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From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery

The paper introduces PersonTTS, an amortized agentic policy-discovery framework for personalized test-time scaling. It aims to maximize the joint satisfaction of user-specific requirements by reusing prior search experience through requirement-matched controller initialization and source-distilled procedural guidance, while evaluating each candidate on the target profile. Experiments show it outperforms existing TTS methods in joint requirement satisfaction on unseen user profiles.

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PublishedOctober 7, 2026Xinglin Wang, Zishen Liu, Tong Zheng
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WHY IT MAY MATTER

This approach allows for more efficient and effective personalization of test-time scaling by reusing prior search experience and adapting to specific user requirements, which can be useful in applications where diverse user needs must be met with limited computational resources.

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