CR study — final result (weak rung measured; H1 verdict)

2026-09-19. Weak rung qwen2.5-coder:7b (k=3, naive+gs) generated on the main PC (RTX 5070 Ti, 12 GB VRAM) and measured on the static suite; combined with the Copilot-arm hosted rungs (RESULTS-copilot-arm-mid-frontier.md). 32B dropped (VRAM). Conformance skipped on the weak rung (system-RAM constrained; and it was 0/0 there anyway — the 7B apps do not serve). See DEVIATION-weak-rung.md.

The ladder (Δ oriented as GS-benefit; + = GS better)

Weak → strong: qwen2.5-coder:7b → gpt-mid → {gpt / gemini / claude}-frontier. Median over k.

Metric qwen7b (weak) gpt-mid frontier (gpt / gemini / claude) H1 (Δ declines weak→strong)?
duplication % (lower better) +14.5 0 −1.2 / −3.5 / 0 YES — strong monotone decline
cyclomatic cc_mean (lower better) +0.63 +0.15 +0.15 / +0.07 / +0.16 YES — mild decline then flat
test files (higher better) −1 0 +3 / +2 / +2 NO — increases weak→strong
layer violations 0 0 0 no signal (flat)
behavioral oracle /6 0/0 (both fail) −1 −1 / −1 / 0 no weak signal; GS ≤ naive

qwen7b raw per cell (k=3): naive dup=25/17/19, cc=2.3/2.1/1.9, test=1/1/1, ts-files=6/5/5; gs dup=0/9/(empty), cc=1.7/1.3/(empty), test=0/4/0, ts-files=5/32/0.

Verdict on H1 (capacity-relative law)

Partially supported, and only on the structural-cleanliness axis.

  • Supported (duplication, complexity): GS’s advantage is largest where the model is weakest and recedes to ~zero at the frontier — the pre-registered capacity-relative shape. On duplication it is strong (+14.5 at the weak rung → 0/negative at mid+frontier). Interpretation: the cascade constrains a weak model toward clean, low-duplication, low-complexity structure that a frontier model already produces unaided.
  • Not supported (behavior, tests): the behavioral oracle gives no weak-rung signal (both conditions fail to serve, 0/6) and GS ≤ naive at every hosted rung; the test-file count moves opposite to H1 (a 7B cannot reliably author tests; the frontier GS arm does). Layer violations never fire.
  • Consistent with the master line: the frontier absorbs the mechanical delta. GS’s measurable benefit here is structural, capacity-relative, and receding — not “better code” at the frontier.

Contribution #4, honestly scoped: the capacity-relative claim holds as “specification scaffolding reduces structural bloat (duplication, complexity) most on weak models, receding to zero at the frontier” — NOT as a universal quality law. Report it that bounded, or as a mechanism demonstration, not a powered effect.

Caveats (load-bearing — do not drop when citing)

  1. n = 3, and GS is high-variance at the weak rung. One GS cell emitted a 32-file structured app, one ~5 files, one an empty/failed project. The advantage is real when the cascade “takes” but unreliable on a 7B. Medians reported, not means; IQRs are wide.
  2. Behavioral axis is null at the weak rung — the 7B apps do not migrate/serve, so conformance is 0/0 for both conditions. The H1 evidence is entirely static structure, not runtime quality.
  3. Single benchmark (Pastura), single weak model. 32B rung dropped (12 GB VRAM < ~20 GB). Conformance skipped on the weak rung for system-RAM reasons.
  4. Harness note: an earlier weak-rung run was invalidated by a background-task race (a stopped run’s inter-cell taskkill node killed live generations); re-run clean, single-run, after freeing memory. This result is the clean re-run.

What would make it clean (future work)

Re-run on a machine with ≥32 GB RAM + ≥24 GB VRAM: qwen7b and 32B, k≥5, with conformance on the weak rung, plus a second invented benchmark. Then a real Jonckheere-Terpstra / Page trend test on the duplication and complexity deltas (which is where the signal lives) rather than the direction+magnitude read reported here under §7’s small-k rule.