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# awesome-agentic-commerce

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](http://makeapullrequest.com)
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A curated list of resources on **Agentic Commerce (A-Commerce)**, covering agentic e-commerce, multi-agent market and commerce systems, conversational commerce, autonomous negotiation and trading, recommendation/search agents, pricing agents, supply chain and logistics agents, and payment agents.

This repository complements the survey:

> **Agentic Commerce: A Survey of How AI Agents Are Reshaping Commerce**  
> [TechRxiv Preprint](https://d197for5662m48.cloudfront.net/documents/publicationstatus/303971/preprint_pdf/6e6fa272238a6481c4ef277f508aa97d.pdf)

```bibtex
@article{zhang2026agentic-commerce,
  title={Agentic Commerce: A Survey of How AI Agents Are Reshaping Commerce},
  author={Zhang, Yifei and Pan, Bo and Zhu, Mengdan and Pei, Jian and Zhao, Liang},
  journal={TechRxiv},
  year={2026},
  doi={10.36227/techrxiv.176972193.39211542/v1},
  url={https://www.techrxiv.org/doi/full/10.36227/techrxiv.176972193.39211542/v1}
}
```

[![From Traditional Commerce to E-Commerce to Agentic Commerce](https://raw.githubusercontent.com/yifeizhangcs/awesome-agentic-commerce/main/assets/a-commerce.png)](assets/a-commerce.pdf)

## Industry Reports & Whitepapers

* [Boston Consulting Group 2025] Agentic Commerce Is Redefining Retail — Here's How to Respond. [link](https://www.bcg.com/publications/2025/agentic-commerce-redefining-retail-how-to-respond)
* [McKinsey & Company 2025] The Agentic Commerce Opportunity: How AI Agents Are Ushering in a New Era for Consumers and Merchants. [link](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchants)
* [Mastercard 2025] What is agentic commerce? Your guide to AI-assisted retail. [link](https://www.mastercard.com/global/en/news-and-trends/stories/2025/agentic-commerce-explainer.html)
* [Visa 2025] What Is Agentic Commerce? [link](https://corporate.visa.com/en/sites/visa-perspectives/innovation/what-is-agentic-commerce.html)
* [Google Cloud 2025] Agentic commerce is here: How retailers can prepare for the new shopping era [link](https://cloud.google.com/transform/agentic-commerce-retailers-can-prepare-for-the-new-shopping-era-ai)
* [Shopify 2025] AI Agents: Harnessing Agentic AI for Ecommerce Businesses [link](https://www.shopify.com/blog/ai-agents)
---

## Part I: Commerce Pipeline (Survey Taxonomy)

*Organization: **Stage** → **Agent Role** (as defined in the survey).*  
*Lifecycle stages (§2): Awareness → Discovery → Engagement → Decision → Transaction → Fulfillment → Service.*

> **Classification policy:** The stage is the primary point at which an agent changes a commerce outcome; the role is the principal whose objectives and authority the agent serves. Part I includes work that implements, trains, or directly evaluates an agent acting at a specific stage. Protocols, non-agent benchmarks, ecosystem measurements, and surveys are not forced into an agent role and are organized in Parts II–III instead. A work may appear once in the lifecycle view and again in a complementary thematic or evaluation view.

[![Agentic Commerce pipeline and agent roles across lifecycle stages](https://raw.githubusercontent.com/yifeizhangcs/awesome-agentic-commerce/main/assets/pipeline.png)](assets/pipeline.pdf)

### Awareness

> **Stage focus:** pre-intent exposure and demand formation (who gets seen, when, and under what constraints)

#### Merchant-Agent

* [KDD 2023] NEON: Living Needs Prediction System in Meituan. [link](https://arxiv.org/pdf/2307.16644)
* [ACL Findings 2025] PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence. [link](https://aclanthology.org/2025.findings-acl.300/)
* [KDD 2026] PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective. [link](https://arxiv.org/abs/2607.27265)

#### Consumer-Agent

*Largely unexplored today; a promising future direction highlighted by the survey (§3.1).*
---

### Discovery

> **Stage focus:** intent-grounded exploration via search and recommendation (finding plausible candidates, not committing)

#### Consumer-Agent

* [NeurIPS 2022] WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents. [link](https://arxiv.org/pdf/2207.01206)
* [ACL Findings 2025] iAgent: LLM Agent as a Shield between User and Recommender Systems. [link](https://aclanthology.org/2025.findings-acl.928/)
* [REALM 2025] PAARS: Persona Aligned Agentic Retail Shoppers. [link](https://aclanthology.org/2025.realm-1.11.pdf)
* [AAAI 2026] ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents. [link](https://ojs.aaai.org/index.php/AAAI/article/view/40640)
* [ACL Industry 2026] ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation. [link](https://aclanthology.org/2026.acl-industry.96/)
* [arXiv 2026] Shopping Companion: A Memory-Augmented LLM Agent for Real-World E-Commerce Tasks. [link](https://arxiv.org/pdf/2603.14864)
* [arXiv 2026] SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents. [link](https://arxiv.org/abs/2605.14205)
* [arXiv 2026] RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents. [link](https://arxiv.org/abs/2605.18805)

