AI UX
The Canvas Becomes a Control Surface for AI
Figma’s agent, Make, and Weave show how selection, visual controls, and design-system context can help people direct AI while keeping decisions inspectable.
Read article →PersonaSI Research
Evidence for personal agents that improve responsibly.
Research briefs on memory, learning, interfaces, evaluation, and trust. Each article keeps claims, limitations, and sources visible.
Browse the research signals database →Featured research
Emerging Interfaces
Agent Interfaces Should Be Built for Correction
New AI interfaces make room for judgment through task-specific controls, contextual feedback, well-timed checkpoints, and inspectable demonstrations.
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Learning Interfaces
Teaching Robots Through Feedback
Three recent studies offer practical lessons for the interfaces people use to train specialist agents.
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AI Learning
What Should a Personal Agent Learn From One Correction?
Personal agents need a disciplined way to decide what a correction should change. Recent research offers approaches to selective training, editable memory, and evaluating whether feedback produced a meaningful improvement.
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Memory
Personal Agent Memory Needs a Transfer Test
Personal agents should prove that experience improves later work while avoiding new mistakes. Recent benchmarks point toward evaluating transfer, retention, appropriate use of personal context, and the effort required from users.
Read article →Supporting articles
Memory
An agent’s memory should include the right moment to act
PM-Bench shifts attention from recalling yesterday’s conversation to honoring tomorrow’s intention. The engineering challenge is deciding what remains valid, when it becomes due, and when to leave it alone.
Read article →Evaluation
Your agent improved. Did your evaluation notice?
An October working paper shows how changes in human reviewers can obscure an agent’s progress. Reliable evaluation needs a stable measuring process and evidence that stays current.
Read article →Trust
Trust Should Follow Evidence
Trust in specialist agents should be tied to evidence, scope and consequences. A review of the research separates observed trust judgments from proposed product architecture.
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