Explains mastery implication in a knobit graph
Prerequisites, applied knowledge and parts imply mastery and must be acyclic; succession implies nothing; an item needs the knobits its sets imply, and knobits no item separates cannot be diagnosed apart (LCG-KNOBIT KN-07; Doignon and Falmagne 1999).
Can AI do this?
Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 99% | Not shown |
| gemini-3.5-flash-lite | Low cost | 98% | Not shown |
| gpt-6-luna | Low cost | 98% | Not shown |
| claude-haiku-5-5 | Low cost | 99% | Not shown |
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: Prerequisites, applied knowledge and parts imply mastery and must be acyclic; succession implies nothing; an item needs the knobits its sets imply, and knobits no item separates cannot be diagnosed apart (LCG-KNOBIT KN-07; Doignon and Falmagne 1999).
How it is assessed: Answers a written question on the mastery implication correctly
Related knobits
Builds on
Needed for
- T-104 Recognises a work-process order recorded as a prerequisite
- T-105 Recognises a cycle in the mastery implications
- T-106 Recognises a transitive shortcut link
- T-108 Recognises knobits no item tells apart
- T-133 Repairs a relation wrongly recorded as a mastery implication
All 16 knobits of Review a knobit register for assessment.
Source: Practice the stage 1 reference and review checklist rest on