Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce

• URL: http://arxiv.org/abs/2606.24783v1
• Date: Tue, 23 Jun 2026 16:42:21 GMT
• Title: Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
• Abstract summary: We argue that the arrival of agent-native micro-payment rails changes what is scarce.<n>We envision agentic e-commerce as a micro-transaction market for verified information.
• Score: 4.655159257282136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Commercial NLP treats the shopping chatbot as a recommender or a conversion tool: its job is to match a user to a catalogue entry and close a sale. We argue that the arrival of agent-native micro-payment rails (e.g., x402, AP2) changes what is scarce. When the buyer is an autonomous agent that can investigate exhaustively, the bottleneck is no longer matching products but acquiring trustworthy, decision-relevant information about them. We envision agentic e-commerce as a micro-transaction market for verified information: buyer agents spend fractions of a cent to progressively unlock seller- and reviewer-supplied data -- service histories, third-party test reports, bills of materials, audited sales and support metrics -- paid for a la carte under a freemium model, with reviewer trust scored reputationally. We sketch the architecture of such a market and argue that it rewards genuine product quality and yields truer competition than ranking-based storefronts. We then translate the vision into concrete NLP problems -- cost-optimal information acquisition, data pricing and negotiation, real-time entity resolution, grounded value exchange, and privacy-preserving persona modelling -- and argue that these, not chat fluency, deserve the field's attention.

Related papers

• Learning to Price with Persuasion [32.1862024428227]
  We consider the economic setting recently introduced by Bergemann et al. (2022), where buyers and sellers know different types of information.<n>We show that the seller offers a new perspective on asymmetric economic settings where buyers and sellers know different types of information.
  arXiv  Detail & Related papers  (2026-08-17T15:17:07Z)
• Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce [83.90449137361595]
  Vibe commerce allows people to express buying or selling goals in natural language and delegate tasks to AI agents.<n>We introduce Agentic Commerce World (ACWorld), an environment for evaluating such agents across ongoing transactions.<n>ACWorld validates agent actions before updating shared transaction state and records the resulting interactions, making agent behavior auditable and evaluation reproducible.
  arXiv  Detail & Related papers  (2026-08-03T16:20:17Z)
• Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent [20.0134260493017]
  We introduce a multi-turn evaluation framework for measuring the bargaining ability of seller agents in e-commerce dialogues.<n>Our contributions are: (1) a large-scale e-commerce bargaining benchmark spanning 622 categories, 9,892 products, and 3,014 tasks; (2) a turn-level evaluation framework grounded in Theory of Mind with annotated buyer intents, moving beyond outcome-only metrics; and (3) an automated pipeline that extracts reliable intent from massive dialogue data.
  arXiv  Detail & Related papers  (2025-09-08T05:12:03Z)
• Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives [0.3069335774032178]
  We propose a transaction framework based on the federated learning architecture, and design a seller selection algorithm and incentive compensation mechanism. Specifically, we use gradient similarity and Shapley algorithm to fairly and accurately evaluate the contribution of sellers. After the training, fair compensation is made according to the seller's participation in the training.
  arXiv  Detail & Related papers  (2024-10-10T03:50:20Z)
• IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce [71.37481473399559]
  In this paper, we present IntentionQA, a benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. IntentionQA consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline. Human evaluations demonstrate the high quality and low false-negative rate of our benchmark.
  arXiv  Detail & Related papers  (2024-06-14T16:51:21Z)
• Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
  We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
  arXiv  Detail & Related papers  (2024-03-21T14:48:37Z)
• Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method [17.388837360641276]
  This paper describes the Bargaining task as an asymmetric incomplete information game. It allows us to quantitatively assess an agent's performance in the Bargain task. We propose a novel approach called OG-Narrator that integrates a deterministic Offer Generator to control the price range of Buyer's offers.
  arXiv  Detail & Related papers  (2024-02-24T13:36:58Z)
• Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems [78.84530426424838]
  Existing conversational recommender systems often overlook users' lack of background knowledge, focusing solely on gathering preferences. We introduce SalesOps, a framework that facilitates the simulation and evaluation of such systems. We build SalesBot and ShopperBot, a pair of LLM-powered agents that can simulate either side of the framework.
  arXiv  Detail & Related papers  (2023-10-26T19:44:06Z)
• Federated Learning Incentive Mechanism under Buyers' Auction Market [2.316580879469592]
  Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners. We adapt the procurement auction framework, aiming to explain the pricing behavior under buyers' market. In order to select clients with high reliability and data quality, and to prevent from external attacks, we utilize a blockchain-based reputation mechanism.
  arXiv  Detail & Related papers  (2023-09-10T16:09:02Z)
• E-Commerce Dispute Resolution Prediction [69.84319333335935]
  We take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy.
  arXiv  Detail & Related papers  (2021-10-13T09:45:06Z)
• OSOUM Framework for Trading Data Research [79.0383470835073]
  We supply, to the best of our knowledge, the first open source simulation platform, Open SOUrce Market Simulator (OSOUM) to analyze trading markets and specifically data markets. We describe and implement a specific data market model, consisting of two types of agents: sellers who own various datasets available for acquisition, and buyers searching for relevant and beneficial datasets for purchase. Although commercial frameworks, intended for handling data markets, already exist, we provide a free and extensive end-to-end research tool for simulating possible behavior for both buyers and sellers participating in (data) markets.
  arXiv  Detail & Related papers  (2021-02-18T09:20:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.