The paper investigates whether large language models (LLMs) produce self‑consistent numerical preference judgments when asked to estimate a person’s willingness to pay for items. By comparing differences in stated willingness to pay with the payment that would make a person indifferent between items, the authors develop statistical tests to measure deviations from a single utility function. Experiments on flight, apartment, and hotel scenarios across six LLMs show persistent inconsistencies, indicating that LLM‑derived preference judgments cannot be reliably summarized by one utility function.
By Matthew T. Ford, Francis Bahk, Jingjing Wang, Adam S. Jovine, Tinghan Ye, David B. Shmoys, Peter I. Frazier
The paper demonstrates that in open‑weight LLM inference markets, selecting a model is insufficient; clients must also choose a provider, as the same model can differ markedly in quality, latency, availability, and price across providers. The authors propose a market‑aware routing approach, including a measured‑map policy and an online router called FACET, which certifies provider feasibility for each task and safely falls back to a reliable anchor. Experiments show that this strategy yields cost savings while maintaining quality and avoiding degraded endpoints.
By Liang He, Jingbo Wen, Yixiong Chen, Yue Yang, Qizhen Lan, Kangning Cui, Xilu Wang
arXiv:2607. 19967v1 Announce Type: cross Abstract: Shippers are beginning to delegate carrier selection to large language model (LLM) agents.
By Takahiro Ezaki, Naoto Imura, Katsuhiro Nishinari
arXiv:2602. 09802v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in applications such as travel assistance and purchasing support, they are often required to make subjective choices on behalf of users in settings where no objectively correct answer exists.
By Manon Reusens, Sofie Goethals, Toon Calders, David Martens
arXiv:2609.37493v1 Announce Type: cross
Abstract: Serving an answer from a large language model requires deciding when to abstain, yet a verifier's ranking accuracy alone does not determine the error...
By Dongyub Jude Lee, Jungseob Lee, Chanjun Park, Hyeonseok Moon, Heuiseok Lim
arXiv:2608.23411v1 Announce Type: new
Abstract: LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes tha...
By Andrei Chetvergov, Stepan Ukolov, Timofei Sivoraksha, Alexander Evseev, Danil Sazanakov, Mikhail Solovev, Sergey Bolovtsov
E-Commerce Bench is an open‑source benchmark that simulates a year‑long e‑commerce operation, requiring LLM agents to manage multiple online stores, negotiate with suppliers, optimize sales, fulfill orders, handle returns, and manage cash flow. The environment uses real product and supplier data, a calendar of promotions and shocks, and deterministic customer and negotiation models to enable reproducible evaluation. The study evaluates 18 state‑of‑the‑art models across seven metrics, finding no single model dominates, with GPT‑5.6 Sol achieving the highest year‑end assets but lagging in fraud avoidance and operational efficiency.
By Wei Fan, Xinjie Shen, Xudong Guo, Jianhong Tu, Yang Su, Yinger Zhang, Lianghao Deng, Fengyu Wang, Baohua Dong, Yangqiu Song, Dayiheng Liu
arXiv:2608. 09282v1 Announce Type: new Abstract: Real-world shopping often requires constructing a basket of complementary items rather than retrieving a single product.
By Adrian Li, Kelong Mao, Yudong Guo, Heming Xia, Xinwei Yang, Lirui Luo, Jace Wong, Pu Yao, Sulong Xu, Simiu Gu
arXiv:2607. 28956v1 Announce Type: new Abstract: Large language model agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria.
By Qiming Shi, Yulong Tao, Linbo Jin, Zhaolu Kang, Yibo Dou, Jiawen Zhu, Tianjun Pan, Shaokang Fu, Chengyu Wang, Siyue Li, Yaping Cheng, Di Weng, Chengfu Huo
arXiv:2605.25200v3 Announce Type: replace
Abstract: Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single us...
By Xiang Cheng, Yulan Hu, Lulu Zheng, Xiangwen Zhang, Zheng Pan, Xin Li, Yong Liu
arXiv:2608. 07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts.
By Chen Liang, Fasheng Xu
arXiv:2608. 14068v1 Announce Type: cross Abstract: Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a stricter requirement: recommendations must be drawn only from a merchant's fixed catalog, without web search or unsupported product claims.
By Juli Huang, Hannah Clay, Sajjad Beygi, Thomas Sarda, Negin Golrezaei, Amin Saberi