arXiv cs.CL· Xinglin Wang, Zishen Liu, Tong Zheng, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Kan Li·· 10 天前AI 评分27
PersonTTS：通过摊销式智能体策略发现实现个性化测试时扩展
From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery
AI 导读
PersonTTS 是一个摊销式智能体策略发现框架，将个性化测试时扩展（TTS）形式化为发现能最大化用户多维需求联合满足率的可执行控制器，通过需求匹配的控制器初始化和源蒸馏过程指导复用先前搜索经验。
正文 · 原文
Authors:Xinglin Wang, Zishen Liu, Tong Zheng, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Kan Li
Abstract:Test-time scaling (TTS) improves the reasoning capabilities of large language models by allocating additional inference computation. Existing approaches to improving TTS efficiency largely optimize accuracy against one resource dimension at a time, advancing either the accuracy--cost or accuracy--latency Pareto frontier. Yet user requirements are multidimensional: users may specify accuracy, latency, and inference-cost requirements jointly, and different requirements can favor different controllers. We formulate Personalized Test-Time Scaling as discovering executable controllers that maximize the joint satisfaction rate of user-specific requirements. To reduce the overhead of repeated policy discovery for new user profiles, we propose PersonTTS, an amortized agentic policy-discovery framework that reuses prior search experience through requirement-matched controller initialization and source-distilled procedural guidance, while retaining target-profile evaluation for every candidate. Experiments on AIME and HMMT show that PersonTTS substantially outperforms strong TTS baselines in joint requirement satisfaction on unseen user profiles and held-out problems. Under the same candidate-evaluation budget, cross-user experience reuse further improves policy quality while substantially reducing discovery-agent time and cost.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.09684 [cs.CL] |
| (or arXiv:2610.09684v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09684 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xinglin Wang [view email]
[v1]
Wed, 7 Oct 2026 08:46:56 UTC (540 KB)
来源：arXiv cs.CL · arxiv.org