LLM-powered reasoning in agent-based modeling

• URL: http://arxiv.org/abs/2607.06757v1
• Date: Tue, 07 Jul 2026 19:39:01 GMT
• Title: LLM-powered reasoning in agent-based modeling
• Abstract summary: We introduce a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework to predict human decision-making in an ABM simulation.<n>As a proof-of-concept, we use HALE to simulate COVID-19 and its effects in Salt Lake County, UT.
• Score: 0.24570599510418345
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on static prior, which prevents the models from adapting to real-time changes. Our research provides a novel approach to addressing this information gap. Large language models (LLMs) offer new opportunities to predict human decision-making. Here, we introduce a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework that leverages LLMs to predict human decision-making in an ABM simulation. As a proof-of-concept, we use HALE to simulate COVID-19 and its effects in Salt Lake County, UT.

Related papers

• Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking [0.42025549992700273]
  Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making.<n>Recent advances in large language models (LLMs) make it tempting to replace, enrich, or perturb these rules with LLM-based agentic capabilities.<n>We investigate how introducing LLM-driven decisions affect the reliability, computational cost, and behavior of ABM simulations.
  arXiv  Detail & Related papers  (2026-07-20T13:49:51Z)
• SimuRA: A World-Model-Driven Simulative Reasoning Architecture for General Goal-Oriented Agents [15.91448165400836]
  SimuRA is a goal-oriented architecture for generalized agentic reasoning.<n>We release ReasonerAgent-Web, a web-browsing agent built on SimuRA, as an open-source research demo.
  arXiv  Detail & Related papers  (2025-07-31T17:57:20Z)
• Aligning LLM with human travel choices: a persona-based embedding learning approach [15.11130742093296]
  This paper introduces a novel framework for aligning large language models with human travel choice behavior.<n>Our framework uses a persona inference and loading process to condition LLMs with suitable prompts to enhance alignment.
  arXiv  Detail & Related papers  (2025-05-25T06:54:01Z)
• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection.<n>Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework [15.11130742093296]
  We propose a general LLM-agent-based modeling framework for transportation systems.<n>Our conceptual framework design closely replicates the decision-making and interaction processes and traits of human travelers.<n>Although further refinement of the LLM-agent-based modeling framework is necessary, we believe that this approach has the potential to improve transportation system modeling and simulation.
  arXiv  Detail & Related papers  (2024-12-09T17:24:41Z)
• Scaling Diffusion Language Models via Adaptation from Autoregressive Models [105.70889434492143]
  Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling.<n>We show that we can convert AR models ranging from 127M to 7B parameters into diffusion models DiffuGPT and DiffuLLaMA, using less than 200B tokens for training.<n>Our experimental results reveal that these models outperform earlier DLMs and are competitive with their AR counterparts.
  arXiv  Detail & Related papers  (2024-10-23T14:04:22Z)
• GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
  We propose a novel large language model (LLMs)-based simulation platform called textitGenSim.<n>Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts.<n>To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
  arXiv  Detail & Related papers  (2024-10-06T05:02:23Z)
• On the limits of agency in agent-based models [13.130587222524305]
  Agent-based modeling offers powerful insights into complex systems, but its practical utility has been limited by computational constraints. Recent advancements in large language models (LLMs) could enhance ABMs with adaptive agents, but their integration into large-scale simulations remains challenging. We present LLM archetypes, a technique that balances behavioral complexity with computational efficiency, allowing for nuanced agent behavior in large-scale simulations.
  arXiv  Detail & Related papers  (2024-09-14T04:17:24Z)
• Mental Modeling of Reinforcement Learning Agents by Language Models [14.668006477454616]
  This study empirically examines, for the first time, how well large language models can build a mental model of agents. This research may unveil the potential of leveraging LLMs for elucidating RL agent behaviour.
  arXiv  Detail & Related papers  (2024-06-26T17:14:45Z)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• Predictable MDP Abstraction for Unsupervised Model-Based RL [93.91375268580806]
  We propose predictable MDP abstraction (PMA) Instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space. We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches.
  arXiv  Detail & Related papers  (2023-02-08T07:37:51Z)
• METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals [151.3601429216877]
  We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model. We propose a recipe, namely "Model generated dEnoising TRaining Objective" (METRO) The resultant models, METRO-LM, consisting of up to 5.4 billion parameters, achieve new state-of-the-art on the GLUE, SuperGLUE, and SQuAD benchmarks.
  arXiv  Detail & Related papers  (2022-04-13T21:39:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.