#### Platform-Agent

* [WWW 2024] AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems. [link](https://doi.org/10.1145/3589334.3645537)
* [SIGIR 2024] Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning. [link](https://dl.acm.org/doi/abs/10.1145/3626772.3657828)
* [SIGIR 2024] MACRec: A Multi-Agent Collaboration Framework for Recommendation. [link](https://dl.acm.org/doi/abs/10.1145/3626772.3657669)
* [SIGIR 2024] On Generative Agents in Recommendation. [link](https://dl.acm.org/doi/pdf/10.1145/3626772.3657844)
* [NAACL Findings 2024] RecMind: Large Language Model Powered Agent For Recommendation. [link](https://aclanthology.org/2024.findings-naacl.271/)
* [SIGIR 2025] Agentic Feedback Loop Modeling Improves Recommendation and User Simulation. [link](https://doi.org/10.1145/3726302.3729893)
* [NeurIPS 2025 Datasets & Benchmarks] AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems. [link](https://proceedings.neurips.cc/paper_files/paper/2025/file/e2d6f7249add096e26679eade1b4cc6f-Paper-Datasets_and_Benchmarks_Track.pdf)
* [arXiv 2025] ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation. [link](https://arxiv.org/pdf/2506.21931)
* [arXiv 2025] Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search. [link](https://arxiv.org/pdf/2510.20567)
* [arXiv 2025] CARTS: Collaborative Agents for Recommendation Textual Summarization. [link](https://arxiv.org/pdf/2506.17765)
* [WSDM 2025] Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement Learning. [link](https://dl.acm.org/doi/epdf/10.1145/3627673.3680109)
* [arXiv 2025] Enterprise Deep Research: Steerable MultiAgent Deep Research for Enterprise Analytics. [link](https://arxiv.org/pdf/2510.17797)
* [KDD 2025] LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan. [link](https://doi.org/10.1145/3711896.3737196)
* [KDD 2026] LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services. [link](https://doi.org/10.1145/3770855.3817466)
* [arXiv 2025] LORE: A Large Generative Model for Search Relevance. [link](https://arxiv.org/pdf/2512.03025)
* [WWW 2025] LREF: A Novel LLM-based Relevance Framework for E-commerce Search. [link](https://dl.acm.org/doi/pdf/10.1145/3701716.3715246)
* [KDD 2026] Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent. [link](https://doi.org/10.1145/3770854.3780286)
* [TMLR 2025] Rec-R1: Bridging generative large language models and user-centric recommendation systems via reinforcement learning. [link](https://openreview.net/forum?id=YBRU9MV2vE)
* [ACL Industry 2025] User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems. [link](https://aclanthology.org/2025.acl-industry.70.pdf)
* [arXiv 2026] Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model. [link](https://arxiv.org/pdf/2604.14051)
* [arXiv 2026] QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation. [link](https://arxiv.org/abs/2606.05671)
* [arXiv 2026] SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents. [link](https://arxiv.org/abs/2606.28356)
* [arXiv 2026] $\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems. [link](https://arxiv.org/abs/2606.10156)
* [arXiv 2026] Iterating Toward Better Search: A Two-Agent Simulation Framework for Evaluating Agentic Search Architectures in E-Commerce. [link](https://arxiv.org/abs/2606.12924)
* [arXiv 2026] ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping. [link](https://arxiv.org/abs/2606.31693)
---

### Engagement

> **Stage focus:** interactive preference elicitation, persuasion, assistance, or negotiation before a final commitment

#### Consumer-Agent

* [arXiv 2024] ChatShop: Interactive information seeking with language agents. [link](https://arxiv.org/pdf/2404.09911)
* [ACL Findings 2024] Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method. [link](https://aclanthology.org/2024.findings-acl.213/)
* [PNAS 2026] Advancing AI Negotiations: A Large-Scale Autonomous Negotiation Competition. [link](https://doi.org/10.1073/pnas.2521774123)
* [NLLP 2025] The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets. [link](https://aclanthology.org/2025.nllp-1.2.pdf)
* [arXiv 2026] AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions. [link](https://arxiv.org/abs/2602.06008)

#### Merchant-Agent

* [arXiv 2025] AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing. [link](https://www.arxiv.org/pdf/2511.12133)
* [EMNLP 2025] ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization. [link](https://aclanthology.org/2025.emnlp-main.821.pdf)
* [IWSDS 2025] Exploring Personality-Aware Interactions in Salesperson Dialogue Agents. [link](https://aclanthology.org/2025.iwsds-1.6.pdf)
* [WWW Companion 2025] FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers. [link](https://doi.org/10.1145/3701716.3715176)
* [arXiv 2025] SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization. [link](https://arxiv.org/pdf/2503.23303)
* [EMNLP Findings 2025] Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action. [link](https://aclanthology.org/2025.findings-emnlp.275.pdf)
* [CVPR 2026 HiGen Workshop] VerbalValue: A Socially Intelligent Virtual Host for Sales-Driven Live Commerce. [link](https://higen-2025.github.io/index.html)

#### Platform-Agent

* [KDD 2025] DiMA: An LLM-Powered Ride-Hailing Assistant at DiDi. [link](https://doi.org/10.1145/3711896.3737208)
* [ACM Transactions on Information Systems 2025] Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations. [link](https://arxiv.org/pdf/2308.16505)
* [ACL Findings 2025] Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent. [link](https://aclanthology.org/2025.findings-acl.307/)
* [arXiv 2025] Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agents. [link](https://arxiv.org/abs/2503.04830)

---

### Decision

> **Stage focus:** evaluating or comparing candidates and making a final selection or commitment

#### Consumer-Agent

* [ACL 2025] Personal Travel Solver: A Preference-Driven LLM-Solver System for Travel Planning. [link](https://aclanthology.org/2025.acl-long.1339.pdf)
* [Amazon 2024] Help Me Decide: AI-powered product comparison on Amazon. [link](https://www.aboutamazon.com/news/retail/amazon-things-to-buy-help-me-decide-gen-ai)
* [arXiv 2026] EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent. [link](https://arxiv.org/abs/2606.17698)

