Decomposing and Measuring Evaluation Awareness
The paper titled 'Decomposing and Measuring Evaluation Awareness' explores how frontier language models recognize evaluation contexts and adjust their behavior accordingly. It identifies the components of evaluation awareness and proposes a new benchmark, EvalAwareBench, to study these interactions. The findings suggest that recognition does not consistently lead to behavioral changes and highlight the importance of safety evaluations.
- ▪The study decomposes evaluation awareness into environment and model components.
- ▪EvalAwareBench is introduced as a benchmark to measure evaluation awareness across different models.
- ▪Recognition rates of models depend on the specific pairing of model and benchmark.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23055 |
| 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 | AWrgvqJsND4B |
| 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 > Machine Learning arXiv:2605.23055 (cs) [Submitted on 21 May 2026] Title:Decomposing and Measuring Evaluation Awareness Authors:Changling Li, Terry Jingchen Zhang, Jie Zhang, Zhijing Jin, Sahar Abdelnabi, Maksym Andriushchenko View a PDF of the paper titled Decomposing and Measuring Evaluation Awareness, by Changling Li and 5 other authors View PDF HTML (experimental) Abstract:Frontier language models sometimes recognize that they are being evaluated and adjust their behavior, undermining validity of benchmark results. Yet the field studies it without a shared foundation, conflating properties of the evaluation with properties of the model, and detection with behavioral response.
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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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