Principles

These are lessons the test series earned — empirically derived from experiments on sign-type discrimination on Gemma 12B (local, llama.cpp), not stylistic preference. They exist so that a future builder (human or agent) does not push the project back into over-design. The full text is in PRINCIPLES.md.

1. The 12B model’s failure mode is over-hedging, not false affirmation. Without a frame, the model rejects even valid conclusions. The most valuable part of the prompt is the commit directive.

2. The content of the frame is often irrelevant; the effect is attention/de-hedging, not ontology. The blueprint is pointers into the material + a tiny spine, not a large ontology.

3. An elaborate procedure does not beat plain framing — and can trip you up. No rigid reasoning templates in the system prompt. Structure goes on the I/O boundary, not into the model’s head.

4. State and facts live outside the model; the model proposes constrained deltas. GBNF (local) / json_schema (OpenAI-compat) for deltas; the engine is the source of truth.

5. Drift is the real front. Measure where the model actually fails. Don’t spend measurement on what the model already passes; target long-horizon coherence.

6. The eval rig is the product; the skeleton/prompt is a consumable. Every change passes a differential eval with a pre-registered threshold.

7. The lessons are scale-dependent. The 12B recipe does not necessarily work on 4B — more skeleton for smaller models, less for larger.

8. Build the thinnest, grow by proof. Start from the thinnest harness; add a piece only when ablation shows a delta. Tokens and rigidity are paid for; the benefit must be proven.