Explains gated weighted evaluation
Mandatory requirements as gates; weights set before scoring and summing to 100; criteria that discriminate.
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Mandatory requirements as gates; weights set before scoring and summing to 100; criteria that discriminate.
How it is assessed: Answers a written question on the gated weighted evaluation correctly
Related knobits
Builds on
Needed for
- O-101 Recognises a mandatory requirement scored instead of gated
- O-102 Recognises a non-discriminating evaluation criterion
- O-103 Recognises an evaluation criterion with no requirement
- O-105 Recognises weights deciding the outcome alone
- O-106 Recognises a gate-failing candidate recommended
- O-108 Recognises a requirement with no criterion
- O-109 Recognises duplicate evaluation criteria
- O-131 Applies mandatory requirements as gates before scoring
All 14 knobits of Evaluate data storage technology.
Source: Practice the stage 1 reference and review checklist rest on