VP-VLA decouples high-level reasoning from low-level control in VLA models by rendering spatial anchors as visual prompts directly in the RGB observation space, outperforming end-to-end baselines.
macmillan (2011)
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VIGIL is the first browser extension for real-time detection and mitigation of cognitive bias triggers, with scroll-synced highlighting, LLM reformulation, privacy tiers, and extensible validated plugins.
Fuzzy AHP and DualJudge deliver more stable and calibrated LLM evaluations than direct scoring by breaking assessments into explicit criteria and adaptively fusing intuitive and deliberative judgments.
Higher trust in AI was associated with weaker discrimination between correct and incorrect recommendations in educational tasks, moderated by AI literacy and need for cognition.
Proposes a behavioral model of positive friction to characterize beneficial obstacles in AI user experiences and developer processes, diagnose needs, and suggest design solutions.
citing papers explorer
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VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models
VP-VLA decouples high-level reasoning from low-level control in VLA models by rendering spatial anchors as visual prompts directly in the RGB observation space, outperforming end-to-end baselines.
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VIGIL: An Extensible System for Real-Time Detection and Mitigation of Cognitive Bias Triggers
VIGIL is the first browser extension for real-time detection and mitigation of cognitive bias triggers, with scroll-synced highlighting, LLM reformulation, privacy tiers, and extensible validated plugins.
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Structured Multi-Criteria Evaluation of Large Language Models with Fuzzy Analytic Hierarchy Process and DualJudge
Fuzzy AHP and DualJudge deliver more stable and calibrated LLM evaluations than direct scoring by breaking assessments into explicit criteria and adaptively fusing intuitive and deliberative judgments.
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Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators
Higher trust in AI was associated with weaker discrimination between correct and incorrect recommendations in educational tasks, moderated by AI literacy and need for cognition.
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Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction
Proposes a behavioral model of positive friction to characterize beneficial obstacles in AI user experiences and developer processes, diagnose needs, and suggest design solutions.