PersonaSI Research
Research briefs on memory, learning, evaluation, and trust. Each article separates published evidence from PersonaSI implications.
AI Learning
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.
Read article →Memory
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 →Memory
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
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 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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