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New benchmarks reveal LLM agents struggle with complex shopping and retail tasks

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new benchmarks to evaluate the capabilities of large language model (LLM) agents in complex, real-world scenarios. ShoppingBench and EComAgentBench focus on intricate shopping tasks that involve hidden intents, budget management, and multi-product sourcing, revealing that even advanced models like GPT-4.1 struggle to achieve high success rates. Similarly, RetailBench assesses LLM agents in long-horizon retail management simulations, highlighting significant gaps in their decision-making and policy consistency compared to optimal strategies. AI

IMPACT These benchmarks highlight the need for more robust LLM agents capable of handling complex, multi-step reasoning and decision-making in real-world applications.

RANK_REASON Multiple research papers introducing new benchmarks for evaluating LLM agents in complex, long-horizon tasks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New benchmarks reveal LLM agents struggle with complex shopping and retail tasks

COVERAGE [4]

  1. arXiv cs.CL TIER_1 English(EN) · Jiangyuan Wang, Kejun Xiao, Qi Sun, Huaipeng Zhao, Tao Luo, Jian Dong Zhang, Xiaoyi Zeng ·

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

    arXiv:2508.04266v4 Announce Type: replace Abstract: 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…

  2. arXiv cs.AI TIER_1 English(EN) · Zeyao Du, Tong Li, Haibo Zhang ·

    EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent

    arXiv:2606.17698v1 Announce Type: new Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked. Benchm…

  3. arXiv cs.CL TIER_1 English(EN) · Haibo Zhang ·

    EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent

    As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked. Benchmarks that expose full intent upfront and grade o…

  4. arXiv cs.AI TIER_1 English(EN) · Linghua Zhang, Jun Wang, Jingtong Wu, Zhisong Zhang ·

    RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments

    arXiv:2606.15862v1 Announce Type: new Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain. We introduce RetailBench, a data…

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