# MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

arXiv: https://arxiv.org/abs/2510.05124 · HTML: https://mlsanity.com/paper/2510.05124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 21 of 21)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs](https://mlsanity.com/paper/2410.12405/) (2024)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments](https://mlsanity.com/paper/2404.09329/) (2024)
- [IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems](https://mlsanity.com/paper/2501.11067/) (2025)
- [Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration](https://mlsanity.com/paper/2012.15375/) (2020)
- [COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal](https://mlsanity.com/paper/2312.11792/) (2023)
- [PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues](https://mlsanity.com/paper/2502.21017/) (2025)
- [Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues](https://mlsanity.com/paper/2407.09897/) (2024)
- [Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations](https://mlsanity.com/paper/2312.13871/) (2023)
- [Self-Refinement of Language Models from External Proxy Metrics Feedback](https://mlsanity.com/paper/2403.00827/) (2024)
- [Muse: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles](https://mlsanity.com/paper/2412.18416/) (2024)
- [Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms](https://mlsanity.com/paper/2502.19193/) (2025)
- [Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs](https://mlsanity.com/paper/2503.08842/) (2025)

## Built on by (most cited first, 2 of 2)

- [SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection](https://mlsanity.com/paper/2601.02871/) (2026)
- [MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion](https://mlsanity.com/paper/2605.18572/) (2026)

## Abstract (arXiv)

We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents designed to simulate diverse persona-driven behaviors by leveraging personality signifiers such as Zodiac Signs and MBTI types, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users' Chain-of-Attitude (CoA) modeling and dedicated LLMs' persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) , demonstrating clear business value.