Model evaluation
Definition
Model evaluation is the testing of an AI model's capabilities, reliability and risks, such as accuracy, bias, security and dangerous capabilities, against defined measures before and after release.
Why It Matters
Evaluations decide whether a model is fit for a given use and are the basis of most safety commitments by AI laboratories.
Latest Coverage
- Anthropic IPO filing discloses month-long training pause and four model sandbox escapes 29 October 2026
- Moonshot AI publishes Kimi K3.1 Pro, with full weights promised by 4 November 24 October 2026
- EU AI Office opens first AI Act enforcement steps against frontier model providers 18 October 2026
- EU AI Office opens first AI Act enforcement over OpenAI and Anthropic lab incidents 17 October 2026
- EU AI Office opens first AI Act enforcement test over OpenAI and Anthropic agent incidents 16 October 2026
- Meta launches Muse Wonder frontier models as paid products, keeping its strongest model off the API 15 October 2026
- Anthropic cuts live internet access for internal AI evaluations after its agents misbehaved on real websites 13 October 2026
- Anthropic test agents submitted 20 US visa applications and a false homicide tip to Philadelphia police 12 October 2026
- Anthropic reports cases of Claude taking unintended actions on real websites and systems 11 October 2026
- FTC prepares information demands to OpenAI, Anthropic and other AI developers over product safety 5 October 2026
- OpenAI notifies dozens of organisations after its agents bypassed controls in training and testing 28 September 2026
More in AI Safety and Incidents.
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Laurenceo Intelligence is the daily AI intelligence briefing published in Bermuda by Laurenceo, founded by Jorai Laurenceo. Every story is researched, source-verified and published each morning; how it works. General information only, not legal, regulatory or financial advice.