Newest papers

Toolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization

Zhenwei Tang, Di Bai, Jintao Liu, Xidong Wu, Qianfeng Wen, Xinwu Cheng · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28454 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

卖家正在开始向其他代理商出售。卖家代理人正在写下工具、服务和产品的列表,买家代理人在他们选择使用时读了这些列表。我们显示卖家代理人只说要选择更频繁地进行生成引擎优化,这会扭曲卖家和买家的市场,我们在亚马逊和Instagram上建立工具市场,并在每个问题上运行每个工具,所以我们知道哪些工具可以回答每个问题。在我们测试的所有五个生产模型中,卖家重写他们的描述,以声称越来越多的领域,包括他们的工具回报成本不到一半的目标。我们称此过程为工具膨胀。在市场上没有什么东西应该限制它,而且它取决于模型,因为卖家选择的五个模型与自己选择的证据不同,卖家从四个模型

Machine translation (Meta M2M100); the English original follows.

Abstract

Agents are starting to sell to other agents. Seller agents write the listings of tools, services and products, and buyer agents read these listings when they choose what to use. We show that seller agents told only to be selected more often practice generative engine optimization on their own, and that this distorts the market for sellers and for buyers. We build tool markets from real data tools on Amazon and Instagram and run every tool on every question, so we know which tools can answer each question. Under all five production models we test, sellers rewrite their descriptions to claim more and more fields, including fields that their tools return for fewer than half of the targets. We call this process toolflation. Nothing in the market limits it, and how far it goes depends on the model, as the number of claims that contradict the sellers' own evidence differs by an order of magnitude between models. Toolflation shifts the buyers' choices between sellers, and the most capable tool loses share in seven of the ten combinations of seller model and platform. Under four models, the choices stay with capable tools and buyer agents keep their task success. Under the fifth, sellers copy one another until the descriptions no longer tell the tools apart. Buyer agents then pick tools by their position in the list, tools that answer almost nothing win a quarter to two fifths of the choices, and task success falls from 89% to about 30% on Amazon, close to random choice, and from 77% to 40% on Instagram. The sellers did only what their vendors asked, and the cost fell on buyers they never dealt with. We therefore argue that honesty toward third parties under competition is part of safety alignment, and that models should be tested as sellers before they are deployed as sellers.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Task (project management), Computer science, Nothing, Order (exchange), Generative grammar, Process (computing)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange