# PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents

arXiv: https://arxiv.org/abs/2407.09394 · HTML: https://mlsanity.com/paper/2407.09394/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation](https://mlsanity.com/paper/2310.04408/) (2023)
- [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](https://mlsanity.com/paper/2311.09210/) (2023)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)

## Built on by (most cited first, 30 of 30)

- [Deep Research Agents: A Systematic Examination And Roadmap](https://mlsanity.com/paper/2506.18096/) (2025)
- [PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory](https://mlsanity.com/paper/2512.06688/) (2025)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [On the Way to LLM Personalization: Learning to Remember User Conversations](https://mlsanity.com/paper/2411.13405/) (2024)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval](https://mlsanity.com/paper/2510.08935/) (2025)
- [EnronQA: Towards Personalized RAG over Private Documents](https://mlsanity.com/paper/2505.00263/) (2025)
- [Agentic Workflows for Conversational Human-AI Interaction Design](https://mlsanity.com/paper/2501.18002/) (2025)
- [Your Language Model Secretly Contains Personality Subnetworks](https://mlsanity.com/paper/2602.07164/) (2026)
- [Reasoning LLMs for User-Aware Multimodal Conversational Agents](https://mlsanity.com/paper/2504.01700/) (2025)
- [LEANN: A Low-Storage Vector Index](https://mlsanity.com/paper/2506.08276/) (2025)
- [From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents](https://mlsanity.com/paper/2607.08028/) (2026)
- [Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality](https://mlsanity.com/paper/2503.16457/) (2025)
- [PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM](https://mlsanity.com/paper/2511.17467/) (2025)
- [SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration](https://mlsanity.com/paper/2505.02418/) (2025)
- [AgentSim: A Platform for Verifiable Agent-Trace Simulation](https://mlsanity.com/paper/2604.26653/) (2026)
- [NuggetIndex: Governed Atomic Retrieval for Maintainable RAG](https://mlsanity.com/paper/2604.27306/) (2026)
- [From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents](https://mlsanity.com/paper/2605.25693/) (2026)
- [Metadata, Structure, or Strategy? A Decomposition of RAG Context Enrichment](https://mlsanity.com/paper/2606.29645/) (2026)
- [The Compaction Cliff in Long-Running AI Agent Memory](https://mlsanity.com/paper/2608.22752/) (2026)
- [Enhancing Critical Thinking with AI: A Tailored Warning System for RAG Models](https://mlsanity.com/paper/2504.16883/) (2025)
- [ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.18769/) (2026)
- [From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users](https://mlsanity.com/paper/2606.00728/) (2026)
- [As We May Search](https://mlsanity.com/paper/2606.29652/) (2026)
- [hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising](https://mlsanity.com/paper/2608.26604/) (2026)
- [PragAlign: Feedback-Guided Pragmatic Alignment for Controlled Synthetic Dialogue Generation](https://mlsanity.com/paper/2609.02480/) (2026)
- [PACE: Towards Surfacing Hidden Conflicts in User Requests](https://mlsanity.com/paper/2609.03293/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)
- [Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation](https://mlsanity.com/paper/2610.02353/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) models address this by enhancing LLMs with external knowledge, but often fail to personalize the retrieval process. This paper introduces PersonaRAG, a novel framework incorporating user-centric agents to adapt retrieval and generation based on real-time user data and interactions. Evaluated across various question answering datasets, PersonaRAG demonstrates superiority over baseline models, providing tailored answers to user needs. The results suggest promising directions for user-adapted information retrieval systems.