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Large language models across the agent-based modelling cycle
US & West ·
The paper reviews current uses of LLMs in agent-based modelling and critically examines opportunities and challenges across the modelling cycle, from problem formulation to documentation and communication of results, including summarization, information extraction and reasoning.
Why it matters
Outlines where language models can speed up building and maintaining simulation models and where caution is required.
Original headline
Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
arXiv:2610.28907v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) offers a principled way of populating simulated economies with many heterogeneous decision makers.
The same TAdviser item: the generative AI market reached 58 billion rubles in 2025, with 44 billion corporate. Measurable results are becoming the key criterion, while most projects remain pilots due to integration, data quality, security and cost issues.
Russia's generative AI market reached 58 billion rubles in 2025, with 44 billion in the corporate segment. Spending growth has not translated into comparable results: many projects remain pilots, and moving to operation depends on integration, data quality, security and processing cost.
