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