mlsanityData to 14 Oct 2026

MAP: Multi-user Personalization with Collaborative LLM-powered Agents

arXiv 2503.12757 ↗First posted 17 Mar 2025cs.HC cs.AI cs.ROcited by 7

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback. We then present MAP -- a Multi-Agent system for multi-user Personalization -- to operationalize this workflow. By delegating subtasks to specialized agents, MAP (1) retrieves and reflects on relevant user information, while enhancing reliability through agent-to-agent interactions, (2) provides detailed analysis for improved transparency and usability, and (3) integrates user feedback to iteratively refine results. Our user study findings (n=12) highlight MAP's effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management. This work highlights the potential of multi-agent systems to implement user-centered, multi-user personalization workflows and concludes by offering insights for personalization in multi-user contexts.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 23 of its 52 references that are in the map. First posted on arXiv 17 Mar 2025.

Builds on

23 of 52 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  6. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  8. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  9. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  10. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  11. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  12. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 639
11 more
  1. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  2. 2023Language to Rewards for Robotic Skill Synthesiscited by 312
  3. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 257
  4. 2023Decomposing Uncertainty for Large Language Models through Input Clarification Ensemblingcited by 114
  5. 2024Uncertainty Quantification for In-Context Learning of Large Language Modelscited by 45
  6. 2024Towards Uncertainty-Aware Language Agentcited by 32
  7. 2023Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Servicescited by 29
  8. 2024Learning Retrieval Augmentation for Personalized Dialogue Generationcited by 18
  9. 2024MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilancecited by 11
  10. 2024REX: Designing User-centered Repair and Explanations to Address Robot Failurescited by 5
  11. 2024Making Informed Decisions: Supporting Cobot Integration Considering Business and Worker Preferencescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025Collaborative Document Editing with Multiple Users and AI Agentscited by 5
  2. 2025UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spacescited by 4
  3. 2025StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Frameworkcited by 4
  4. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 4
  5. 2026AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Researchcited by 1
  6. 2026U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planningcited by 0
  7. 2026Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Workcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles
  2. 2026LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
  3. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
  4. 2025PersonaAgent: Bridging Memory and Action for Personalized LLM Agents
  5. 2025A Survey of Personalization: From RAG to Agent
  6. 2026Multi-User Large Language Model Agents
  7. 2026APeB: Benchmarking Personalization Ability of Large Language Model Agents
  8. 2026From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents