GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 48
Source count: 3
Coverage: 67%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation | Route /signal-canvas/guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation

MCP example

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  "arguments": {
    "mode": "paper",
    "paper_ref": "guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation",
    "query_text": "Summarize GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation"
  }
}

source_context

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  "mode": "paper",
  "query": "GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation",
  "normalized_query": "2603.26266",
  "route": "/signal-canvas/guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation",
  "paper_ref": "guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 7

References: 48

Proof: Verification pending

Freshness state: computing

Source paper: GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

PDF: https://arxiv.org/pdf/2603.26266v1

Source count: 3

Coverage: 67%

Last proof check: 2026-03-31T20:30:20.275Z

Signal Canvas receipt window

Watch and verify: GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

/buildability/guide-resolving-domain-bias-in-gui-agents-through-real-time-web-video-retrieval-and-plug-and-play-annotation

Watchwatch

Subject: GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
In this paper, we present GUIDE (GUI Unbiasing via Instructional-Video Driven Expertise), a training-free, plug-and-play framework that resolves GUI agent domain bias by autonomously acquiring domain-specific expertise from web tutorial videos through a retrieval-augmented automated annotation pipeline.
Implicationpartial
This is a core statement of the paper's contribution, explicitly mentioned in the abstract and introduction.
Verificationpartial
partial
Evidencepartial
First, a subtitle-driven Video-RAG pipeline unlocks video semantics through subtitle analysis, performing progressive three-stage retrieval—domain classification, topic extraction, and relevance matching—to identify task-relevant tutorial videos.
Implicationpartial
The abstract and introduction clearly describe the three stages of the Video-RAG pipeline as a key innovation.
Verificationpartial
partial
Evidencepartial
Second, a fully automated annotation pipeline built on an inverse dynamics paradigm feeds consecutive keyframes enhanced with UI element detection into VLMs, inferring the required planning and grounding knowledge that are injected into the agent's corresponding modules to address both manifestations of domain bias.
Implicationpartial
The abstract and introduction detail the automated annotation pipeline and its reliance on the inverse dynamics paradigm.
Verificationpartial
partial
Evidencepartial
It consistently yields over 5% improvements and reduces execution steps—without modifying any model parameters or architecture—validating GUIDE as an architecture-agnostic enhancement to bridge GUI agent domain bias.
Implicationpartial
The abstract provides specific quantitative results and emphasizes the architecture-agnostic nature of the improvement.
Verificationpartial
partial
Evidencepartial
From 50+ YouTube candidates, a metadata pre-filter removes outliers, then three subtitle-driven stages progressively narrow results
Implicationpartial
The figures and descriptions of the retrieval pipeline indicate an initial large set of candidates that are filtered.
Verificationpartial
partial
Evidencepartial
Final top-K (K≤2) videos proceed to an
Implicationpartial
The figures and descriptions explicitly state that the final output of the retrieval is a small number of top videos.
Verificationpartial
partial
Evidencepartial
Extensive experiments on OSWorld demonstrate GUIDE's generality as a plug-and-play component for both multi-agent systems and single-model agents.
Implicationpartial
The abstract and introduction explicitly state the generality of GUIDE across different agent architectures.
Verificationpartial
partial

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GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

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