ID-TAP Framework for Test-Time Agent Personalization
October 16, 2026
ID-TAP utilizes a neural dueling-bandit framework to personalize LLM agents via lightweight binary preference comparisons. The method combines a query-conditioned neural utility model with a Bradley-Terry explorer to scale across large discrete persona spaces.
HOW THIS AFFECTS YOU
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builderYou can implement this to adapt agent behavior to user preferences with minimal interaction.
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researcherThis introduces a scalable method for online persona selection using dueling bandits.