Reading
An agent-based simulation framework for studying how large language models (LLMs) with different roles and professional backgrounds form, spread, and evolve beliefs in social networks.
Section 01
An agent-based simulation framework for studying how large language models (LLMs) with different roles and professional backgrounds form, spread, and evolve beliefs in social networks.
Section 02
Section 03
As large language models (LLMs) are increasingly deployed as intelligent agents to participate in collaborative tasks, an important yet under-explored question emerges: How do the beliefs of multiple LLMs form, spread, and evolve when they interact in social networks? Traditional single-agent reasoning research cannot answer this question, as the group dynamics introduced by social interactions are fundamentally different from isolated reasoning.
CoevolveSim is designed to fill this research gap. It is an agent-based simulation framework specifically for studying the process of belief co-evolution among LLMs with different roles and professional backgrounds in social networks. Developed by a research team at Northeastern University, this framework has been open-sourced as a supporting codebase for an academic paper.
Section 04
The simulation cycle of CoevolveSim follows a concise yet powerful four-step model:
Section 05
Each agent first forms an initial belief about a certain statement. This belief can be based on the agent's own knowledge, preset stance, or random initialization, depending on the experimental design.
Section 06
Agents receive belief summaries from neighboring nodes in the social network. This information propagation simulates opinion exchange in real society—individuals do not make decisions in isolation but are influenced by their social circles.
Section 07
Based on the received neighbor belief information, agents continuously update their beliefs through multiple rounds of interaction. The update rules are configurable, allowing researchers to test different hypotheses about belief evolution.
Section 08
Each simulation round generates rich metrics and outputs, which are saved to timestamped folders for subsequent analysis. These include individual belief trajectories, network-level convergence patterns, influence distributions, etc.
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