We built something to replace the teacher. It worked. Then something else went wrong.

Part 6 ended with a problem we couldn't patch: a token model cannot reliably grade a concept model. The mismatch isn't fixable with a better rubric or a better teacher model. It's architectural.

So we stopped trying to fix the teacher and built a replacement.

Discovery: The Teacher Replacement

The idea was simple. Instead of asking Gemma to generate questions and grade responses, we'd build a rule-based system that already knew the right answers.

Each rule is a (pattern, expected response signature) pair. "does ice float?" expects a response containing "float" and "water." "what is your name?" expects a response containing "origin." No LLM anywhere in the loop. No drift. No mode collapse. No token-fluency bias.

We called it Discovery. We ran the first test.

The numbers: 0.79 seconds for 180 tests. 94.6% pass rate on Tier 1. Zero duplicates.…