Recognises reverse-engineered objects not marked as-is
Reverse-engineered objects without status: as-is and their source.
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 2 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 89% | Not shown |
| gemini-3.5-flash-lite | Low cost | 90% | Mastered |
| gpt-6-luna | Low cost | 9% | Not mastered |
| claude-haiku-5-5 | Low cost | 90% | 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: The as-is status and the source system on every object
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: produced lists PT-004 with no status Acceptable: status: as-is, source: AS-001 on every object
Also known as: As-is not marked as-is
Related knobits
Builds on
All 7 knobits of Reverse-engineer a model from an existing database.
Source: Review checklist reverse-engineering, row 2 (DMBOK §2.2.2)