Recognises a cycle in the mastery implications
depends_on, applies_knowledge and has_part together form a cycle, so no knowledge state is consistent.
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 | 82% | Not shown |
| gemini-3.5-flash-lite | Low cost | 81% | Not shown |
| gpt-6-luna | Low cost | 81% | Not shown |
| claude-haiku-5-5 | Low cost | 82% | 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: The link that is not a mastery implication
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: K-3 applies_knowledge K-10 and K-10 depends_on K-3 Acceptable: The link that is not a mastery implication removed (the knowledge does not need the skill)
Also known as: Cycle in the mastery implications
Related knobits
Builds on
- T-152 Explains mastery implication in a knobit graph
- T-1901 Explains a directed graph and a cycle in it
Usually followed by
All 16 knobits of Review a knobit register for assessment.
Source: Review checklist 01-knobit-register, row 5 (LCG KB-08, KB-17; KST)