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Research Paper#GUI Agents, MLLMs, AI🔬 ResearchAnalyzed: Jan 3, 2026 20:17

iSHIFT: Lightweight GUI Agent with Adaptive Perception

Published:Dec 26, 2025 12:09
1 min read
ArXiv

Analysis

This paper introduces iSHIFT, a novel lightweight GUI agent designed for efficient and precise interaction with graphical user interfaces. The core contribution lies in its slow-fast hybrid inference approach, allowing the agent to switch between detailed visual grounding for accuracy and global cues for efficiency. The use of perception tokens to guide attention and the agent's ability to adapt reasoning depth are also significant. The paper's claim of achieving state-of-the-art performance with a compact 2.5B model is particularly noteworthy, suggesting potential for resource-efficient GUI agents.
Reference

“iSHIFT matches state-of-the-art performance on multiple benchmark datasets.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:06

ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

Published:Dec 11, 2025 18:59
1 min read
ArXiv

Analysis

This article introduces ImplicitRDP, a novel approach using diffusion models for visual-force control. The 'slow-fast learning' aspect suggests an attempt to improve efficiency and performance by separating different learning rates or processing speeds for different aspects of the task. The end-to-end nature implies a focus on a complete system, likely aiming for direct input-to-output control without intermediate steps. The use of 'structural' suggests an emphasis on the underlying architecture and how it's designed to handle the visual and force data.

Key Takeaways

    Reference

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    PermalinkArXiv