BBCritic reframes GUI critique as continuous semantic alignment via contrastive learning in an affordance space, outperforming larger binary SOTA models on a new four-level hierarchical benchmark without extra annotations.
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Mai-ui technical report: Real-world centric foundation gui agents
17 Pith papers cite this work. Polarity classification is still indexing.
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2026 17representative citing papers
Presents CUActSpot benchmark and renderer-LLM data synthesis that lets a 4B model outperform larger open-source models on complex computer interactions.
GUI grounding in VLMs is bottlenecked by prefill-stage candidate selection that decoding cannot fix, so Re-Prefill uses attention to extract and re-inject target tokens for up to 4.3% gains on ScreenSpot-Pro.
OS-SPEAR is a new evaluation toolkit that tests 22 OS agents and identifies trade-offs between efficiency and safety or robustness.
FedGUI is the first comprehensive benchmark for federated GUI agents that studies cross-platform, cross-device, cross-OS, and cross-source heterogeneity, with experiments showing performance gains from cross-platform collaboration and identifying platform and OS as the most influential factors.
Android Coach improves online agent training efficiency by enabling multiple actions per state via a critic-based coach, process reward model, and group-wise advantage estimation, delivering 7.5-8.3% success rate gains and 1.4x efficiency over PPO/GRPO baselines.
VF-Coder raises GUI code success rate from 21.68% to 28.29% and visual score from 0.4284 to 0.5584 on a new 984-task benchmark by adding direct visual perception and interaction.
AQuaUI uses adaptive quadtrees to cut visual tokens in GUI-agent LMMs by up to 29.52% at inference time while retaining 99.06% of full-token accuracy on grounding and navigation benchmarks.
MementoGUI introduces a modular memory-control framework with working and episodic memory operators that improves long-horizon GUI agent performance over history-replay and text-only baselines.
Introduces DocOS benchmark to test GUI agents on proactively locating, comprehending, and executing instructions from online documentation in interactive web settings.
Phone-use agents avoid harm more often through inability to act than through deliberate safe choices, so benchmarks must separate unsafe judgment from capability failure.
Personalized soft prompts steer VLM attention to match user-specific gaze patterns, yielding better attention alignment and click prediction in recommendation simulations.
SE-GA combines Test-Time Memory Extension for dynamic context retrieval with Memory-Augmented Self-Evolution training to reach 89.0% on ScreenSpot and 75.8% on AndroidControl-High.
The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.
World models trained on delta text, full text, diffusion images, and renderable code achieve SoTA on two benchmarks and improve downstream GUI agent performance on three mobile datasets with modality-specific strengths.
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
ClawMobile proposes a hierarchical system separating probabilistic LLM planning from structured deterministic execution to improve stability and reproducibility of agentic systems on real smartphones.
citing papers explorer
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What Happens Before Decoding? Prefill Determines GUI Grounding in VLMs
GUI grounding in VLMs is bottlenecked by prefill-stage candidate selection that decoding cannot fix, so Re-Prefill uses attention to extract and re-inject target tokens for up to 4.3% gains on ScreenSpot-Pro.
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MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents
MementoGUI introduces a modular memory-control framework with working and episodic memory operators that improves long-horizon GUI agent performance over history-replay and text-only baselines.