# Agentic Persona Control and Task State Tracking for Realistic User Simulation in Interactive Scenarios arXiv: https://arxiv.org/abs/2601.15290 ยท HTML: https://mlsanity.com/paper/2601.15290/ ## Key facts - Cited by 1 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 48 of its 52 references that are in the map. - First posted on arXiv 30 Nov 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, 30 of 48) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023) - [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019) - [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025) - [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023) - [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024) - [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024) - [Agent-as-a-Judge: Evaluate Agents with Agents](https://mlsanity.com/paper/2410.10934/) (2024) - [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024) - [Evaluation and Benchmarking of LLM Agents: A Survey](https://mlsanity.com/paper/2507.21504/) (2025) - [Multi-Agent Collaboration via Evolving Orchestration](https://mlsanity.com/paper/2505.19591/) (2025) - [Establishing Best Practices for Building Rigorous Agentic Benchmarks](https://mlsanity.com/paper/2507.02825/) (2025) - [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025) - [Humanoid Agents: Platform for Simulating Human-like Generative Agents](https://mlsanity.com/paper/2310.05418/) (2023) - [PERSONA: A Reproducible Testbed for Pluralistic Alignment](https://mlsanity.com/paper/2407.17387/) (2024) - [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025) - [Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2011.00483/) (2020) - [IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems](https://mlsanity.com/paper/2501.11067/) (2025) - [Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation](https://mlsanity.com/paper/2405.13037/) (2024) - [Towards Multimodal Emotional Support Conversation Systems](https://mlsanity.com/paper/2408.03650/) (2024) - [Goal Alignment in LLM-Based User Simulators for Conversational AI](https://mlsanity.com/paper/2507.20152/) (2025) - [User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue](https://mlsanity.com/paper/2309.13233/) (2023) - [OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking](https://mlsanity.com/paper/2311.09758/) (2023) - [Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications](https://mlsanity.com/paper/2412.05449/) (2024) - [DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications](https://mlsanity.com/paper/2409.19020/) (2024) - [Human Simulacra: Benchmarking the Personification of Large Language Models](https://mlsanity.com/paper/2402.18180/) (2024) - [User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation](https://mlsanity.com/paper/2501.04410/) (2025) - [Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey](https://mlsanity.com/paper/2506.11102/) (2025) ## Built on by (most cited first, 1 of 1) - [SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection](https://mlsanity.com/paper/2601.02871/) (2026) ## Abstract (arXiv) Testing conversational AI systems at scale across diverse domains necessitates realistic and diverse user interactions capturing a wide array of behavioral patterns. We present a novel multi-agent framework for realistic, explainable human user simulation in interactive scenarios, using persona control and task state tracking to mirror human cognitive processes during goal-oriented conversations. Our system employs three specialized AI agents: (1) a User Agent to orchestrate the overall interaction, (2) a State Tracking Agent to maintain structured task state, and (3) a Message Attributes Generation Agent that controls conversational attributes based on task progress and assigned persona. To validate our approach, we implement and evaluate the framework for guest ordering at a restaurant with scenarios rich in task complexity, behavioral diversity, and conversational ambiguity. Through systematic ablations, we evaluate the contributory efficacy of each agentic component to overall simulation quality in terms of persona adherence, task completion accuracy, explainability, and realism. Our experiments demonstrate that the complete multi-agent system achieves superior simulation quality compared to single-LLM baselines, with significant gains across all evaluation metrics. This framework establishes a powerful environment for orchestrating agents to simulate human users with cognitive plausibility, decomposing the simulation into specialized sub-agents that reflect distinct aspects of human thought processes applicable across interactive domains.