---

### Transaction

> **Stage focus:** authorization, payment, clearing, settlement, and execution of an agreed transaction

#### Payment-Agent

* [IEEE BigData 2025] CASE: An Agentic AI Framework for Enhancing Scam Intelligence in Digital Payments. [link](https://doi.org/10.1109/BigData66926.2025.11402424)
* [GitHub] Zen7 Payment Agent: A Dedicated Payment Network. [link](https://github.com/Zen7-Labs/Zen7-Payment-Agent)

---

### Fulfillment

> **Stage focus:** inventory, supply-chain, warehousing, logistics, and physical delivery after a decision or transaction

#### Supply-Side Agent

* [IROS 2023] L3MVN: Leveraging Large Language Models for Visual Target Navigation. [link](https://doi.org/10.1109/IROS55552.2023.10342512)
* [CoRL 2023] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. [link](https://arxiv.org/pdf/2307.15818)
* [RSS 2024] RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots. [link](https://rpl.cs.utexas.edu/publications/2024/07/15/nasiriany-rss24-robocasa/)
* [arXiv 2024] Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking. [link](https://arxiv.org/abs/2411.10184)
* [ICBDT 2025] Agentic AI Framework for Smart Inventory Replenishment. [link](https://www.arxiv.org/pdf/2511.23366)
* [arXiv 2025] AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager. [link](https://arxiv.org/abs/2508.11416)
* [arXiv 2025] Leveraging LLM-Based Agents for Intelligent Supply Chain Planning. [link](https://arxiv.org/pdf/2509.03811)
* [CoRL 2025] Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs. [link](https://proceedings.mlr.press/v270/xu25b.html)
* [ICRA 2025] Physics-Aware Robotic Palletization with Online Masking Inference. [link](https://doi.org/10.1109/ICRA55743.2025.11128204)
* [Sustainability 2025] Research on Composite Robot Scheduling and Task Allocation for Warehouse Logistics Systems. [link](https://www.mdpi.com/2071-1050/17/11/5051)
* [ICCV 2025] RoboFactory: Exploring Embodied Agent Collaboration with Compositional Constraints. [link](https://openaccess.thecvf.com/content/ICCV2025/html/Qin_RoboFactory_Exploring_Embodied_Agent_Collaboration_with_Compositional_Constraints_ICCV_2025_paper.html)
* [IEEE CASE 2025] Safe Human Robot Navigation in Warehouse Scenario. [link](https://doi.org/10.1109/CASE58245.2025.11164151)
* [ICRA Workshop 2025] Task Planning for Mobile Manipulation in Retail Stores using Foundation Models with Iterative Re-planning. [link](https://dyalab.mines.edu/2025/icra-workshop/18.pdf)

---

### Service

> **Stage focus:** post-purchase support, issue resolution, retention, and the ongoing customer relationship

#### Consumer-Agent

*No canonical benchmark/paper list yet in the survey; contributions welcome.*

#### Platform-Agent

* [ACL Industry 2025] A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs. [link](https://aclanthology.org/2025.acl-industry.107/)
* [ACL Findings 2025] ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration. [link](https://aclanthology.org/2025.findings-acl.617/)
* [EMNLP Industry 2025] ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? [link](https://aclanthology.org/2025.emnlp-industry.19/)
* [arXiv 2025] Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems. [link](https://arxiv.org/pdf/2510.13291)
* [arXiv 2025] MindFlow+: A Self-Evolving Agent for E-Commerce Customer Service. [link](https://arxiv.org/pdf/2507.18884)

---

## Part II: Applications, Infrastructure & Cross-Cutting Research

> **Foundations curation note:** The five compact sections below provide only the historical and technical backbone needed to connect recent Agentic Commerce with established MAS research. Recent Agentic AI and commerce systems remain the main emphasis. Peer-reviewed research, books, and standards are labeled separately; market simulations are not treated as delegated commerce agents.

### Historical Foundations — Agent Theory & Coordination

* [IEEE Transactions on Computers 1980] The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. [link](https://doi.org/10.1109/TC.1980.1675516)
* [The Knowledge Engineering Review 1995] Intelligent Agents: Theory and Practice. [link](https://doi.org/10.1017/S0269888900008122)
* [ICMAS 1995] BDI Agents: From Theory to Practice. [link](https://aaai.org/papers/icmas95-042-bdi-agents-from-theory-to-practice/)
* [Artificial Intelligence 2000] On Agent-Based Software Engineering. [link](https://doi.org/10.1016/S0004-3702(99)00107-1)
* [The Knowledge Engineering Review 2004] A Survey of Multi-Agent Organizational Paradigms. [link](https://doi.org/10.1017/S0269888905000317)
* [Book 2009] Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. [link](https://www.masfoundations.org/)

### Historical Foundations — Communication, Negotiation & Institutions

* [CIKM 1994] KQML as an Agent Communication Language. [link](https://doi.org/10.1145/191246.191322)
* [Robotics and Autonomous Systems 1998] Negotiation Decision Functions for Autonomous Agents. [link](https://doi.org/10.1016/S0921-8890(98)00029-3)
* [Group Decision and Negotiation 2001] Automated Negotiation: Prospects, Methods and Challenges. [link](https://doi.org/10.1023/A:1008746126376)
* [FIPA Standard 2002] FIPA ACL Message Structure Specification. [link](https://www.fipa.org/specs/fipa00061/SC00061G.html)
* [AAMAS 2002] Flexible Protocol Specification and Execution: Applying Event Calculus Planning Using Commitments. [link](https://doi.org/10.1145/544862.544867)
* [Engineering Applications of Artificial Intelligence 2005] Engineering Open Environments with Electronic Institutions. [link](https://doi.org/10.1016/j.engappai.2004.11.019)
* [IEEE Computer 2009] Commitment-Based Service-Oriented Architecture. [link](https://doi.org/10.1109/MC.2009.347)
* [AAMAS 2009] Multiagent Commitment Alignment. [link](https://doi.org/10.1145/1558109.1558143)

