# Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles

arXiv: https://arxiv.org/abs/2510.07925 · HTML: https://mlsanity.com/paper/2510.07925/

## Key facts

- Cited by 17 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 10 of its 43 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 10 of 10)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [On Memory Construction and Retrieval for Personalized Conversational Agents](https://mlsanity.com/paper/2502.05589/) (2025)
- [Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.13193/) (2024)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [Personalized Large Language Model Assistant with Evolving Conditional Memory](https://mlsanity.com/paper/2312.17257/) (2023)
- [Personality-Driven Decision-Making in LLM-Based Autonomous Agents](https://mlsanity.com/paper/2504.00727/) (2025)
- [A computational framework for human values](https://mlsanity.com/paper/2305.02748/) (2023)
- [Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces](https://mlsanity.com/paper/2502.16376/) (2025)

## Built on by (most cited first, 17 of 17)

- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms](https://mlsanity.com/paper/2605.06716/) (2026)
- [AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents](https://mlsanity.com/paper/2512.23343/) (2025)
- [MemSyco-Bench: Benchmarking Sycophancy in Agent Memory](https://mlsanity.com/paper/2607.01071/) (2026)
- [Continuum Memory Architectures for Long-Horizon LLM Agents](https://mlsanity.com/paper/2601.09913/) (2026)
- [Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation](https://mlsanity.com/paper/2608.28241/) (2026)
- [MILD: Mediator Agent System with Bidirectional Perception and Multi-Layered Alignment for Human-Vehicle Collaboration](https://mlsanity.com/paper/2605.01507/) (2026)
- [EXG: Self-Evolving Agents with Experience Graphs](https://mlsanity.com/paper/2605.17721/) (2026)
- [Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalization](https://mlsanity.com/paper/2605.27389/) (2026)
- [From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents](https://mlsanity.com/paper/2608.02171/) (2026)
- [Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories](https://mlsanity.com/paper/2608.10319/) (2026)
- [Alignment has a Fantasia Problem](https://mlsanity.com/paper/2604.21827/) (2026)
- [DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models](https://mlsanity.com/paper/2607.02374/) (2026)
- [AI YOU Town: Make Friends and Money with Your Digital Twin](https://mlsanity.com/paper/2607.10539/) (2026)
- [To Memories and Beyond: From Remembering to Knowing You across Long-Term Multimodal Personal Archives](https://mlsanity.com/paper/2609.19167/) (2026)
- [Pincer: Resource Authorization for Agents using a Digital Twin](https://mlsanity.com/paper/2610.02569/) (2026)

## Abstract (arXiv)

Large language models (LLMs) increasingly serve as the central control unit of AI agents, yet current approaches remain limited in their ability to deliver personalized interactions. While Retrieval Augmented Generation enhances LLM capabilities by improving context-awareness, it lacks mechanisms to combine contextual information with user-specific data. Although personalization has been studied in fields such as human-computer interaction or cognitive science, existing perspectives largely remain conceptual, with limited focus on technical implementation. To address these gaps, we build on a unified definition of personalization as a conceptual foundation to derive technical requirements for adaptive, user-centered LLM-based agents. Combined with established agentic AI patterns such as multi-agent collaboration or multi-source retrieval, we present a framework that integrates persistent memory, dynamic coordination, self-validation, and evolving user profiles to enable personalized long-term interactions. We evaluate our approach on three public datasets using metrics such as retrieval accuracy, response correctness, or BertScore. We complement these results with a five-day pilot user study providing initial insights into user feedback on perceived personalization. The study provides early indications that guide future work and highlights the potential of integrating persistent memory and user profiles to improve the adaptivity and perceived personalization of LLM-based agents.