Generalized Multi-agent Social Simulation Framework

• URL: http://arxiv.org/abs/2510.06225v1
• Date: Fri, 26 Sep 2025 09:36:16 GMT
• Title: Generalized Multi-agent Social Simulation Framework
• Authors: Gang Li, Jie Lin, Yining Tang, Ziteng Wang, Yirui Huang, Junyu Zhang, Shuang Luo, Chao Wu, Yike Guo,
• Abstract summary: Multi-agent social interaction has clearly benefited from Large Language Models.<n>Current simulation systems face challenges such as difficulties in scaling to diverse scenarios and poor reusability due to a lack of modular design.<n>We develop a modular, object-oriented framework that organically integrates various base classes through a hierarchical structure, harvesting scalability and reusability.
• Score: 43.47601625634413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent social interaction has clearly benefited from Large Language Models. However, current simulation systems still face challenges such as difficulties in scaling to diverse scenarios and poor reusability due to a lack of modular design. To address these issues, we designed and developed a modular, object-oriented framework that organically integrates various base classes through a hierarchical structure, harvesting scalability and reusability. We inherited the framework to realize common derived classes. Additionally, a memory summarization mechanism is proposed to filter and distill relevant information from raw memory data, prioritizing contextually salient events and interactions. By selecting and combining some necessary derived classes, we customized a specific simulated environment. Utilizing this simulated environment, we successfully simulated human interactions on social media, replicating real-world online social behaviors. The source code for the project will be released and evolve.

Related papers

• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior [0.0]
  We introduce the Unified Interaction Foundation Model (UIFM), a foundation model engineered for genuine behavioral understanding.<n>At its core is the principle of composite tokenization, where each multi-attribute event is treated as a single, semantically coherent unit.<n>This allows UIFM to learn the underlying "grammar" of user behavior, perceiving entire interactions rather than a disconnected stream of data points.
  arXiv  Detail & Related papers  (2025-09-07T11:57:41Z)
• Simulating Generative Social Agents via Theory-Informed Workflow Design [11.992123170134185]
  We propose a theory-informed framework that provides a systematic design process for social agents.<n>Our framework is grounded in principles from Social Cognition Theory and introduces three key modules: motivation, action planning, and learning.<n>Experiments demonstrate that our theory-driven agents reproduce realistic human behavior patterns under complex conditions.
  arXiv  Detail & Related papers  (2025-08-12T08:14:48Z)
• Multi-Actor Generative Artificial Intelligence as a Game Engine [49.360775442760314]
  Generative AI can be used in multi-actor environments with purposes ranging from social science modeling to interactive narrative and AI evaluation.<n>We argue here that a good approach is to take inspiration from tabletop role-playing games (TTRPGs), where a Game Master (GM) is responsible for the environment and generates all parts of the story not directly determined by the voluntary actions of player characters.
  arXiv  Detail & Related papers  (2025-07-10T22:31:09Z)
• SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning [53.16179295245888]
  We introduce SIV-Bench, a novel video benchmark for evaluating the capabilities of Multimodal Large Language Models (MLLMs) across Social Scene Understanding (SSU), Social State Reasoning (SSR), and Social Dynamics Prediction (SDP)<n>SIV-Bench features 2,792 video clips and 8,792 meticulously generated question-answer pairs derived from a human-LLM collaborative pipeline.<n>It also includes a dedicated setup for analyzing the impact of different textual cues-original on-screen text, added dialogue, or no text.
  arXiv  Detail & Related papers  (2025-06-05T05:51:35Z)
• Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research [0.0]
  This article proposes a social simulation paradigm based on the GPT-3.5 large language model. It involves constructing Generative Agents that emulate human cognition, memory, and decision-making frameworks. Agents exhibit personalized customization, and public events are seamlessly incorporated through natural language processing.
  arXiv  Detail & Related papers  (2023-11-12T20:48:01Z)
• CGMI: Configurable General Multi-Agent Interaction Framework [0.0]
  General Multi-Agent Interaction (CGMI) framework designed to replicate human interactions in real-world scenarios. We propose a tree-structured methodology for the assignment, detection, and maintenance of agent personality. We have also integrated general agents to augment the virtual environment's realism.
  arXiv  Detail & Related papers  (2023-08-24T02:03:29Z)
• 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)
• Modular Deep Learning [120.36599591042908]
  Transfer learning has recently become the dominant paradigm of machine learning. It remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference. Modular deep learning has emerged as a promising solution to these challenges.
  arXiv  Detail & Related papers  (2023-02-22T18:11:25Z)
• Adaptive Synthetic Characters for Military Training [0.9802137009065037]
  Behaviors of synthetic characters in current military simulations are limited since they are generally generated by rule-based and reactive computational models. This paper introduces a framework that aims to create autonomous synthetic characters that can perform coherent sequences of believable behavior.
  arXiv  Detail & Related papers  (2021-01-06T18:45:48Z)

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.