observation representations. Screenshot-based observa view at source ↗
Figure 5
Figure 5. Figure 5: Example of modal dialog handling with LineRetriever. In this instance, the model failed to perceive the view at source ↗
read the original abstract

AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding. The accessibility tree is a commonly used text-based format that encodes UI element attributes, but it suffers from redundancy and lacks structural information such as spatial relationships among elements. We propose A11y-Compressor, a framework that transforms linearized accessibility trees into compact and structured representations. Our implementation, Compressed-a11y, applies a lightweight and structured transformation pipeline with modal detection, redundancy reduction, and semantic structuring. Experiments on the OSWorld benchmark show that Compressed-a11y reduces input tokens to 22% of the original while improving task success rates by 5.1 percentage points on average.

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 / 2 minor

Summary. The manuscript proposes A11y-Compressor, a framework that transforms linearized accessibility trees into compact structured representations for GUI agents via a pipeline of modal detection, redundancy reduction, and semantic structuring. On the OSWorld benchmark, the Compressed-a11y implementation reduces input tokens to 22% of the original while improving average task success rates by 5.1 percentage points.

Significance. If the empirical results are robust, the work addresses a practical bottleneck in GUI agent scalability by reducing token consumption without apparent loss of grounding capability. The lightweight, structured pipeline could be adopted broadly to improve efficiency in observation representations for AI agents.

major comments (2)
  1. [OSWorld benchmark experiments] The OSWorld benchmark experiments report a 5.1 pp success-rate lift and 22% token reduction but provide no information on the baseline observation format, agent model, number of runs, statistical tests, or controls for confounding variables. This information is required to substantiate the central claim that the compression improves performance.
  2. [Framework description and pipeline] The claim that the compressed representation retains all grounding-critical information (spatial relationships and semantic details) rests on overall task success rates alone. No ablation studies or targeted evaluations of information loss are described, leaving the weakest assumption untested.
minor comments (2)
  1. Define 'modal detection' and 'semantic structuring' more precisely on first use, as these terms are not standard in the GUI agent literature.
  2. [Abstract] The abstract would be strengthened by briefly naming the concrete operations performed in redundancy reduction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for improving clarity and rigor in the experimental reporting and evaluation of information retention. We address each major comment below and outline the revisions we will incorporate.

read point-by-point responses

  1. Referee: [OSWorld benchmark experiments] The OSWorld benchmark experiments report a 5.1 pp success-rate lift and 22% token reduction but provide no information on the baseline observation format, agent model, number of runs, statistical tests, or controls for confounding variables. This information is required to substantiate the central claim that the compression improves performance.

    Authors: We agree that these experimental details are essential for reproducibility and to fully support the claims. The manuscript describes the baseline as the standard linearized accessibility tree provided by the OSWorld environment (see Section 3.1), with the agent being a multimodal LLM. However, to address this concern directly, we will revise the Experiments section to explicitly detail the baseline observation format, the specific agent model, the number of runs performed, the statistical tests applied (including significance levels), and the controls used to isolate the effect of the observation representation. These additions will be included in the revised manuscript. revision: yes

  2. Referee: [Framework description and pipeline] The claim that the compressed representation retains all grounding-critical information (spatial relationships and semantic details) rests on overall task success rates alone. No ablation studies or targeted evaluations of information loss are described, leaving the weakest assumption untested.

    Authors: We acknowledge that end-to-end task success rates, while indicative of effective grounding, do not directly test the retention of specific information types such as spatial relationships. The pipeline components (modal detection, redundancy reduction, and semantic structuring) are designed to preserve these elements, and the observed performance improvement supports this. To strengthen the evidence, we will add ablation studies in the revised version that isolate each pipeline stage and measure their impact on task success, along with a targeted qualitative comparison of key UI elements between original and compressed representations on a sample of tasks. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an empirical framework for compressing accessibility trees via modal detection, redundancy reduction, and semantic structuring, with performance measured on the external OSWorld benchmark. No equations, derivations, fitted parameters, or mathematical claims are present that could reduce outputs to inputs by construction. Claims rest on benchmark results rather than internal logic or self-citations, rendering the work self-contained with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The framework rests on the domain assumption that accessibility trees contain removable redundancy while preserving task-relevant structure; no free parameters, invented entities, or additional axioms are stated in the abstract.

pith-pipeline@v0.9.0 · 5437 in / 1075 out tokens · 21182 ms · 2026-05-09T19:38:05.269103+00:00 · methodology

discussion (0)

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