PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory

• URL: http://arxiv.org/abs/2512.06688v1
• Date: Sun, 07 Dec 2025 06:48:23 GMT
• Title: PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory
• 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,
• Abstract summary: Personalization is one of the next milestones in advancing AI capability and alignment.<n> PersonaMem-v2 simulates 1,000 realistic user-chatbot interactions on 300+ scenarios, 20,000+ user preferences, and 128k-token context windows.<n>We train Qwen3-4B to outperforms GPT-5, reaching 53% accuracy in implicit personalization.
• Score: 56.81126490418336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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