Bridging Exploration and Persona: Information-Directed Dueling for Test-Time Agent Personalization

The paper introduces ID‑TAP, a neural dueling‑bandit framework that personalizes large‑language‑model agents at test time by selecting personas through binary comparisons of candidate responses. It combines a query‑conditioned neural utility model with a lightweight Bradley‑Terry explorer, and uses similarity‑weighted warm starts to transfer evidence across related queries. Experiments on seven user‑conditioned generation benchmarks show that ID‑TAP outperforms baselines within a ten‑round comparison budget, demonstrating the effectiveness of information‑directed challenger selection for persona adaptation.

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• llms
• agents
• reinforcement-learning
• benchmarks