Design and Report Benchmarks for Knowledge Work
The paper discusses the need for improved benchmarks in knowledge work AI, particularly in areas like coding and healthcare. It proposes a three-step approach to better align benchmark tasks with real-world work activities. The authors provide case analyses to illustrate how benchmark design influences the validity of performance claims.
- ▪The development of LLM agents has increased interest in knowledge-work AI.
- ▪Current evaluation methods for knowledge work often rely on traditional NLP task logic.
- ▪The paper introduces a three-step approach for designing and reporting benchmarks that reflect actual work activities.
2 outlets in our directory ran this story, first to last over 14 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ AI and the Rise of Just-in-Time Knowledge Work — Operating Notes
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Story provenance
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23262 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| 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 | 5WXLLZxLu2d2 · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| 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.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.23262 (cs) [Submitted on 22 May 2026] Title:Design and Report Benchmarks for Knowledge Work Authors:Yining Hua, Hongbin Na, Cyrus Ayubcha, Levi Lian View a PDF of the paper titled Design and Report Benchmarks for Knowledge Work, by Yining Hua and 3 other authors View PDF HTML (experimental) Abstract:The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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