DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation
DeepWeb-Bench is a new benchmark designed to evaluate deep research capabilities of language models. It emphasizes the need for extensive evidence collection, cross-source reconciliation, and long-horizon reasoning. The benchmark aims to provide clearer insights into model performance and weaknesses compared to existing evaluation methods.
- ▪DeepWeb-Bench introduces a more challenging evaluation framework for frontier language models.
- ▪The benchmark focuses on four capability families: Retrieval, Derivation, Reasoning, and Calibration.
- ▪Evaluation results show that derivation and calibration failures account for over 70% of errors in model performance.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.21482 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | WgzDtX87pVW0 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.21482 (cs) [Submitted on 20 May 2026] Title:DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation Authors:Sixiong Xie, Zhuofan Shi, Haiyang Shen, Jiuzheng Wang, Siqi Zhong, Mugeng Liu, Chongyang Pan, Peilun Jia, Baoqing Sun, Xiang Jing, Yun Ma View a PDF of the paper titled DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation, by Sixiong Xie and 9 other authors View PDF HTML (experimental) Abstract:Deep research, in which an agent searches the open web, collects evidence, and derives an answer through extended reasoning, is a prominent use case for frontier language models.
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