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

TL;DR

PersonaMem-v2 introduces a comprehensive dataset and methods to enhance AI personalization by learning implicit user preferences and maintaining scalable agentic memory, significantly improving accuracy over existing models.

Contribution

The paper presents a new dataset, PersonaMem-v2, and a novel agentic memory framework that together advance personalized AI by enabling models to better understand and remember user preferences over time.

Findings

Reinforcement fine-tuning improves implicit personalization accuracy from 37-48% to 53%.

Agentic memory achieves 55% accuracy with 16x fewer tokens.

Models still struggle with implicit personalization despite long context support.

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.