Llama-3.2-3B-Instruct with layers 21-23 duplicated. A late-stack math circuit runs twice on every forward pass.
28 base layers → 31 after duplication. No training, no merging, no weight changes.
Math 0.470 → 0.6132 (+14.32 — the biggest math lift in the v2 corpus). Reasoning 88.24% → 82.36% (−5.88). EQ 84.38 → 84.11 (−0.27).
Results
Metric
Baseline
RYS (21,24)
Delta
Math
0.470
0.6132
+14.32
EQ
84.38
84.11
−0.27
Reasoning
88.24%
82.36%
−5.88
The math amplifier. Llama-3.2-3B-Instruct has the second-highest baseline reasoning in the v2 corpus (88.24%) — near-ceiling, so RYS has little reasoning room to lift. But the same train-free intervention applied to the late-stack block (21,24) produces the biggest math lift anywhere in the corpus (+14.32 absolute, ~30% relative). Math and reasoning circuits sit at different depths in this model; the math one has headroom.
Pick this when math throughput matters and reasoning is already strong enough. The within-family contrast is the loudest in the corpus: sibling Llama-3.2-1B-RYS-10-13-GGUF lifts reasoning from 0% → 64.71% on the same block-duplication mechanism applied to a different depth.
v2 — cross-architecture corpus. 21 model variants across 10 architecture families. Inverse correlation (r = −0.726): weak baselines lift more, in their weakest dimension. The Llama-3.2 family alone (1B + 3B) spans the entire baseline-vs-magnitude curve in the v2 corpus. 13 deployable RYS-applied weight repos covering every non-zero-lift variant.
John Broadway, with collaboration from Claude (Opus 4.6 in April 2026 sweep generation and build pipeline; Opus 4.7 in May 2026 cross-architecture analysis and publication). Original RYS method by David Ng on Qwen2-72B; sweep + probe toolkit by alainnothere.
