{"ID":2888452,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23694","arxiv_id":"2507.23694","title":"A survey of multi-agent geosimulation methodologies: from ABM to LLM","abstract":"We provide a comprehensive examination of agent-based approaches that codify the principles and linkages underlying multi-agent systems, simulations, and information systems. Based on two decades of study, this paper confirms a framework intended as a formal specification for geosimulation platforms. Our findings show that large language models (LLMs) can be effectively incorporated as agent components if they follow a structured architecture specific to fundamental agent activities such as perception, memory, planning, and action. This integration is precisely consistent with the architecture that we formalize, providing a solid platform for next-generation geosimulation systems.","short_abstract":"We provide a comprehensive examination of agent-based approaches that codify the principles and linkages underlying multi-agent systems, simulations, and information systems. Based on two decades of study, this paper confirms a framework intended as a formal specification for geosimulation platforms. Our findings show...","url_abs":"https://arxiv.org/abs/2507.23694","url_pdf":"https://arxiv.org/pdf/2507.23694v1","authors":"[\"Virginia Padilla\",\"Jacinto Dávila\"]","published":"2025-07-31T16:12:22Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}