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Large Behavior Model: A Promptable Digital Twin of the Retail Customer

Summary

We present the Large Behavioral Model (LBM) that learns customer decision making directly from large-scale retail transactions through a unified Person-Environment formulation. Using behavioral profiles from historical purchases and retrieval-augmented generation, LBM is trained with continued pre-training, supervised fine-tuning, and reinforcement learning with verifiable rewards. It outperforms frontier general-purpose language models on purchase prediction, basket completion, and other retail tasks, with strong zero-shot and fine-tuned transfer across retailers. Ablation studies highlight continued pre-training as the main driver of generalization, and reinforcement learning improves reliance on behavioral evidence.

SourcearXiv AIAuthor: Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
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[Submitted on 8 Jul 2026]

Title:Large Behavior Model: A Promptable Digital Twin of the Retail Customer

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Abstract:Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data. We present the Large Behavioral Model (LBM) that learns customer decision making directly from large-scale retail transactions through a unified Person-Environment formulation. Customer state is represented by a behavioral profile derived from historical purchases, while product context is incorporated through retrieval-augmented generation. The model is trained using continued pre-training on verbalized behavioral data, supervised fine-tuning for decision generation, and reinforcement learning with verifiable rewards for evidence-based calibration. We evaluate the proposed framework on purchase prediction, hard-negative discrimination, basket completion, promotion response, and cross-domain voucher redemption. The model consistently outperforms frontier general-purpose language models on in-domain retail tasks while demonstrating strong zero-shot and fine-tuned transfer across retailers and decision domains. Ablation studies show that continued pre-training is the primary driver of behavioral generalization, retrieval is most effective when applied during both training and inference, and reinforcement learning improves reliance on explicit behavioral evidence over generic language-model priors. These results demonstrate that behavioral knowledge encoded in transaction histories can be effectively learned by language models, providing a scalable foundation for customer digital twins and behavior simulation.

Comments: 17 pages, 5 figures

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.06993 [cs.AI]

(or arXiv:2607.06993v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.06993

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Krittin Pachtrachai PhD [view email] [v1] Wed, 8 Jul 2026 04:31:18 UTC (242 KB)

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Key points and analysis

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Key points

  • LBM learns customer behavior from transaction histories using a Person-Environment framework
  • Outperforms general-purpose LLMs on multiple retail tasks (purchase prediction, basket completion, etc.)
  • Continued pre-training is key for behavioral generalization; retrieval works best when used in both training and inference
  • Demonstrates strong zero-shot and fine-tuned transfer across retailers and decision domains

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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