[论文讨论] PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling #53
Description
论文信息
标题: PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling
作者: Kavana Venkatesh, Yinhan He, Jundong Li, Jiaming Cui
发布时间: 2026-02-05
分类: cs.LG
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简介
Large language model (LLM)-based multi-agent systems enable expressive agent reasoning but are expensive to scale and poorly calibrated for timestep-aligned state-transition simulation, while classical agent-based models (ABMs) offer interpretability but struggle to integrate rich individual-level signals and non-stationary behaviors. We propose PhysicsAgentABM, which shifts inference to behaviorally coherent agent clusters: state-specialized symbolic agents encode mechanistic transition priors, a multimodal neural transition model captures temporal and interaction dynamics, and uncertainty-aware epistemic fusion yields calibrated cluster-level transition distributions. Individual agents then stochastically realize transitions under local constraints, decoupling population inference from entity-level variability. We further introduce ANCHOR, an LLM agent-driven clustering strategy based on cross-contextual behavioral responses and a novel contrastive loss, reducing LLM calls by up to 6-8 times. Experiments across public health, finance, and social sciences show consistent gains in event-time accuracy and calibration over mechanistic, neural, and LLM baselines. By re-architecting generative ABM around population-level inference with uncertainty-aware neuro-symbolic fusion, PhysicsAgentABM establishes a new paradigm for scalable and calibrated simulation with LLMs.
推荐理由
论文5值得探讨：ANCHOR聚类策略的有效性、物理引导 vs 数据驱动的方法论比较、在社会科学领域的应用边界
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