{"ID":2831946,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06688","arxiv_id":"2512.06688","title":"PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory","abstract":"Personalization is one of the next milestones in advancing AI capability and alignment. We introduce PersonaMem-v2, the state-of-the-art dataset for LLM personalization that simulates 1,000 realistic user-chatbot interactions on 300+ scenarios, 20,000+ user preferences, and 128k-token context windows, where most user preferences are implicitly revealed to reflect real-world interactions. Using this data, we investigate how reinforcement fine-tuning enables a model to improve its long-context reasoning capabilities for user understanding and personalization. We also develop a framework for training an agentic memory system, which maintains a single, human-readable memory that grows with each user over time. In our experiments, frontier LLMs still struggle with implicit personalization, achieving only 37-48% accuracy. While they support long context windows, reasoning remains the bottleneck for implicit personalization tasks. Using reinforcement fine-tuning, we successfully train Qwen3-4B to outperforms GPT-5, reaching 53% accuracy in implicit personalization. Moreover, our agentic memory framework achieves state-of-the-art 55% accuracy while using 16x fewer input tokens, relying on a 2k-token memory instead of full 32k conversation histories. These results underscore the impact of our dataset and demonstrate agentic memory as a scalable path toward real-world personalized intelligence.","short_abstract":"Personalization is one of the next milestones in advancing AI capability and alignment. We introduce PersonaMem-v2, the state-of-the-art dataset for LLM personalization that simulates 1,000 realistic user-chatbot interactions on 300+ scenarios, 20,000+ user preferences, and 128k-token context windows, where most user p...","url_abs":"https://arxiv.org/abs/2512.06688","url_pdf":"https://arxiv.org/pdf/2512.06688v1","authors":"[\"Bowen Jiang\",\"Yuan Yuan\",\"Maohao Shen\",\"Zhuoqun Hao\",\"Zhangchen Xu\",\"Zichen Chen\",\"Ziyi Liu\",\"Anvesh Rao Vijjini\",\"Jiashu He\",\"Hanchao Yu\",\"Radha Poovendran\",\"Gregory Wornell\",\"Lyle Ungar\",\"Dan Roth\",\"Sihao Chen\",\"Camillo Jose Taylor\"]","published":"2025-12-07T06:48:23Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}