OCR-Memory encodes agent trajectories as images with visual anchors and retrieves verbatim text via locate-and-transcribe, yielding gains on long-horizon benchmarks under strict context limits.
arXiv preprint arXiv:2312.13771 , year=
9 Pith papers cite this work. Polarity classification is still indexing.
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Desktop GUI agents face TOCTOU attacks from UI state changes during the ~6.5s observation-to-action gap, with a three-layer pre-execution verification defense achieving 100% interception on two attack types but failing on DOM injection.
AutoGUI-v2 is a new benchmark exposing that VLMs handle basic GUI grounding but struggle with complex interaction logic and state prediction.
The paper systematizes agentic skills beyond tool use, providing design pattern and representation-scope taxonomies plus security analysis of malicious skill infiltration in agent marketplaces.
OS-Atlas, trained on the largest open-source cross-platform GUI grounding corpus of 13 million elements, outperforms prior open-source models on six benchmarks across mobile, desktop, and web platforms.
Degradation-Driven Prompting improves VQA by intentionally reducing image detail and using masks, lines, and examples to guide models toward essential structures.
The paper surveys agent skills for LLM agents, organizing the literature into a four-stage lifecycle of representation, acquisition, retrieval, and evolution while highlighting their role in system scalability.
X-OmniClaw presents a unified architecture for Android mobile agents using Omni Perception, Memory, and Action modules to enable efficient multimodal task handling and personalized interactions.
citing papers explorer
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OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory
OCR-Memory encodes agent trajectories as images with visual anchors and retrieves verbatim text via locate-and-transcribe, yielding gains on long-horizon benchmarks under strict context limits.
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Temporal UI State Inconsistency in Desktop GUI Agents: Formalizing and Defending Against TOCTOU Attacks on Computer-Use Agents
Desktop GUI agents face TOCTOU attacks from UI state changes during the ~6.5s observation-to-action gap, with a three-layer pre-execution verification defense achieving 100% interception on two attack types but failing on DOM injection.
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AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark
AutoGUI-v2 is a new benchmark exposing that VLMs handle basic GUI grounding but struggle with complex interaction logic and state prediction.
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SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
The paper systematizes agentic skills beyond tool use, providing design pattern and representation-scope taxonomies plus security analysis of malicious skill infiltration in agent marketplaces.
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OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
OS-Atlas, trained on the largest open-source cross-platform GUI grounding corpus of 13 million elements, outperforms prior open-source models on six benchmarks across mobile, desktop, and web platforms.
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Less Detail, Better Answers: Degradation-Driven Prompting for VQA
Degradation-Driven Prompting improves VQA by intentionally reducing image detail and using masks, lines, and examples to guide models toward essential structures.
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A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
The paper surveys agent skills for LLM agents, organizing the literature into a four-stage lifecycle of representation, acquisition, retrieval, and evolution while highlighting their role in system scalability.
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X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction
X-OmniClaw presents a unified architecture for Android mobile agents using Omni Perception, Memory, and Action modules to enable efficient multimodal task handling and personalized interactions.
- MMSkills: Towards Multimodal Skills for General Visual Agents