PersonTTS: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery
An arXiv preprint introduces PersonTTS, an amortized agentic policy-discovery framework that reframes test-time scaling (TTS) as finding controllers that maximize joint satisfaction of a user's accuracy, latency and inference-cost requirements, rather than optimizing one resource dimension at a time. It reuses prior search experience through requirement-matched controller initialization and source-distilled procedural guidance, while still evaluating each candidate against the target profile. On AIME and HMMT, the authors report that PersonTTS substantially outperforms strong TTS baselines on joint requirement satisfaction for unseen user profiles and held-out problems, and that cross-user experience reuse improves policy quality while cutting discovery-agent time and cost under the same evaluation budget.