# PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process

arXiv: https://arxiv.org/abs/2507.04607 · HTML: https://mlsanity.com/paper/2507.04607/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning.
- Builds on 32 of its 98 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Placed in

- LLM reasoning / [Reinforcement learning for reasoning](https://mlsanity.com/lr/reinforcement-learning-for-reasoning/)

## Builds on (most cited first, 30 of 32)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models](https://mlsanity.com/paper/2503.16419/) (2025)
- [Scaling Relationship on Learning Mathematical Reasoning with Large Language Models](https://mlsanity.com/paper/2308.01825/) (2023)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [Personalized Language Modeling from Personalized Human Feedback](https://mlsanity.com/paper/2402.05133/) (2024)
- [Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction](https://mlsanity.com/paper/2305.06474/) (2023)
- [LongLaMP: A Benchmark for Personalized Long-form Text Generation](https://mlsanity.com/paper/2407.11016/) (2024)
- [An Empirical Study on Eliciting and Improving R1-like Reasoning Models](https://mlsanity.com/paper/2503.04548/) (2025)
- [User-LLM: Efficient LLM Contextualization with User Embeddings](https://mlsanity.com/paper/2402.13598/) (2024)
- [A Survey of Personalized Large Language Models: Progress and Future Directions](https://mlsanity.com/paper/2502.11528/) (2025)
- [Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation](https://mlsanity.com/paper/2503.22675/) (2025)
- [Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models](https://mlsanity.com/paper/2310.20081/) (2023)
- [Personalized LLM Response Generation with Parameterized Memory Injection](https://mlsanity.com/paper/2404.03565/) (2024)
- [User Modeling and User Profiling: A Comprehensive Survey](https://mlsanity.com/paper/2402.09660/) (2024)
- [Revisiting Self-Distillation](https://mlsanity.com/paper/2206.08491/) (2022)
- [Generative Sequential Recommendation with GPTRec](https://mlsanity.com/paper/2306.11114/) (2023)
- [On the Way to LLM Personalization: Learning to Remember User Conversations](https://mlsanity.com/paper/2411.13405/) (2024)
- [Empathic Conversations: A Multi-level Dataset of Contextualized Conversations](https://mlsanity.com/paper/2205.12698/) (2022)
- [Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap](https://mlsanity.com/paper/2501.01945/) (2025)
- [Slow Thinking for Sequential Recommendation](https://mlsanity.com/paper/2504.09627/) (2025)
- [Personalized Language Models via Privacy-Preserving Evolutionary Model Merging](https://mlsanity.com/paper/2503.18008/) (2025)

## Built on by (most cited first, 18 of 18)

- [PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory](https://mlsanity.com/paper/2512.06688/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [Do LLMs Really Need 10+ Thoughts for "Find the Time 1000 Days Later"? Towards Structural Understanding of LLM Overthinking](https://mlsanity.com/paper/2510.07880/) (2025)
- [Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions](https://mlsanity.com/paper/2603.04191/) (2026)
- [DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings](https://mlsanity.com/paper/2606.22877/) (2026)
- [Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation](https://mlsanity.com/paper/2604.00131/) (2026)
- [Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation](https://mlsanity.com/paper/2605.24647/) (2026)
- [Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction](https://mlsanity.com/paper/2601.05654/) (2026)
- [TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation](https://mlsanity.com/paper/2604.07894/) (2026)
- [From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents](https://mlsanity.com/paper/2605.25693/) (2026)
- [ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking](https://mlsanity.com/paper/2603.20785/) (2026)
- [LiFT: How to Enable In-Context Learning for Longitudinal Modelling](https://mlsanity.com/paper/2604.16382/) (2026)
- [From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generation](https://mlsanity.com/paper/2607.21620/) (2026)
- [Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning](https://mlsanity.com/paper/2609.00014/) (2026)
- [Inferring Hidden User Models from the Behavior of Personalized LLM Agents](https://mlsanity.com/paper/2609.03815/) (2026)
- [ProTTT: Learning to Learn Semantic User Memory with Test-Time Training](https://mlsanity.com/paper/2609.32564/) (2026)
- [PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization](https://mlsanity.com/paper/2610.16372/) (2026)
- [Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback](https://mlsanity.com/paper/2610.19380/) (2026)

## Abstract (arXiv)

Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions. While recent efforts have implemented various personalization methods, a unified theoretical framework that can systematically understand the drivers of effective personalization is still lacking. In this work, we integrate the well-established cognitive dual-memory model into LLM personalization, by mirroring episodic memory to historical user engagements and semantic memory to long-term, evolving user beliefs. Specifically, we systematically investigate memory instantiations and introduce a unified framework, PRIME, using episodic and semantic memory mechanisms. We further augment PRIME with a novel personalized thinking capability inspired by the slow thinking strategy. Moreover, recognizing the absence of suitable benchmarks, we introduce a dataset using Change My View (CMV) from Reddit, specifically designed to evaluate long-context personalization. Extensive experiments validate PRIME's effectiveness across both long- and short-context scenarios. Further analysis confirms that PRIME effectively captures dynamic personalization beyond mere popularity biases.