AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems
The paper titled 'AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems' introduces a framework for analyzing AI benchmark ecosystems. It highlights the measurement noise in leaderboard scores and proposes methods to quantify the sources of ranking variance. The authors provide insights into benchmark dynamics and suggest improvements for benchmark design and trustworthiness.
- ▪The study analyzes over 4,000 models from the Open LLM Leaderboard to understand ranking variances.
- ▪Current reporting practices underestimate the relationships between benchmarks and reveal local dependencies among leaderboard items.
- ▪Contributor metadata accounts for approximately 9% of rank-relevant variance, more than architecture or deployment categories.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25272 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| 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 | KsjV03IUotN8 |
| 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:2605.25272 (cs) [Submitted on 24 May 2026] Title:AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems Authors:Michael Hardy, Anka Reuel, Lijin Zhang, Jodi M. Casabianca, Sang Truong, Yash Dave, Hansol Lee, Benjamin Domingue, Sanmi Koyejo View a PDF of the paper titled AI Cartography: Mapping the Latent Landscape of AI Benchmark Ecosystems, by Michael Hardy and 8 other authors View PDF HTML (experimental) Abstract:While aggregate leaderboard scores drive AI development, they contain substantial measurement noise whose sources and magnitudes remain unquantified, making it unclear when rankings reflect genuine capability differences versus evaluation artifacts.
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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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