### Historical Foundations — Trust & Human-Agent Delegation

* [CHI 1999] Principles of Mixed-Initiative User Interfaces. [link](https://doi.org/10.1145/302979.303030)
* [Computational Intelligence 2002] Distributed Reputation Management for Electronic Commerce. [link](https://doi.org/10.1111/1467-8640.00202)
* [Autonomous Agents and Multi-Agent Systems 2006] An Integrated Trust and Reputation Model for Open Multi-Agent Systems. [link](https://doi.org/10.1007/s10458-005-6825-4)
* [Human Factors 2007] Designing for Flexible Interaction Between Humans and Automation: Delegation Interfaces for Supervisory Control. [link](https://doi.org/10.1518/001872007779598037)
* [The Knowledge Engineering Review 2007] On Agent Technology for E-Commerce: Trust, Security and Legal Issues. [link](https://doi.org/10.1017/S0269888907001014)

### Historical Foundations — Agent-Mediated Electronic Commerce

* [The Knowledge Engineering Review 1998] Agent-Mediated Electronic Commerce: A Survey. [link](https://doi.org/10.1017/S0269888998002082)
* [Agents 1998] The Michigan Internet AuctionBot: A Configurable Auction Server for Human and Software Agents. [link](https://strategicreasoning.org/publications/1998/the-michigan-internet-auctionbot-a-configurable-auction-server-for-human-and-software-agents/)
* [Communications of the ACM 1999] Agents That Buy and Sell. [link](https://doi.org/10.1145/295685.295716)
* [LNAI 2001] Agent-Mediated Electronic Commerce: Scientific and Technological Roadmap. [link](https://doi.org/10.1007/3-540-44682-6_1)
* [IEEE Transactions on Knowledge and Data Engineering 2003] On Agent-Mediated Electronic Commerce. [link](https://doi.org/10.1109/TKDE.2003.1209014)

### Technical Foundations — Learning, Markets & LLM-MAS Bridge

* [JAIR 1993] A Market-Oriented Programming Environment and Its Application to Distributed Multicommodity Flow Problems. [link](https://strategicreasoning.org/publications/1993/a-market-oriented-programming-environment-and-its-application-to-distributed-multicommodity-flow-problems/)
* [Handbook of Computational Economics 2006] Agent-Based Computational Economics: A Constructive Approach to Economic Theory. [link](https://doi.org/10.1016/S1574-0021(05)02016-2)
* [IEEE Transactions on Systems, Man, and Cybernetics, Part C 2008] A Comprehensive Survey of Multiagent Reinforcement Learning. [link](https://doi.org/10.1109/TSMCC.2007.913919)
* [American Economic Review 2020] Artificial Intelligence, Algorithmic Pricing, and Collusion. [link](https://doi.org/10.1257/aer.20190623)
* [IJCAI 2024 Survey Track] Large Language Model Based Multi-agents: A Survey of Progress and Challenges. [link](https://doi.org/10.24963/ijcai.2024/890)
* [AAMAS 2025] Learning Collusion in Episodic, Inventory-Constrained Markets. [link](https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p803.pdf)

### Retail Shopping & Personal Assistants

* [OpenAI] Shopping Research in ChatGPT. [link](https://openai.com)
* [OpenAI] Buy it in ChatGPT. [link](https://developers.openai.com)
* [Perplexity] Shop like a Pro. [link](https://perplexity.ai)
* [Amazon] Rufus: AI Shopping Assistant. [link](https://aws.amazon.com/solutions/amazon/one-amazon-lane/shopping-tools/)
* [Amazon 2025] Buy for Me. [link](https://www.aboutamazon.com/news/retail/amazon-shopping-app-buy-for-me-brands)
* [NeurIPS 2022] WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents. [link](https://arxiv.org/pdf/2207.01206)
* [arXiv 2023] Intelligent Virtual Assistants with LLM-based Process Automation. [link](https://arxiv.org/pdf/2312.06677)
* [arXiv 2024] TravelAgent: An AI Assistant for Personalized Travel Planning. [link](https://arxiv.org/pdf/2409.08069)
* [CVPR 2024] Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment. [link](https://arxiv.org/pdf/2403.12965)
* [ICML 2025] Position: Build Agent Advocates, Not Platform Agents. [link](https://proceedings.mlr.press/v267/kapoor25a.html)
* [KDD 2026] DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents. [link](https://arxiv.org/pdf/2509.21842)
* [arXiv 2025] FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization. [link](https://arxiv.org/pdf/2507.13311)
* [arXiv 2025] Flippi: End To End GenAI Assistant for E-Commerce. [link](https://arxiv.org/pdf/2507.05788)
* [REALM 2025] PAARS: Persona Aligned Agentic Retail Shoppers. [link](https://aclanthology.org/2025.realm-1.11.pdf)
* [ACL 2025] Personal Travel Solver: A Preference-Driven LLM-Solver System for Travel Planning. [link](https://aclanthology.org/2025.acl-long.1339.pdf)
* [EMNLP 2025] RETAIL: Towards Real-world Travel Planning for Large Language Models. [link](https://aclanthology.org/2025.emnlp-main.752.pdf)
* [arXiv 2025] What Is Your AI Agent Buying? Evaluation, Implications, and Emerging Questions for Agentic E-Commerce. [link](https://arxiv.org/pdf/2508.02630)
* [SIGIR 2026] WebMall - A Multi-Shop Benchmark for Evaluating Web Agents. [link](https://doi.org/10.1145/3805712.3808592)
* [ACL Industry 2026] ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation. [link](https://aclanthology.org/2026.acl-industry.96/)
* [arXiv 2026] Shopping Companion: A Memory-Augmented LLM Agent for Real-World E-Commerce Tasks. [link](https://arxiv.org/pdf/2603.14864)
* [arXiv 2026] EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent. [link](https://arxiv.org/abs/2606.17698)
* [arXiv 2026] ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping. [link](https://arxiv.org/abs/2606.31693)

