Testing the effectiveness AI peer review
Meta ScienceHow well does AI peer review work?A new evaluation benchmarkPaul LitvakAug 10, 20261596ShareSummaryClaude and I planted 100 known errors into 10 open-access psychology papers and then ran them through frontier models and two commercial AI review tools. In brief:The best single system caught 71 of 100 errors, while the worst caught 30.Pooling every system’s output caught 93 of 100. Models are only partly correlated in the errors they find, making ensembling a big lever for finding issues in papers.
- ▪Meta ScienceHow well does AI peer review work?A new evaluation benchmarkPaul LitvakAug 10, 20261596ShareSummaryClaude and I planted 100 known errors into 10 open-access psychology papers and then ran them through frontier models and two com
- ▪In brief:The best single system caught 71 of 100 errors, while the worst caught 30.Pooling every system’s output caught 93 of 100.
- ▪Models are only partly correlated in the errors they find, making ensembling a big lever for finding issues in papers.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Hacker News (AI / LLM) |
| Canonical URL | https://www.paullitvak.com/p/how-well-does-ai-peer-review-work |
| Publication time | Tue, 11 Aug 2026 10:12:39 +0000 |
| Retrieval time | 2026-08-11T10:15:42.188Z |
| Last seen | 2026-08-11T10:15:42.188Z |
| 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 | L8Pb2-ScgJ2s · 1 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
Meta ScienceHow well does AI peer review work?A new evaluation benchmarkPaul LitvakAug 10, 20261596ShareSummaryClaude and I planted 100 known errors into 10 open-access psychology papers and then ran them through frontier models and two commercial AI review tools. In brief:The best single system caught 71 of 100 errors, while the worst caught 30.Pooling every system’s output caught 93 of 100. Models are only partly correlated in the errors they find, making ensembling a big lever for finding issues in papers. Check your papers against multiple models!Seven errors could not be caught by any system.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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