WANDR: A Benchmark for Wide and Deep Research

[2608.14747] WANDR: A Benchmark for Wide and Deep Research Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14747 (cs) [Submitted on 14 Aug 2026] Title: WANDR: A Benchmark for Wide and Deep Research Authors: Vitaliy Polshkov , Marcin Pitera , Jeremy Yang , Kirill Priemko , Maksim Gaiduk , Aleksandr Nikolenko , Denis Bykov , Clare Southern , Denis Yarats , Jerry Ma View a PDF of the paper titled WANDR: A Benchmark for Wide and Deep Research, by Vitaliy Polshkov and 9 other authors View PDF HTML (experimental) Abstract: WANDR (Wide ANd Deep Research) is a benchmark of 500 realistic, challenging data-collection tasks for research agents. Each task requires a system to discover a large set of entities that satisfy specified criteria (breadth), investigate each entity through multiple coordinated web searches (depth), and return independently verifiable records with supporting sources and excerpts. Tasks are represented as qualification key hierarchies that specify the entities, relationships, evidence, and required count at each level; a hierarchy with n companies, m employees per company, and k sources per employee requires n x m x k records. This structure supports diverse workflows such as market mapping, due diligence, literature review, product comparison, and talent sourcing, with targets ranging from dozens to thousands of records. WANDR replaces static gold answer sets with task-specific judges that refetch cited pages and verify each record against its evidence, allowing evaluation of current and changing facts. Record verdicts are aggregated into soft and hard precision, recall, and F1 scores that distinguish factual quality, coverage, and hierarchical completeness. The tasks are derived from de-identified product-usage logs and produced through a semi-automated pipeline with automated checks, empirical audits, and human review where needed. We evaluate six production research systems and find that the benchmark is far from saturated: at high effort, the strongest system reaches only 0.363 soft F1 and 0.133 hard F1. Performance degrades as target volume and hierarchy depth increase, with incomplete discovery, missing enrichment, and incomplete evidence construction remaining major bottlenecks. The benchmark and evaluation harness are available at this https URL . Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.14747 [cs.LG] (or arXiv:2608.14747v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14747 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jerry Ma [ view email ] [v1] Fri, 14 Aug 2026 00:18:53 UTC (2,083 KB) Full-text links: Access Paper: View a PDF of the paper titled WANDR: A Benchmark for Wide and Deep Research, by Vitaliy Polshkov and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.#476896
• Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents#1069954
• VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale#609789
• FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving#998172
• Data Integration Challenges for Machine Learning in Precision Medicine#335790