Oversight strategy in computer-use agents shapes exposure to problematic actions more reliably than correction success, with plan-based approaches reducing occurrences but not uniformly improving interventions.
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7 Pith papers cite this work. Polarity classification is still indexing.
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A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
PRISM-XR adds edge-based sensitive-data filtering and quick registration to MLLM-driven XR collaboration, reporting 90% request accuracy, sub-0.3s registration, and over 90% sensitive-object filtering in a 28-person study.
A taxonomy and design space for chart annotations synthesized from qualitative coding of 1,800 static real-world examples.
Developers anticipate code review staying central with more automation and broader scope, while highlighting tensions around understanding, accountability, and trust in AI-mediated processes.
citing papers explorer
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Comparing Human Oversight Strategies for Computer-Use Agents
Oversight strategy in computer-use agents shapes exposure to problematic actions more reliably than correction success, with plan-based approaches reducing occurrences but not uniformly improving interventions.
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Beyond Compliance: How AI Could Help Creative Writers by Refusing Them
A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
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PRISM-XR: Empowering Privacy-Aware XR Collaboration with Multimodal Large Language Models
PRISM-XR adds edge-based sensitive-data filtering and quick registration to MLLM-driven XR collaboration, reporting 90% request accuracy, sub-0.3s registration, and over 90% sensitive-object filtering in a 28-person study.
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A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space
A taxonomy and design space for chart annotations synthesized from qualitative coding of 1,800 static real-world examples.
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Quo Vadis, Code Review? Exploring the Future of Code Review
Developers anticipate code review staying central with more automation and broader scope, while highlighting tensions around understanding, accountability, and trust in AI-mediated processes.
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