Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop

• URL: http://arxiv.org/abs/2608.11215v1
• Date: Sun, 19 Jul 2026 08:44:38 GMT
• Title: Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
• Abstract summary: We create a simulation of a large language model society using a few hundred agents.<n>We map perception and memory to an effective theory and a predicted $N$-trend of the surrogate error.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent. We turn a statistical-physics observation into a method: replace each LLM agent by a low-parameter model fitted from a few hundred to a few thousand cheap queries, then run the society at any $N$ on a laptop. Whether this works is decided before the simulation runs, chiefly by what each agent perceives. We introduce an [interaction order x memory] taxonomy that maps perception and memory to an effective theory and a predicted $N$-trend of the surrogate error. We validate it on a faithful reimplementation of the LLM macroeconomy EconAgent and seven further named LLM simulations, with agent decisions cloned from genuine LLM elicitations (primarily DeepSeek) for a few dollars; the predicted error trends hold cell by cell, and the two refuted predictions, both on a strongly saturating response and traced to its curvature, are themselves matched quantitatively by the theory with no free parameters.

Related papers

• Scaling Multi-Agent Systems with Prospect-State Propagation [8.564207831867272]
  We show a phenomenon in which agent heterogeneity gradually decreases during simulation.<n>Inspired by prospect theory, PspMAS decouples each agent's micro state into a compact Prospect State and an expressive Semantic State.<n>These two components work complementarily, providing a scalable LLM-based multi-agent simulation solution.
  arXiv  Detail & Related papers  (2026-09-07T22:30:12Z)
• Attributing Emergence in Million-Agent Systems [68.53670424791751]
  Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents.<n>Such studies require attributing macro emergence to individual agents.<n>We adapt Aumann--Shapley path-integral attribution to LLM-powered MAS at million-agent scale.
  arXiv  Detail & Related papers  (2026-05-12T01:49:41Z)
• ReDAct: Uncertainty-Aware Deferral for LLM Agents [61.507376922278894]
  We propose ReDAct (Reason-Defer-Act) to solve sequential decision-making problems.<n>In ReDAct, an agent is equipped with two LLMs: a small, cheap model used by default, and a large, more reliable but expensive model.<n>We show that deferring only about 15% of decisions to the large model can match the quality of using it exclusively, while significantly reducing inference costs.
  arXiv  Detail & Related papers  (2026-04-08T12:51:01Z)
• Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability [0.6117371161379209]
  The landscape of Large Language Models (LLMs) shifts rapidly towards dynamic, multi-agent systems.<n>This paper proposes a verification framework that achieves tractable asymmetric effort.<n>We show that targeted verification can be over 12 times faster than full regeneration.
  arXiv  Detail & Related papers  (2025-09-14T03:30:06Z)
• Integrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour [35.19786322586909]
  We propose Agentic eXplanations via Interrogative Simulation (AXIS)<n>AXIS generates human-centred action explanations for multi-agent policies.<n>We evaluate AXIS on autonomous driving across ten scenarios for five LLMs.
  arXiv  Detail & Related papers  (2025-05-23T12:19:18Z)
• MALT: Improving Reasoning with Multi-Agent LLM Training [67.76186488361685]
  MALT (Multi-Agent LLM Training) is a novel post-training strategy that divides the reasoning process into generation, verification, and refinement steps.<n>On MATH, GSM8K, and CSQA, MALT surpasses the same baseline LLM with a relative improvement of 15.66%, 7.42%, and 9.40% respectively.
  arXiv  Detail & Related papers  (2024-12-02T19:30:36Z)
• 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)
• Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL [154.13105285663656]
  A cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications. Unfortunately, the theoretical understanding of this MARL problem is lacking due to the curse of many agents and the limited exploration of the relational reasoning in existing works. We prove that the suboptimality gaps of the model-free and model-based algorithms are independent of and logarithmic in the number of agents respectively, which mitigates the curse of many agents.
  arXiv  Detail & Related papers  (2022-09-20T16:42:59Z)
• ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations [110.72725220033983]
  Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps. ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations. In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
  arXiv  Detail & Related papers  (2021-06-10T04:32:20Z)

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.