arXiv AI June 16, 2026 By Chengpiao Huang, Kaizheng Wang

LLM-Powered Virtual Population for Demand Simulation and Pricing

arXiv:2606. 16183v1 Announce Type: cross Abstract: We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices.

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llms

arXiv Computation and Language

Sep 1

LLP: LLM-Based Product Pricing in E-commerce

The paper introduces LLP, a Large Language Model–based generative framework for pricing second‑hand products on consumer‑to‑consumer platforms. LLP retrieves similar items to capture market dynamics, then uses LLMs to generate price suggestions, refined through supervised fine‑tuning and group relative policy optimization. A confidence‑based filter rejects unreliable predictions, and experiments show LLP outperforms prior methods, achieving higher static adoption rates when deployed on Xianyu.

By Hairu Wang, Sheng You, Qiheng Zhang, Xike Xie, Shuguang Han, Yuchen Wu, Fei Huang, Jufeng Chen

llms fine-tuning

arXiv Machine Learning

Jul 3

Learning Consumer Preferences from Bundle Sales Data

arXiv:2209. 04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data.

By Ningyuan Chen, Setareh Farajollahzadeh, Qingwei Jin, Fanni Shen, Guan Wang

arXiv AI

Aug 3

What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents

arXiv:2604. 04468v2 Announce Type: replace Abstract: Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through buyer-seller interaction to purchase decisions.

By Jeonghwan Choi, Jibin Hwang, Gyeonghun Sun, Minjeong Ban, Taewon Yun, Hyeonjae Cheon, Hwanjun Song

llms agents

arXiv Machine Learning

Jun 15

High-Frequency Pricing at Scale for E-Commerce

arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.

By Stefan Birr, Tobias Huelden, Mones Raslan, Adele Gouttes, Andreas Schmitt, Mateusz Koren, Johannes Stephan, Robert Streek, Manuel Kunz, Tim Januschowski

arXiv Machine Learning

Sep 2

Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption

arXiv:2609.00710v1 Announce Type: cross Abstract: An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly mu...

By Patrick Wong

llms benchmarks

arXiv AI

Aug 14

Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy

arXiv:2608. 12674v1 Announce Type: new Abstract: Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers.

By Ravi Teja Chunduri, Srikaran Reddy Boya, Deep Narayan Mishra, Ajay Kumar B, Karthik Kumaran, Pranay Kona

llms agents multimodal

One email a morning, machine-written

Related stories

LLP: LLM-Based Product Pricing in E-commerce

Learning Consumer Preferences from Bundle Sales Data

What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents

High-Frequency Pricing at Scale for E-Commerce

Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption

Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy