mlsanityData to 7 Oct 2026

EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce

arXiv 2512.08868 ↗First posted 9 Dec 2025cs.AIcited by 7

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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

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  6. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  7. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  8. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  9. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
  10. 2025Humanity's Last Examcited by 467
  11. 2025GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Modelscited by 446
  12. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
17 more
  1. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 320
  2. 2025WebSailor: Navigating Super-human Reasoning for Web Agentcited by 218
  3. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  4. 2025WebDancer: Towards Autonomous Information Seeking Agencycited by 180
  5. 2025WebWalker: Benchmarking LLMs in Web Traversalcited by 171
  6. 2025BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinesecited by 139
  7. 2025WebShaper: Agentically Data Synthesizing via Information-Seeking Formalizationcited by 118
  8. 2025Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolutioncited by 114
  9. 2025WideSearch: Benchmarking Agentic Broad Info-Seekingcited by 66
  10. 2025WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agentscited by 66
  11. 2025SealQA: Raising the Bar for Reasoning in Search-Augmented Language Modelscited by 65
  12. 2025WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learningcited by 59
  13. 2025FutureX: An Advanced Live Benchmark for LLM Agents in Future Predictioncited by 55
  14. 2025StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?cited by 43
  15. 2025ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?cited by 27
  16. 2025FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoningcited by 26
  17. 2025A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domainscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.