EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce
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Abstract (arXiv)
Foundation agents have rapidly advanced in their ability to reason and interact with real environments, making the evaluation of their core capabilities increasingly important. While many benchmarks have been developed to assess agent performance, most concentrate on academic settings or artificially designed scenarios while overlooking the challenges that arise in real applications. To address this issue, we focus on a highly practical real-world setting, the e-commerce domain, which involves a large volume of diverse user interactions, dynamic market conditions, and tasks directly tied to real decision-making processes. To this end, we introduce EcomBench, a holistic E-commerce Benchmark designed to evaluate agent performance in realistic e-commerce environments. EcomBench is built from genuine user demands embedded in leading global e-commerce ecosystems and is carefully curated and annotated through human experts to ensure clarity, accuracy, and domain relevance. It covers multiple task categories within e-commerce scenarios and defines three difficulty levels that evaluate agents on key capabilities such as deep information retrieval, multi-step reasoning, and cross-source knowledge integration. By grounding evaluation in real e-commerce contexts, EcomBench provides a rigorous and dynamic testbed for measuring the practical capabilities of agents in modern e-commerce.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 29 of its 34 references that are in the map. First posted on arXiv 9 Dec 2025.
Builds on
29 of 34 references.
References · 29
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17 more
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Built on by
Citing papers per year: 2026: 7.
7 papers.
- 2026FutureX-Pro: Extending Future Prediction to High-Value Vertical Domainscited by 8
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationcited by 8
- 2026IndustryBench: Probing the Industrial Knowledge Boundaries of LLMscited by 3
- 2026JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Taskscited by 2
- 2026IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Productscited by 2
- 2026AlphaEval: Evaluating Agents in Productioncited by 1
- 2026Valley3: Scaling Omni Foundation Models for E-commercecited by 1
- 2026IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products12 Jun 2026
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application10 Jun 2026
- 2026IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs11 May 2026
- 2026Valley3: Scaling Omni Foundation Models for E-commerce2 May 2026
- 2026AlphaEval: Evaluating Agents in Production14 Apr 2026
- 2026JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks6 Feb 2026
- 2026FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains18 Jan 2026