@inproceedings{ferreira-etal-2026-deepresearch,
    title = "{D}eep{R}esearch Retail: Benchmarking Tool-Augmented Deep Research in the {E}-Commerce Domain",
    author = "Ferreira, Rafael  and
      Palo, Flavio Di  and
      Lu, Huilin  and
      Jain, Ayush  and
      Aduri, Harsha",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.26/",
    pages = "386--409",
    ISBN = "979-8-89176-394-4",
    abstract = "Deep Research (DR) systems autonomously retrieve and synthesize information from web sources, however, industrial DR applications face a critical gap: effective integration of internal tools with web search. In this work, we introduce DeepResearch Retail, an evaluation framework grounded in real-world e-commerce data for assessing Deep Research with tools (DR+Tools) in realistic commercial settings. The framework evaluates both factual faithfulness and multidimensional response quality when reasoning over heterogeneous web and internal data sources.We further present Hybrid-ReAct, a multi-agent architecture that demonstrates how collaborative reasoning and tool use can produce evidence-grounded answers. Experimental results validate our framework{'}s utility, showing improvements in agent{'}s performance when leveraging web-page information and multi-agent specialization."
}