mlsanityData to 14 Oct 2026

AutoPal: Autonomous Adaptation to Users for Personal AI Companionship

arXiv 2406.13960 ↗First posted 20 Jun 2024cs.CL cs.AIcited by 6

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Abstract (arXiv)

Previous research has demonstrated the potential of AI agents to act as companions that can provide constant emotional support for humans. In this paper, we emphasize the necessity of autonomous adaptation in personal AI companionship, an underexplored yet promising direction. Such adaptability is crucial as it can facilitate more tailored interactions with users and allow the agent to evolve in response to users' changing needs. However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process. To address them, we devise a hierarchical framework, AutoPal, that enables controllable and authentic adjustments to the agent's persona based on user interactions. A personamatching dataset is constructed to facilitate the learning of optimal persona adaptations. Extensive experiments demonstrate the effectiveness of AutoPal and highlight the importance of autonomous adaptability in AI companionship.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 32 of its 67 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

32 of 67 references.

References · 32

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20 more
  1. 2018Training Millions of Personalized Dialogue Agentscited by 126
  2. 2022Long Time No See! Open-Domain Conversation with Long-Term Persona Memorycited by 108
  3. 2022MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversationcited by 85
  4. 2019Personalizing Dialogue Agents via Meta-Learningcited by 83
  5. 2019Learning to Select Knowledge for Response Generation in Dialog Systemscited by 80
  6. 2022Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planningcited by 60
  7. 2023CharacterChat: Learning towards Conversational AI with Personalized Social Supportcited by 60
  8. 2022Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversationcited by 56
  9. 2022Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generationcited by 47
  10. 2021Transformer-based Conditional Variational Autoencoder for Controllable Story Generationcited by 44
  11. 2023Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversationscited by 41
  12. 2021One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profilescited by 39
  13. 2020XPersona: Evaluating Multilingual Personalized Chatbotcited by 36
  14. 2023TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transitioncited by 30
  15. 2019A Dynamic Strategy Coach for Effective Negotiationcited by 26
  16. 2021DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialoguescited by 25
  17. 2023Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curationcited by 23
  18. 2023COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goalcited by 18
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  20. 2022PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variablecited by 5

Built on by

Cited by year (2026 to 14 Oct)

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

6 papers.

  1. 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 170
  2. 2025LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAscited by 4
  3. 2025Agentic Persona Control and Task State Tracking for Realistic User Simulation in Interactive Scenarioscited by 1
  4. 2026Embedded AI Companion System on Edge Devicescited by 0
  5. 2026Beyond Individual Personas: Aligning Synthetic Dialogue to Population-Level Behavior Distributionscited by 0
  6. 2026MindTailor: Personalized Emotional Support via Post History-Grounded Case Formulation and Collaborative Refinementcited by 0