System-level Impact of Enabling All Agents with ID-RAG

Determine the broader impact of enabling all agents in multi-agent generative agent social simulations with Identity Retrieval-Augmented Generation (ID-RAG) on social dynamics, emergent behaviors, and overall system fidelity.

Background

The paper introduces Identity Retrieval-Augmented Generation (ID-RAG) and demonstrates its benefits for long-horizon persona coherence and action alignment within Human-AI Agents (HAis) deployed in the Concordia social simulation framework. The evaluation primarily focuses on agent-level metrics such as Identity Recall Score and Action Alignment Score, with simulation convergence time serving as a limited system-level proxy.

The authors explicitly acknowledge that broader system-level consequences—such as changes to social dynamics, emergent behavior patterns, and overall fidelity—have not been measured when all agents in a simulation are ID-RAG enabled. Assessing these collective effects remains an unresolved question beyond the agent-centric analysis presented.