# ShoppingComp: Are LLMs Really Ready for Your Shopping Cart?

arXiv: https://arxiv.org/abs/2511.22978 · HTML: https://mlsanity.com/paper/2511.22978/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 19 of 19)

- [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)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://mlsanity.com/paper/2501.00274/) (2024)
- [ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents](https://mlsanity.com/paper/2511.07685/) (2025)
- [eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data](https://mlsanity.com/paper/2402.08831/) (2024)
- [PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning](https://mlsanity.com/paper/2511.11562/) (2025)
- [OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation](https://mlsanity.com/paper/2506.05606/) (2025)
- [ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents](https://mlsanity.com/paper/2508.04266/) (2025)
- [Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models](https://mlsanity.com/paper/2410.20745/) (2024)
- [SedarEval: Automated Evaluation using Self-Adaptive Rubrics](https://mlsanity.com/paper/2501.15595/) (2025)
- [YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering](https://mlsanity.com/paper/2505.14279/) (2025)

## Built on by (most cited first, 9 of 9)

- [SimGym: Traffic-Grounded Browser Agents for Offline A/B Testing in E-Commerce](https://mlsanity.com/paper/2602.01443/) (2026)
- [Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants](https://mlsanity.com/paper/2606.12608/) (2026)
- [ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons](https://mlsanity.com/paper/2608.09282/) (2026)
- [SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents](https://mlsanity.com/paper/2605.19219/) (2026)
- [Evaluating Deep-Search Agents under Hierarchical Web Evidence Poisoning](https://mlsanity.com/paper/2609.06027/) (2026)
- [RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents](https://mlsanity.com/paper/2605.18805/) (2026)
- [APeB: Benchmarking Personalization Ability of Large Language Model Agents](https://mlsanity.com/paper/2607.03162/) (2026)
- [Traverse: Learning When to Remember, Reset, and Redirect for Long-Horizon Web Search](https://mlsanity.com/paper/2609.37082/) (2026)
- [Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce](https://mlsanity.com/paper/2610.17378/) (2026)

## Abstract (arXiv)

We present ShoppingComp, a challenging real-world benchmark for comprehensively evaluating LLM-powered shopping agents on three core capabilities: precise product retrieval, expert-level report generation, and safety critical decision making. Unlike prior e-commerce benchmarks, ShoppingComp introduces difficult product discovery queries with many constraints, while guaranteeing open-world products and enabling easy verification of agent outputs. The benchmark comprises 145 instances and 558 scenarios, curated by 35 experts to reflect authentic shopping needs. Results reveal stark limitations of current LLMs: even state-of-the-art models achieve low performance (e.g., 17.76% for GPT-5.2, 15.82% for Gemini-3-Pro).Error analysis reflects limitations in core agent competencies, including information grounding in open-world environments, reliable verification of multi-constraint requirements, consistent reasoning over noisy and conflicting evidence, and risk-aware decision making. By exposing these capability gaps, ShoppingComp characterizes the trust threshold that AI systems must cross before they can be proactively trusted for reliable real-world decision making. Our code and dataset are available at https://github.com/ByteDance-BandAI/ShoppingComp.