# Dynamic Context Adaptation for Consistent Role-Playing Agents with Retrieval-Augmented Generations

arXiv: https://arxiv.org/abs/2508.02016 · HTML: https://mlsanity.com/paper/2508.02016/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 28 of 28)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://mlsanity.com/paper/2310.17976/) (2023)
- [Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach](https://mlsanity.com/paper/2407.16833/) (2024)
- [Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment](https://mlsanity.com/paper/2401.12474/) (2024)
- [CRAG -- Comprehensive RAG Benchmark](https://mlsanity.com/paper/2406.04744/) (2024)
- [RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework](https://mlsanity.com/paper/2408.01262/) (2024)
- [CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds](https://mlsanity.com/paper/2412.05631/) (2024)
- [Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning](https://mlsanity.com/paper/2503.17662/) (2025)
- [HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.12442/) (2025)
- [Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.00456/) (2024)
- [TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models](https://mlsanity.com/paper/2405.18027/) (2024)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis](https://mlsanity.com/paper/2505.21700/) (2025)
- [MBTI Personality Prediction for Fictional Characters Using Movie Scripts](https://mlsanity.com/paper/2210.10994/) (2022)
- [CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents](https://mlsanity.com/paper/2405.19778/) (2024)
- [RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval](https://mlsanity.com/paper/2505.18541/) (2025)
- [Role-Playing Evaluation for Large Language Models](https://mlsanity.com/paper/2505.13157/) (2025)

## Built on by (most cited first, 4 of 4)

- [When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction](https://mlsanity.com/paper/2605.12922/) (2026)
- [GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing](https://mlsanity.com/paper/2606.00860/) (2026)
- [From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents](https://mlsanity.com/paper/2605.25693/) (2026)
- [Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novels](https://mlsanity.com/paper/2512.07474/) (2025)

## Abstract (arXiv)

Building role-playing agents (RPAs) that faithfully emulate specific characters remains challenging because collecting character-specific utterances and continually updating model parameters are resource-intensive, making retrieval-augmented generation (RAG) a practical necessity. However, despite the importance of RAG, there has been little research on RAG-based RPAs. For example, we empirically find that when a persona lacks knowledge relevant to a given query, RAG-based RPAs are prone to hallucination, making it challenging to generate accurate responses. In this paper, we propose Amadeus, a training-free framework that can significantly enhance persona consistency even when responding to questions that lie beyond a character's knowledge. In addition, to underpin the development and rigorous evaluation of RAG-based RPAs, we manually construct CharacterRAG, a role-playing dataset that consists of persona documents for 15 distinct fictional characters totaling 976K written characters, and 450 question-answer pairs. We find that our proposed method effectively models not only the knowledge possessed by characters, but also various attributes such as personality.