The Indispensable Role of User Simulation in the Pursuit of AGI

• URL: http://arxiv.org/abs/2509.19456v1
• Date: Tue, 23 Sep 2025 18:12:45 GMT
• Title: The Indispensable Role of User Simulation in the Pursuit of AGI
• Authors: Krisztian Balog, ChengXiang Zhai,
• Abstract summary: We argue that realistic simulators provide the necessary environments for scalable evaluation, data generation for interactive learning, and fostering the adaptive capabilities central to Artificial General Intelligence (AGI)<n>This article elaborates on the critical role of user simulation for AGI, explores the interdisciplinary nature of building realistic simulators, identifies key challenges including those posed by large language models, and proposes a future research agenda.
• Score: 37.789218939871105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Progress toward Artificial General Intelligence (AGI) faces significant bottlenecks, particularly in rigorously evaluating complex interactive systems and acquiring the vast interaction data needed for training adaptive agents. This paper posits that user simulation -- creating computational agents that mimic human interaction with AI systems -- is not merely a useful tool, but is a critical catalyst required to overcome these bottlenecks and accelerate AGI development. We argue that realistic simulators provide the necessary environments for scalable evaluation, data generation for interactive learning, and fostering the adaptive capabilities central to AGI. Therefore, research into user simulation technology and intelligent task agents are deeply synergistic and must advance hand-in-hand. This article elaborates on the critical role of user simulation for AGI, explores the interdisciplinary nature of building realistic simulators, identifies key challenges including those posed by large language models, and proposes a future research agenda.

Related papers

• TongSIM: A General Platform for Simulating Intelligent Machines [59.27575233453533]
  Embodied intelligence focuses on training agents within realistic simulated environments.<n>TongSIM is a high-fidelity, general-purpose platform for training and evaluating embodied agents.
  arXiv  Detail & Related papers  (2025-12-23T10:00:43Z)
• FreeAskWorld: An Interactive and Closed-Loop Simulator for Human-Centric Embodied AI [24.545163508739943]
  FreeAskWorld is an interactive simulation framework that integrates large language models for high-level behavior planning and semantically grounded interaction.<n>Our framework supports scalable, realistic human-agent simulations and includes a modular data generation pipeline tailored for diverse embodied tasks.<n>We present and publicly release FreeAskWorld, a large-scale benchmark dataset comprising reconstructed environments, six diverse task types, 16 core object categories, 63,429 annotated sample frames, and more than 17 hours of interaction data.
  arXiv  Detail & Related papers  (2025-11-17T15:58:46Z)
• LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback [121.78866929908871]
  Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data.<n>We present LAM SIMULATOR, a comprehensive framework designed for online exploration of agentic tasks with high-quality feedback.<n>Our framework features a dynamic task query generator, an extensive collection of tools, and an interactive environment where Large Language Model (LLM) Agents can call tools and receive real-time feedback.
  arXiv  Detail & Related papers  (2025-06-02T22:36:02Z)
• YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models [50.35333054932747]
  We introduce a novel social simulator called YuLan-OneSim.<n>Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator.<n>We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication.
  arXiv  Detail & Related papers  (2025-05-12T14:05:17Z)
• Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment [7.695222586877482]
  This study investigates the potential and gap of simulative data to address the limitation of AI-based assessment tools.<n>Our findings reveal that while simulative data demonstrates promising results in training automated assessment models, its effectiveness has notable limitations.<n>The absence of real-world noise and biases, which are also present in over-processed real-world data, contributes to this limitation.
  arXiv  Detail & Related papers  (2025-02-10T11:40:11Z)
• User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation [38.48048183731099]
  User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI.<n>It involves creating an intelligent agent that mimics the actions of a human user interacting with an AI system.<n>User simulation has profound implications for diverse fields and plays a vital role in the pursuit of Artificial General Intelligence.
  arXiv  Detail & Related papers  (2025-01-08T10:49:13Z)
• A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis [6.460475042590685]
  We present a human crowd simulator, called UniCrowd, and its associated validation pipeline. We show how the simulator can generate annotated data, suitable for computer vision tasks, in particular for detection and segmentation, as well as the related applications, as crowd counting, human pose estimation, trajectory analysis and prediction, and anomaly detection.
  arXiv  Detail & Related papers  (2023-12-05T09:43:27Z)
• Reinforcement Learning with Human Feedback for Realistic Traffic Simulation [53.85002640149283]
  Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge. This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
  arXiv  Detail & Related papers  (2023-09-01T19:29:53Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• SimDem A Multi-agent Simulation Environment to Model Persons with Dementia and their Assistance [2.179313476241343]
  We propose a simulation model (SimDem) that focuses on cognitive impairments suffered by Persons with Dementia (PwD) SimDem can be easily configured and adapted by the users to model and evaluate assistive solutions.
  arXiv  Detail & Related papers  (2021-07-12T12:13:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.