I pre-registered 23 experiments (~$207, solo) testing whether a verification harness can substitute for scale at 4B — including a locked holdout that falsified my own headline result [R]

**Question:** how much of grounded-reasoning performance is weights, and how much is architecture? I spent a month testing this on the Qwen3-4B class, solo, with pre-registration discipline: every run's success criterion frozen in a runbook before spend, every failed bar published (most runs failed their bars).

**Setup.** Same weights (Qwen3-4B-Instruct-2507 + one LoRA) in every arm. The harness ("cube"): model extracts facts → **code verifies each fact against the problem text** (numbers must appear verbatim, ≥60% word coverage) → code performs the decisive arithmetic → each reasoning stage (constraint judgment / conditional prediction / choice / estimate) receives only verified facts as an anchor → template assembly with digit screens. Controls: (a) same weights, one pass; (b) Qwen3-4B-Thinking-2507, the reasoning-trained sibling. Judge: frozen Claude Sonnet prompt, cached verdicts, never writes training data.

**Blindness discipline, in increasing order:** frozen bars (all runs) → harness git-committed *before* the 16 holdout problems existed, problems authored by a third model (GPT — neither judge nor contestant), zero post-contact edits (verifiable in commit history) → the harness's answers judged *before* its final opponent was selected.

**What held:**

- Same weights, 0/16 → 6/16 certified-clean on strict grounding vs the one-pass control (paired, p≈0.008) on in-distribution problems.

- On the third-party holdout, realistic-severity criterion: harness 5/16, Thinking 2/16, one-pass 1/16 — at comparable token budgets (1,525 vs ≤1,360 tokens/answer).

- Two blind constraint-fidelity predictions, registered before their runs, both held.

**What failed (reported at equal volume):**

- The strict-band result **did not transfer to the holdout** (0/16 both arms). My 6/16 was local to my own problem generator — an external audit predicted exactly this risk before the run.

- A two-judge validity study on my own verdicts: flaw *detection* agrees across judge families (97.9%) but severity *ordering* is single-judge-dependent, so the 5–2 carries that qualifier until a judge-native severity read.

- Open confound: the harness makes 5 calls vs 1; matched-compute ablation (best-of-5 + corrupted-anchor arm) is the next run.

- n=16 throughout — existence-grade, Wilson CIs quoted in the record.

**The residual failure mode**, consistent across everything at this scale: *cross-stage incoherence* — the model correctly names the binding constraint, then violates it in its chosen action two sentences later. Facts verify; agreement between reasoning stages doesn't. Reasoning-trained weights reduce the rate but don't change the species.

**Full record** (23 runbooks, external adversarial audit with all 8 findings + dispositions, md5-sealed holdout, judge caches): https://github.com/Nikhiljangra07/cube-program

**Asking this sub:** (1) validity threats I haven't listed? (2) Is cross-stage incoherence measured anywhere in the literature under another name? (3) Given one more $10: matched-compute ablation or a 14B judgment-seat swap first?