### Marketplaces (C2C / P2P)

* [arXiv 2025] FaMA: LLM-Empowered Agentic Assistant for Consumer-to-Consumer Marketplace. [link](https://arxiv.org/pdf/2509.03890v1)
* [WWW Companion 2025] FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers. [link](https://doi.org/10.1145/3701716.3715176)
* [NLLP 2025] The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets. [link](https://aclanthology.org/2025.nllp-1.2.pdf)

### Advertising, Bidding & Merchant Growth

* [TKDE 2024] Hierarchical Multi-Agent Meta-Reinforcement Learning for Cross-Channel Bidding. [link](https://ieeexplore.ieee.org/document/10817487/)
* [EMNLP Industry 2024] IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing. [link](https://aclanthology.org/2024.emnlp-industry.52.pdf)
* [ICLR 2025 FM-Wild Workshop] Agentic Multimodal AI for Hyper-Personalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework. [link](https://openreview.net/forum?id=4qzqY0PFRK)
* [arXiv 2025] CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design. [link](https://arxiv.org/pdf/2506.21934)
* [SIGIR 2025] Insight Agents: An LLM-Based Multi-Agent System for Data Insights. [link](https://arxiv.org/pdf/2601.20048)
* [WWW Companion 2025] RTBAgent: A LLM-based Agent System for Real-Time Bidding. [link](https://doi.org/10.1145/3701716.3715259)
* [CVPR 2026] E-comIQ-ZH: A Human-Aligned Dataset and Benchmark for Fine-Grained Evaluation of E-commerce Posters with Chain-of-Thought. [link](https://arxiv.org/pdf/2602.21698)
* [ICML 2026] DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation. [link](https://arxiv.org/abs/2606.14192)
* [KDD 2026] PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective. [link](https://arxiv.org/abs/2607.27265)

### Sales & Customer Service

* [Amazon] Seller Assistant. [link](https://aws.amazon.com/solutions/amazon/one-amazon-lane/home-office/)
* [ACL Industry 2025] A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs. [link](https://aclanthology.org/2025.acl-industry.107/)
* [ACL Findings 2025] ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration. [link](https://aclanthology.org/2025.findings-acl.617/)
* [arXiv 2025] AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing. [link](https://www.arxiv.org/pdf/2511.12133)
* [IWSDS 2025] Exploring Personality-Aware Interactions in Salesperson Dialogue Agents. [link](https://aclanthology.org/2025.iwsds-1.6.pdf)
* [arXiv 2025] MindFlow+: A Self-Evolving Agent for E-Commerce Customer Service. [link](https://arxiv.org/pdf/2507.18884)
* [arXiv 2025] SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization. [link](https://arxiv.org/pdf/2503.23303)
* [EMNLP Findings 2025] Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action. [link](https://aclanthology.org/2025.findings-emnlp.275.pdf)
* [arXiv 2025] Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agents. [link](https://arxiv.org/abs/2503.04830)
* [CVPR 2026 HiGen Workshop] VerbalValue: A Socially Intelligent Virtual Host for Sales-Driven Live Commerce. [link](https://higen-2025.github.io/index.html)

