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

Understand

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.