Reimagining Agent-based Modeling with Large Language Model Agents via Shachi

• URL: http://arxiv.org/abs/2509.21862v2
• Date: Fri, 10 Oct 2025 02:11:03 GMT
• Title: Reimagining Agent-based Modeling with Large Language Model Agents via Shachi
• Authors: So Kuroki, Yingtao Tian, Kou Misaki, Takashi Ikegami, Takuya Akiba, Yujin Tang,
• Abstract summary: The study of emergent behaviors in large language model (LLM)-driven multi-agent systems is a critical research challenge.<n>We introduce Shachi, a formal methodology and modular framework that decomposes an agent's policy into core cognitive components.<n>We validate our methodology on a comprehensive 10-task benchmark and demonstrate its power through novel scientific inquiries.
• Score: 16.625794969005966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The study of emergent behaviors in large language model (LLM)-driven multi-agent systems is a critical research challenge, yet progress is limited by a lack of principled methodologies for controlled experimentation. To address this, we introduce Shachi, a formal methodology and modular framework that decomposes an agent's policy into core cognitive components: Configuration for intrinsic traits, Memory for contextual persistence, and Tools for expanded capabilities, all orchestrated by an LLM reasoning engine. This principled architecture moves beyond brittle, ad-hoc agent designs and enables the systematic analysis of how specific architectural choices influence collective behavior. We validate our methodology on a comprehensive 10-task benchmark and demonstrate its power through novel scientific inquiries. Critically, we establish the external validity of our approach by modeling a real-world U.S. tariff shock, showing that agent behaviors align with observed market reactions only when their cognitive architecture is appropriately configured with memory and tools. Our work provides a rigorous, open-source foundation for building and evaluating LLM agents, aimed at fostering more cumulative and scientifically grounded research.

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