HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation

• URL: http://arxiv.org/abs/2601.05656v2
• Date: Thu, 15 Jan 2026 07:37:55 GMT
• Title: HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation
• Abstract summary: A robust framework should capture macro-level joint distributions while ensuring micro-level individual rationality.<n>Existing approaches fall into two categories: static data-based retrieval methods that fail to adapt to unseen topics absent from the data, and LLM-based generation methods that lack macro-level distribution awareness.<n>We propose HAG, a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process.
• Score: 26.61515474480962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-fidelity agent initialization is crucial for credible Agent-Based Modeling across diverse domains. A robust framework should be Topic-Adaptive, capturing macro-level joint distributions while ensuring micro-level individual rationality. Existing approaches fall into two categories: static data-based retrieval methods that fail to adapt to unseen topics absent from the data, and LLM-based generation methods that lack macro-level distribution awareness, resulting in inconsistencies between micro-level persona attributes and reality. To address these problems, we propose HAG, a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process. Firstly, utilizing a World Knowledge Model to infer hierarchical conditional probabilities to construct the Topic-Adaptive Tree, achieving macro-level distribution alignment. Then, grounded real-world data, instantiation and agentic augmentation are carried out to ensure micro-level consistency. Given the lack of specialized evaluation, we establish a multi-domain benchmark and a comprehensive PACE evaluation framework. Extensive experiments show that HAG significantly outperforms representative baselines, reducing population alignment errors by an average of 37.7% and enhancing sociological consistency by 18.8%.

Related papers

• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• Scaling Behavior Foundation Model for Humanoid Robots [77.31115920959364]
  Behavior Foundation Models (BFMs) have emerged as a promising solution to address challenges by leveraging large-scale behavioral data.<n>We show that our approach yields significant improvements in control fidelity and task generalization.<n>These results establish BFM as a principled and effective foundation for scalable and general-purpose humanoid control.
  arXiv  Detail & Related papers  (2026-07-16T16:08:27Z)
• Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection [46.657161089480375]
  This paper proposes a novel hierarchical federated learning framework.<n>The framework orchestrates a synergistic two-tier optimization strategy.<n> Comprehensive evaluations on a large-scale, real-world structural inspection dataset demonstrate that the hierarchical integration of macro-clustering and micro-regularization successfully neutralizes dual-level heterogeneity.
  arXiv  Detail & Related papers  (2026-06-02T03:14:26Z)
• Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition [60.52810518437911]
  High-fidelity generative models are increasingly needed in privacy-sensitive scenarios.<n> limited data leads to poor generative models, which in turn fail to mitigate data scarcity.<n>We propose a reinforcement-guided synthetic data generation framework that adapts general-domain generative priors to identity recognition tasks.
  arXiv  Detail & Related papers  (2026-04-09T06:52:03Z)
• Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach [1.8505047763172107]
  Large Language Models (LLMs) are increasingly used to simulate social attitudes and behaviors.<n>Current simulation practice often collapses diversity into an "average persona," masking subgroup variation that is central to social reality.<n>This study introduces audience segmentation as a systematic approach for restoring heterogeneity in LLM-based social simulation.
  arXiv  Detail & Related papers  (2026-04-08T04:29:08Z)
• Bilevel Optimization for Covert Memory Tampering in Heterogeneous Multi-Agent Architectures (XAMT) [0.0]
  Multi-Agent Systems (MAS) are inherently heterogeneous, integrating conventional Multi-Agent Reinforcement Learning (MARL) with emerging Large Language Model (LLM) agent architectures.<n>A critical shared vulnerability is reliance on centralized memory components: the shared Experience Replay (ER) buffer in MARL and the external Knowledge Base (K) in RAG agents.<n>This paper proposes XAMT (Bilevel Optimization for Covert Memory Tampering in Heterogeneous Multi-Agent Architectures), a novel framework that formalizes attack generation as a bilevel optimization problem.
  arXiv  Detail & Related papers  (2025-12-15T23:04:48Z)
• Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection [108.5042835056188]
  This work introduces Agent4FaceForgery to address two fundamental problems.<n>How to capture the diverse intents and iterative processes of human forgery creation.<n>How to model the complex, often adversarial, text-image interactions that accompany forgeries in social media.
  arXiv  Detail & Related papers  (2025-09-16T01:05:01Z)
• HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation [12.655334562608314]
  Federated Learning (FL) is a decentralized approach where multiple clients collaboratively train a shared global model without sharing their raw data.<n>This paper introduces Hierarchical Federated Domain Generalization (HFedDG), a novel scenario designed to investigate domain shift within hierarchical architectures.
  arXiv  Detail & Related papers  (2025-08-07T08:14:52Z)
• Fair Deepfake Detectors Can Generalize [51.21167546843708]
  We show that controlling for confounders (data distribution and model capacity) enables improved generalization via fairness interventions.<n>Motivated by this insight, we propose Demographic Attribute-insensitive Intervention Detection (DAID), a plug-and-play framework composed of: i) Demographic-aware data rebalancing, which employs inverse-propensity weighting and subgroup-wise feature normalization to neutralize distributional biases; and ii) Demographic-agnostic feature aggregation, which uses a novel alignment loss to suppress sensitive-attribute signals.<n>DAID consistently achieves superior performance in both fairness and generalization compared to several state-of-the-art
  arXiv  Detail & Related papers  (2025-07-03T14:10:02Z)
• NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
  We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
  arXiv  Detail & Related papers  (2025-06-11T06:59:17Z)
• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework [53.82097200295448]
  Mean-Field LLM (MF-LLM) is first to incorporate mean field theory into social simulation.<n>MF-LLM models bidirectional interactions between individuals and the population through an iterative process.<n> IB-Tune is a novel fine-tuning method inspired by the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-04-30T12:41:51Z)
• Local vs. Global Models for Hierarchical Forecasting [0.0]
  This study explores the influence of distinct information utilisation on the accuracy of hierarchical forecasts. We develop Global Forecasting Models (GFMs) to exploit cross-series and cross-hierarchies information. Two specific GFMs based on LightGBM are introduced, demonstrating superior accuracy and lower model complexity.
  arXiv  Detail & Related papers  (2024-11-10T08:51:49Z)
• Adapting tree-based multiple imputation methods for multi-level data? A simulation study [0.0]
  Tree-based imputation methods implicitly assume independence between observations.<n>Alternative tree-based approaches have shown promise for individual-level data, but remain largely unexplored for hierarchical contexts.
  arXiv  Detail & Related papers  (2024-01-25T13:12:50Z)
• Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
  We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity. We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class. We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
  arXiv  Detail & Related papers  (2022-01-31T20:58:47Z)

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.