### Recommendation & Search Systems

* [KDD 2023] NEON: Living Needs Prediction System in Meituan. [link](https://arxiv.org/pdf/2307.16644)
* [WWW 2024] AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems. [link](https://doi.org/10.1145/3589334.3645537)
* [SIGIR 2024] Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning. [link](https://dl.acm.org/doi/abs/10.1145/3626772.3657828)
* [SIGIR 2024] MACRec: A Multi-Agent Collaboration Framework for Recommendation. [link](https://dl.acm.org/doi/abs/10.1145/3626772.3657669)
* [SIGIR 2024] On Generative Agents in Recommendation. [link](https://dl.acm.org/doi/pdf/10.1145/3626772.3657844)
* [NAACL Findings 2024] RecMind: Large Language Model Powered Agent For Recommendation. [link](https://aclanthology.org/2024.findings-naacl.271/)
* [NeurIPS 2025 Datasets & Benchmarks] AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems. [link](https://proceedings.neurips.cc/paper_files/paper/2025/file/e2d6f7249add096e26679eade1b4cc6f-Paper-Datasets_and_Benchmarks_Track.pdf)
* [arXiv 2025] ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation. [link](https://arxiv.org/pdf/2506.21931)
* [arXiv 2025] Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search. [link](https://arxiv.org/pdf/2510.20567)
* [arXiv 2025] CARTS: Collaborative Agents for Recommendation Textual Summarization. [link](https://arxiv.org/pdf/2506.17765)
* [WSDM 2025] Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement Learning. [link](https://dl.acm.org/doi/epdf/10.1145/3627673.3680109)
* [arXiv 2025] Enterprise Deep Research: Steerable MultiAgent Deep Research for Enterprise Analytics. [link](https://arxiv.org/pdf/2510.17797)
* [arXiv 2025] Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems. [link](https://arxiv.org/pdf/2510.13291)
* [KDD 2025] LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan. [link](https://doi.org/10.1145/3711896.3737196)
* [KDD 2026] LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services. [link](https://doi.org/10.1145/3770855.3817466)
* [arXiv 2025] LORE: A Large Generative Model for Search Relevance. [link](https://arxiv.org/pdf/2512.03025)
* [WWW 2025] LREF: A Novel LLM-based Relevance Framework for E-commerce Search. [link](https://dl.acm.org/doi/pdf/10.1145/3701716.3715246)
* [ACM Transactions on Information Systems 2025] Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations. [link](https://arxiv.org/pdf/2308.16505)
* [ACL Findings 2025] Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent. [link](https://aclanthology.org/2025.findings-acl.307/)
* [KDD 2026] Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent. [link](https://doi.org/10.1145/3770854.3780286)
* [TMLR 2025] Rec-R1: Bridging generative large language models and user-centric recommendation systems via reinforcement learning. [link](https://openreview.net/forum?id=YBRU9MV2vE)
* [arXiv 2025] The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems. [link](https://arxiv.org/abs/2507.02097)
* [ACL Industry 2025] User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems. [link](https://aclanthology.org/2025.acl-industry.70.pdf)
* [arXiv 2026] Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model. [link](https://arxiv.org/pdf/2604.14051)
* [arXiv 2026] RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents. [link](https://arxiv.org/abs/2605.18805)
* [arXiv 2026] QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation. [link](https://arxiv.org/abs/2606.05671)
* [arXiv 2026] $\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems. [link](https://arxiv.org/abs/2606.10156)
* [arXiv 2026] Iterating Toward Better Search: A Two-Agent Simulation Framework for Evaluating Agentic Search Architectures in E-Commerce. [link](https://arxiv.org/abs/2606.12924)
* [arXiv 2026] ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping. [link](https://arxiv.org/abs/2606.31693)
* [arXiv 2026] RecGPT-V3 Technical Report. [link](https://arxiv.org/abs/2607.15591)

### Platform Services & Enterprise Solutions

* [OpenAI] Operator. [link](https://openai.com/index/introducing-operator/)
* [Adobe] Adobe Experience Platform Agent Orchestrator. [link](https://business.adobe.com)
* [Salesforce] Artificial Intelligence (AI) at Salesforce. [link](https://salesforce.com)
* [ACL Industry 2026] Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations. [link](https://aclanthology.org/2026.acl-industry.29/)

### Mobility & On-demand Services

* [Transportation Research Part C 2025] Agentic Large Language Models for day-to-day route choices (LLMTraveler). [link](https://www.sciencedirect.com/science/article/pii/S0968090X25003110)
* [KDD 2025] DiMA: An LLM-Powered Ride-Hailing Assistant at DiDi. [link](https://doi.org/10.1145/3711896.3737208)
* [IEEE TASE 2026] RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations. [link](https://doi.org/10.1109/TASE.2026.3685621)

### Unmanned Stores & Autonomous Retail

* [Wikipedia] Amazon Go. [link](https://en.wikipedia.org/wiki/Amazon_Go)
* [Nature Scientific Reports 2024] Smart customer service in unmanned retail store enhanced by large language model. [link](https://www.nature.com/articles/s41598-024-71089-9)
* [arXiv 2025] A Survey of Challenges and Sensing Technologies in Autonomous Retail Systems. [link](https://arxiv.org/pdf/2503.07997)

### Intelligent Cockpit & In-Vehicle Commerce

* [SoundHound 2025] CES 2025: SoundHound AI Debuts Its First Ever In-Vehicle Voice Assistant With On-The-Go Food Ordering. [link](https://www.soundhound.com/newsroom/press-releases/ces-2025-soundhound-ai-debuts-its-first-ever-in-vehicle-voice-assistant-with-on-the-go-food-ordering)
* [Alibaba Cloud 2025] Alibaba Unveils Intelligent Cockpits, Enterprise Partnerships and AI Glasses at WAIC 2025. [link](https://www.alibabacloud.com/blog/alibaba-unveils-intelligent-cockpits-enterprise-partnerships-and-ai-glasses-at-waic-2025_602409)

### Supply Chain, Warehousing & Embodied Commerce

* [IROS 2023] L3MVN: Leveraging Large Language Models for Visual Target Navigation. [link](https://doi.org/10.1109/IROS55552.2023.10342512)
* [CoRL 2023] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. [link](https://arxiv.org/pdf/2307.15818)
* [IROS 2025] AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environments. [link](https://doi.org/10.1109/IROS60139.2025.11246901)
* [RSS 2024] RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots. [link](https://rpl.cs.utexas.edu/publications/2024/07/15/nasiriany-rss24-robocasa/)
* [arXiv 2024] Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking. [link](https://arxiv.org/abs/2411.10184)
* [ICBDT 2025] Agentic AI Framework for Smart Inventory Replenishment. [link](https://www.arxiv.org/pdf/2511.23366)
* [arXiv 2025] AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager. [link](https://arxiv.org/abs/2508.11416)
* [NeurIPS 2025 Datasets & Benchmarks] IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios. [link](https://papers.neurips.cc/paper_files/paper/2025/hash/941de7aa5976f372117725abd87c639a-Abstract-Datasets_and_Benchmarks_Track.html)
* [arXiv 2025] Leveraging LLM-Based Agents for Intelligent Supply Chain Planning. [link](https://arxiv.org/pdf/2509.03811)
* [CoRL 2025] Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs. [link](https://proceedings.mlr.press/v270/xu25b.html)
* [arXiv 2025] Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions. [link](https://arxiv.org/pdf/2507.02698)
* [ICRA 2025] Physics-Aware Robotic Palletization with Online Masking Inference. [link](https://doi.org/10.1109/ICRA55743.2025.11128204)
* [Sustainability 2025] Research on Composite Robot Scheduling and Task Allocation for Warehouse Logistics Systems. [link](https://www.mdpi.com/2071-1050/17/11/5051)
* [ICCV 2025] RoboFactory: Exploring Embodied Agent Collaboration with Compositional Constraints. [link](https://openaccess.thecvf.com/content/ICCV2025/html/Qin_RoboFactory_Exploring_Embodied_Agent_Collaboration_with_Compositional_Constraints_ICCV_2025_paper.html)
* [IEEE CASE 2025] Safe Human Robot Navigation in Warehouse Scenario. [link](https://doi.org/10.1109/CASE58245.2025.11164151)
* [ICRA Workshop 2025] Task Planning for Mobile Manipulation in Retail Stores using Foundation Models with Iterative Re-planning. [link](https://dyalab.mines.edu/2025/icra-workshop/18.pdf)
* [Amazon] Amazon has more than 1 million robots that sort, lift, and carry packages—see them in action. [link](https://www.aboutamazon.com/news/operations/amazon-robotics-robots-fulfillment-center)

### Negotiation & Pricing

* [ACL Findings 2024] Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method. [link](https://aclanthology.org/2024.findings-acl.213/)
* [ICML 2024] CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents. [link](https://proceedings.mlr.press/v235/zhao24q.html)
* [ICML 2024] How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis. [link](https://proceedings.mlr.press/v235/bianchi24a.html)
* [AAMAS 2025] Learning Collusion in Episodic, Inventory-Constrained Markets. [link](https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p803.pdf)
* [PNAS 2026] Advancing AI Negotiations: A Large-Scale Autonomous Negotiation Competition. [link](https://doi.org/10.1073/pnas.2521774123)
* [arXiv 2026] AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions. [link](https://arxiv.org/abs/2602.06008)
* [arXiv 2026] Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition. [link](https://arxiv.org/abs/2604.05523)
* [arXiv 2026] TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate. [link](https://arxiv.org/abs/2605.13909)

### Protocols & Standards

* [Model Context Protocol] Model Context Protocol (MCP). [link](https://modelcontextprotocol.io)
* [Agent2Agent Protocol] Agent2Agent (A2A) Protocol. [link](https://a2a-protocol.org)
* [OpenAI] Agentic Commerce Protocol (ACP). [link](https://github.com/openai/agentic-commerce-protocol)
* [W3C 2025] Verifiable Credentials 2.0 family of specifications. [link](https://www.w3.org/news/2025/the-verifiable-credentials-2-0-family-of-specifications-is-now-a-w3c-recommendation/)
* [arXiv 2025] Agent TCP/IP: An Agent-to-Agent Transaction System. [link](https://arxiv.org/pdf/2501.06243)
* [arXiv 2025] Building A Secure Agentic AI Application Leveraging Google's A2A Protocol. [link](https://arxiv.org/pdf/2504.16902)

### Payment & Transaction Protocols

* [Google Cloud] Powering AI commerce with the new Agent Payments Protocol (AP2). [link](https://cloud.google.com/blog/products/ai-machine-learning/announcing-agents-to-payments-ap2-protocol)
* [Skyfire] KYA & Payments for Agents. [link](https://skyfire.xyz/product/)
* [GitHub] MachinePal – x402 AI Payment Agent for Any Website or API. [link](https://github.com/skalenetwork/machinepal)
* [GitHub] Zen7 Payment Agent: A Dedicated Payment Network. [link](https://github.com/Zen7-Labs/Zen7-Payment-Agent)
* [arXiv 2025] Secure Autonomous Agent Payments: Verifying Authenticity and Intent in a Trustless Environment. [link](https://arxiv.org/pdf/2511.15712)
* [arXiv 2026] RAILS: Verification-Native Clearing For Agentic Commerce. [link](https://arxiv.org/abs/2606.08790)

### User Simulation & Behavioral Modeling

* [SIGIR 2025] Agentic Feedback Loop Modeling Improves Recommendation and User Simulation. [link](https://doi.org/10.1145/3726302.3729893)
* [ACL Industry 2025] SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation. [link](https://aclanthology.org/2025.acl-industry.5.pdf)
* [arXiv 2025] LLM Agent Meets Agentic AI: Can LLM Agents Simulate Customers to Evaluate Agentic-AI-based Shopping Assistants? [link](https://arxiv.org/pdf/2509.21501)
* [arXiv 2026] SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents. [link](https://arxiv.org/abs/2605.14205)
* [arXiv 2026] SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators. [link](https://arxiv.org/abs/2605.08334)

### Trust, Safety & Ecosystem Measurement

* [ICLR 2025] Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents. [link](https://proceedings.iclr.cc/paper_files/paper/2025/hash/5750f91d8fb9d5c02bd8ad2c3b44456b-Abstract-Conference.html)
* [ICML 2025] Position: AI Agents Need Authenticated Delegation. [link](https://proceedings.mlr.press/v267/south25a.html)
* [ACL 2025] The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents. [link](https://aclanthology.org/2025.acl-long.1435/)
* [ACL Findings 2025] iAgent: LLM Agent as a Shield between User and Recommender Systems. [link](https://aclanthology.org/2025.findings-acl.928/)
* [IEEE BigData 2025] CASE: An Agentic AI Framework for Enhancing Scam Intelligence in Digital Payments. [link](https://doi.org/10.1109/BigData66926.2025.11402424)
* [ICIS 2025] Market Design Interventions for Safer Agentic AI. [link](https://aisel.aisnet.org/icis2025/gen_ai/gen_ai/30/)
* [ICSE-NIER 2026] Towards Verifiably Safe Tool Use for LLM Agents. [link](https://doi.org/10.1145/3786582.3786839)
* [ACM CAIS 2026] Securing Agents With Tracked Capabilities. [link](https://doi.org/10.1145/3786335.3813127)
* [ACL 2026] Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces. [link](https://aclanthology.org/2026.acl-long.1009/)
* [arXiv 2026] Five Attacks on x402 Agentic Payment Protocol. [link](https://arxiv.org/abs/2605.11781)
* [arXiv 2026] SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents. [link](https://arxiv.org/abs/2606.28356)
* [arXiv 2026] How Agentic Is Agentic Commerce? A Population-Scale Measurement of x402 Adoption and Authenticity. [link](https://arxiv.org/abs/2607.12575)

---

## Part III: Benchmarks, Environments & Evaluation

This section covers benchmarks, datasets, simulation environments, and evaluation frameworks that cut across commerce stages and agent roles.

* [NeurIPS 2022] WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents. [link](https://arxiv.org/pdf/2207.01206)
* [ACL Findings 2024] Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method. [link](https://aclanthology.org/2024.findings-acl.213/)
* [ICML 2024] How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis. [link](https://proceedings.mlr.press/v235/bianchi24a.html)
* [ICLR 2025] Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents. [link](https://proceedings.iclr.cc/paper_files/paper/2025/hash/5750f91d8fb9d5c02bd8ad2c3b44456b-Abstract-Conference.html)
* [NeurIPS 2025 Datasets & Benchmarks] AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems. [link](https://proceedings.neurips.cc/paper_files/paper/2025/file/e2d6f7249add096e26679eade1b4cc6f-Paper-Datasets_and_Benchmarks_Track.pdf)
* [arXiv 2025] AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager. [link](https://arxiv.org/abs/2508.11416)
* [arXiv 2025] $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment. [link](https://arxiv.org/pdf/2506.07982)
* [arXiv 2025] DeepShop: A Benchmark for Deep Research Shopping Agents. [link](https://arxiv.org/pdf/2506.02839)
* [arXiv 2025] EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce. [link](https://arxiv.org/pdf/2512.08868)
* [EMNLP Industry 2025] ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? [link](https://aclanthology.org/2025.emnlp-industry.19/)
* [arXiv 2025] LLM Agent Meets Agentic AI: Can LLM Agents Simulate Customers to Evaluate Agentic-AI-based Shopping Assistants? [link](https://arxiv.org/pdf/2509.21501)
* [KDD 2025] LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan. [link](https://doi.org/10.1145/3711896.3737196)
* [KDD 2026] LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services. [link](https://doi.org/10.1145/3770855.3817466)
* [arXiv 2025] Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets. [link](https://arxiv.org/pdf/2510.25779)
* [arXiv 2025] Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions. [link](https://arxiv.org/pdf/2507.02698)
* [AAAI 2026] ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents. [link](https://ojs.aaai.org/index.php/AAAI/article/view/40640)
* [SIGIR 2026] WebMall - A Multi-Shop Benchmark for Evaluating Web Agents. [link](https://doi.org/10.1145/3805712.3808592)
* [ACL Industry 2025] SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation. [link](https://aclanthology.org/2025.acl-industry.5.pdf)
* [arXiv 2025] What Is Your AI Agent Buying? Evaluation, Implications, and Emerging Questions for Agentic E-Commerce. [link](https://arxiv.org/pdf/2508.02630)
* [ACL 2026] DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints. [link](https://aclanthology.org/2026.acl-long.335/)
* [ACL 2026] A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains. [link](https://aclanthology.org/2026.acl-long.68/)
* [ACL 2026] Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces. [link](https://aclanthology.org/2026.acl-long.1009/)
* [CVPR 2026] E-comIQ-ZH: A Human-Aligned Dataset and Benchmark for Fine-Grained Evaluation of E-commerce Posters with Chain-of-Thought. [link](https://arxiv.org/pdf/2602.21698)
* [KDD 2026] PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective. [link](https://arxiv.org/abs/2607.27265)
* [arXiv 2026] AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions. [link](https://arxiv.org/abs/2602.06008)
* [arXiv 2026] Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition. [link](https://arxiv.org/abs/2604.05523)
* [arXiv 2026] Shopping Companion: A Memory-Augmented LLM Agent for Real-World E-Commerce Tasks. [link](https://arxiv.org/pdf/2603.14864)
* [ICLR 2026] VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications. [link](https://arxiv.org/pdf/2509.26490)
* [arXiv 2026] SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators. [link](https://arxiv.org/abs/2605.08334)
* [arXiv 2026] RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents. [link](https://arxiv.org/abs/2605.18805)
* [arXiv 2026] TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate. [link](https://arxiv.org/abs/2605.13909)
* [arXiv 2026] $\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems. [link](https://arxiv.org/abs/2606.10156)
* [arXiv 2026] EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent. [link](https://arxiv.org/abs/2606.17698)
  
---

## Contributing

Contributions are welcome! Please submit a Pull Request following the format:

- `[Venue Year] Paper Title. [link](url)`

## License

This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).

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