mlsanityData to 14 Oct 2026

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

arXiv 2507.04607 ↗First posted 7 Jul 2025cs.CL cs.AIcited by 18

On the map: LLM reasoning → Reinforcement learning for reasoning

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.

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.

Builds on

32 of 98 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan

Other references · 31

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2024Qwen2.5 Technical Reportcited by 4,942
  4. 2025s1: Simple test-time scalingcited by 1,341
  5. 2024Phi-4 Technical Reportcited by 711
  6. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  7. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Modelscited by 464
  8. 2023Scaling Relationship on Learning Mathematical Reasoning with Large Language Modelscited by 427
  9. 2024The Faiss librarycited by 376
  10. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  11. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 144
  12. 2023User Behavior Simulation with Large Language Model based Agentscited by 140
19 more
  1. 2024Personalized Language Modeling from Personalized Human Feedbackcited by 132
  2. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 116
  3. 2024LongLaMP: A Benchmark for Personalized Long-form Text Generationcited by 97
  4. 2025An Empirical Study on Eliciting and Improving R1-like Reasoning Modelscited by 78
  5. 2024User-LLM: Efficient LLM Contextualization with User Embeddingscited by 76
  6. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 71
  7. 2025Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendationcited by 51
  8. 2023Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Modelscited by 45
  9. 2024Personalized LLM Response Generation with Parameterized Memory Injectioncited by 33
  10. 2024User Modeling and User Profiling: A Comprehensive Surveycited by 29
  11. 2022Revisiting Self-Distillationcited by 24
  12. 2023Generative Sequential Recommendation with GPTReccited by 21
  13. 2024On the Way to LLM Personalization: Learning to Remember User Conversationscited by 21
  14. 2022Empathic Conversations: A Multi-level Dataset of Contextualized Conversationscited by 20
  15. 2025Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmapcited by 20
  16. 2025Slow Thinking for Sequential Recommendationcited by 11
  17. 2025Personalized Language Models via Privacy-Preserving Evolutionary Model Mergingcited by 8
  18. 2024Overview of AI-Debater 2023: The Challenges of Argument Generation Taskscited by 2
  19. 2024Change My Frame: Reframing in the Wild in r/ChangeMyViewcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 16.

18 papers.

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  1. 2025PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memorycited by 88
  2. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directionscited by 14
  3. 2025Do LLMs Really Need 10+ Thoughts for "Find the Time 1000 Days Later"? Towards Structural Understanding of LLM Overthinkingcited by 10
  4. 2026Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactionscited by 10
  5. 2026DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settingscited by 4
  6. 2026Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activationcited by 3
  7. 2026Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversationcited by 3
  8. 2026Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Predictioncited by 1
  9. 2026TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillationcited by 1
  10. 2026From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agentscited by 1
  11. 2026ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Rankingcited by 0
  12. 2026LiFT: How to Enable In-Context Learning for Longitudinal Modellingcited by 0
6 more
  1. 2026From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generationcited by 0
  2. 2026Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoningcited by 0
  3. 2026Inferring Hidden User Models from the Behavior of Personalized LLM Agentscited by 0
  4. 2026ProTTT: Learning to Learn Semantic User Memory with Test-Time Trainingcited by 0
  5. 2026PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalizationcited by 0
  6. 2026Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedbackcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RPM: Reasoning-Level Personalization for Black-Box Large Language Models
  2. 2026PersonaVLM: Long-Term Personalized Multimodal LLMs
  3. 2026Rethinking Personalization in Large Language Models at the Token Level
  4. 2026Latent Personal Memory: Represent personal memory as dynamic soft prompts
  5. 2026PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments
  6. 2026Re-Centering Humans in LLM Personalization
  7. 2026Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
  8. 2025PrefDisco: Benchmarking Proactive Personalized Reasoning