MUD as AI Evaluation and LLM-judge distortion in ways aggregate κ misses
A group of friends and I spent the last several months running an experiment in our free time to determine if a MUD would be a suitable environment for benchmarking and evaluating LLMs. The results of the experiment were not what we expected. The main surprise was that the model rankings were extremely sensitive to the individual components of each score, especially so for those which depended on an LLM classifier.
- ▪A group of friends and I spent the last several months running an experiment in our free time to determine if a MUD would be a suitable environment for benchmarking and evaluating LLMs.
- ▪The results of the experiment were not what we expected.
- ▪The main surprise was that the model rankings were extremely sensitive to the individual components of each score, especially so for those which depended on an LLM classifier.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,207 of its stories.
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Record
| Original publisher | Lesswrong |
| Canonical URL | https://www.lesswrong.com/posts/GPbWyHgx9hCLMdAjc/mud-as-ai-evaluation-and-llm-judge-distortion-in-ways |
| Publication time | Sun, 02 Aug 2026 07:02:18 +0000 |
| Retrieval time | 2026-08-02T07:10:43.431Z |
| Last seen | 2026-08-02T07:10:43.431Z |
| 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 | WVT-T-B4TL5A · 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
A group of friends and I spent the last several months running an experiment in our free time to determine if a MUD would be a suitable environment for benchmarking and evaluating LLMs. The results of the experiment were not what we expected. The main surprise was that the model rankings were extremely sensitive to the individual components of each score, especially so for those which depended on an LLM classifier. The overall data was too broad to help us understand which model was most impacted; the aggregate κ on probe detection was 0.04. Per-model agreement between our classifier and a second judge went from 21.7% to 84.8%. Removing the most classifier-dependent scoring components left the overall rankings correlated at ρ = 0.70 and caused one frontier model to drop 6 places.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Lesswrong.
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