The paper introduces PersonTTS, an amortized agentic policy-discovery framework for personalized test-time scaling (TTS) of large language models. PersonTTS aims to maximize the joint satisfaction of user-specified accuracy, latency, and inference-cost requirements by reusing prior search experience through requirement-matched controller initialization and source-distilled procedural guidance. Experiments on AIME and HMMT datasets demonstrate that PersonTTS outperforms strong TTS baselines in joint requirement satisfaction on unseen user profiles and reduces discovery-agent time and cost.
arXiv:2609.36488v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...
arXiv:2606. 10394v1 Announce Type: new Abstract: Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge.
arXiv:2511. 17006v2 Announce Type: replace Abstract: Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction.
Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existing benchmarks still rely on sandboxed artifacts, static task design, and coarse scoring, which hinder scalability and limit progress toward reliable personal-agent evaluation.
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
The paper introduces ENOUGH, a method for creating minimal sufficient user profiles for personalized language models. ENOUGH iteratively adds behavioral records or stops, evaluating profile prefixes with a counterfactual search that balances downstream gains, user specificity, and token costs. The resulting profiles are distilled into a lightweight controller that orders records and triggers the generator only once, achieving better effectiveness and efficiency than existing baselines across six tasks.
arXiv:2512. 11213v2 Announce Type: replace Abstract: Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training.
arXiv:2607. 19739v1 Announce Type: cross Abstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks.
arXiv:2608. 10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization.
arXiv:2607. 21635v1 Announce Type: new Abstract: Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user.
arXiv:2609.01603v1 Announce Type: cross Abstract: Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and...
arXiv:2607. 00011v2 Announce Type: replace-cross Abstract: Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents.