# ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents

arXiv: https://arxiv.org/abs/2508.04266 · HTML: https://mlsanity.com/paper/2508.04266/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 13 of 13)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use](https://mlsanity.com/paper/2310.03128/) (2023)
- [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023)
- [Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models](https://mlsanity.com/paper/2410.20745/) (2024)
- [U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation](https://mlsanity.com/paper/2305.04774/) (2023)
- [EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association](https://mlsanity.com/paper/2505.15196/) (2025)

## Built on by (most cited first, 28 of 28)

- [WebMall -- A Multi-Shop Benchmark for Evaluating Web Agents](https://mlsanity.com/paper/2508.13024/) (2025)
- [ShoppingComp: Are LLMs Really Ready for Your Shopping Cart?](https://mlsanity.com/paper/2511.22978/) (2025)
- [AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping](https://mlsanity.com/paper/2602.12315/) (2026)
- [MCP vs RAG vs NLWeb vs HTML: A Comparison of the Effectiveness and Efficiency of Different Agent Interfaces to the Web (Technical Report)](https://mlsanity.com/paper/2511.23281/) (2025)
- [RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments](https://mlsanity.com/paper/2606.15862/) (2026)
- [MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations](https://mlsanity.com/paper/2607.28956/) (2026)
- [Shopping Companion: Benchmarking and Training LLM Agents for Long-Horizon Preference-Grounded E-Commerce Tasks](https://mlsanity.com/paper/2603.14864/) (2026)
- [ShopGym: An Integrated Framework for Realistic Simulation and Scalable Benchmarking of E-Commerce Web Agents](https://mlsanity.com/paper/2605.16116/) (2026)
- [EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent](https://mlsanity.com/paper/2606.17698/) (2026)
- [SimGym: Traffic-Grounded Browser Agents for Offline A/B Testing in E-Commerce](https://mlsanity.com/paper/2602.01443/) (2026)
- [ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation](https://mlsanity.com/paper/2602.23716/) (2026)
- [JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks](https://mlsanity.com/paper/2602.06486/) (2026)
- [What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents](https://mlsanity.com/paper/2604.04468/) (2026)
- [Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants](https://mlsanity.com/paper/2606.12608/) (2026)
- [Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents](https://mlsanity.com/paper/2606.13385/) (2026)
- [Business Arena: Benchmarking LLM Agents in a Realistic Marketplace](https://mlsanity.com/paper/2608.08621/) (2026)
- [Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations](https://mlsanity.com/paper/2609.19101/) (2026)
- [Performance Trade-offs of Optimizing Small Language Models for E-Commerce](https://mlsanity.com/paper/2510.21970/) (2025)
- [RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments](https://mlsanity.com/paper/2603.16453/) (2026)
- [Valley3: Scaling Omni Foundation Models for E-commerce](https://mlsanity.com/paper/2605.01278/) (2026)
- [SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents](https://mlsanity.com/paper/2605.19219/) (2026)
- [Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce](https://mlsanity.com/paper/2608.02441/) (2026)
- [RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents](https://mlsanity.com/paper/2605.18805/) (2026)
- [MMShopBench: A Real-Log Benchmark for Multimodal, Multi-Turn Shopping Agents](https://mlsanity.com/paper/2607.29002/) (2026)
- [Evaluating Open-Weight E-Commerce Agents with Environment-Grounded Verification](https://mlsanity.com/paper/2609.16093/) (2026)
- [CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments](https://mlsanity.com/paper/2609.27273/) (2026)
- [Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another](https://mlsanity.com/paper/2610.16141/) (2026)
- [Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce](https://mlsanity.com/paper/2610.17378/) (2026)

## Abstract (arXiv)

Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as applying vouchers, managing budgets, and finding multi-products seller. To bridge this gap, we propose ShoppingBench, a novel end-to-end shopping benchmark designed to encompass increasingly challenging levels of grounded intent. Specifically, we propose a scalable framework to simulate user instructions based on various intents derived from sampled real-world products. To facilitate consistent and reliable evaluations, we provide a large-scale shopping sandbox that serves as an interactive simulated environment, incorporating over 2.5 million real-world products. Experimental results demonstrate that even state-of-the-art language agents (such as GPT-4.1) achieve absolute success rates under 50% on our benchmark tasks, highlighting the significant challenges posed by our ShoppingBench. In addition, we propose a trajectory distillation strategy and leverage supervised fine-tuning, along with reinforcement learning on synthetic trajectories, to distill the capabilities of a large language agent into a smaller one. As a result, our trained agent achieves competitive performance compared to GPT-4.1.