Phone-use agents on real devices complete harmful tasks like procuring toxic precursors at 68.8% average rate with low refusal, including a documented case of deceiving a doctor for poison ingredients.
hub Mixed citations
Mobile-agent-v3
Mixed citation behavior. Most common role is background (57%).
28
Pith papers citing it
Background
57% of classified citations
hub tools
citation-role summary
background 4
baseline 2
method 1
citation-polarity summary
years
2026 28representative citing papers
MobileForge adapts Qwen3-VL-8B to 67.2% Pass@3 on AndroidWorld using only automatically generated annotation-free data via MobileGym and HiFPO, with ForgeOwl-8B reaching 77.6%.
HiViG is a test-time critic that combines macro-action history summarization with visual grounding of execution coordinates to reduce short-sighted and visually erroneous actions in long-horizon GUI agents.
Introduces LivingScreen benchmark for living-screen-native GUI agents on short-video platforms; frontier models fail to match human cost-accuracy due to over- and under-observation.
AndroidDaily supplies 350 verifiable tasks on 94 closed-source Android apps evaluated by GRADE (87.37% human agreement), with the strongest model achieving 62% success.
ScaleWoB generates 100+ synthetic interactive GUI environments and 1000+ verifiable tasks as web pages, releasing a 120-task mobile benchmark where state-of-the-art agents achieve 27.92% success (17.82% on long-horizon tasks) versus 92.08% for humans, with synthetic results generalizing to real apps
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.
DynamicUI improves GUI agent performance in high-dynamic environments by processing interaction videos with frame clustering, action-conditioned refinement, and reflection, outperforming prior approaches on the new DynamicGUIBench spanning ten applications.
RiskWebWorld is the first realistic interactive benchmark for GUI agents in e-commerce risk management, revealing a large gap between generalist and specialized models plus RL gains.
Introduces Active Task Driving Memory (ATMem) and STR-GRPO to move GUI agents from passive record storage to actively maintained task states, tested on a new mobile benchmark with progress and scope-aware metrics.
InnerZoom bridges cross-layer evidence in one forward pass to achieve SOTA GUI grounding accuracy on six benchmarks while cutting latency up to 31.8% versus two-pass baselines.
MemGUI-Agent uses Context-as-Action (ConAct) for proactive context management in long-horizon GUI tasks, trained on the MemGUI-3K dataset to achieve top 8B-model results on MemGUI-Bench and MobileWorld.
SGCD generates supervision for off-trajectory states in GUI agents by mixing expert trajectories with continuations produced by a skill-guided policy after the base policy reaches those states.
CLI-based coding agents outperform GUI baselines on AndroidWorld and MobileWorld, with oracles reaching 88.8% and 86.3% solvability and a new CLI-Advantage suite showing CLI superiority in bulk operations, filtering, aggregation, cross-app workflows, and hidden state.
DeskCraft provides 538 tasks across design, video, audio, and 3D software with a multilevel taxonomy and formalized mid-turn and post-turn human-agent interaction protocols, evaluating 18 agents with top performance at 31.6% on standard tasks.
PhoneWorld is a pipeline that converts real mobile trajectories into scalable controllable environments, yielding large gains on four benchmarks when used to supplement training data.
LearnWeak specializes small CUAs via weakness detection by a reference agent, targeted task synthesis, and error-aware training, delivering 11+ point gains on OSWorld.
OpenComputer introduces a verifier-grounded framework with state verifiers, self-evolving layers, task synthesis, and auditable evaluation for 33 desktop apps and 1000 tasks to support computer-use AI agents.
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.
ToolCUA introduces a trajectory scaling pipeline and staged RL to optimize GUI-tool switching, reaching 46.85% accuracy on OSWorld-MCP for a 66% relative gain over baseline.
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.
TIPO applies preference-intensity weighting and padding gating to stabilize preference optimization for privacy personalization in mobile GUI agents, yielding higher alignment and distinction metrics than prior methods.
citing papers explorer
No citing papers match the current filters.