Initial eval-only card: RYS sweep results for SmolLM2-1.7B-Instruct (negative result; v2 corpus)

README.md

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+tags:
+  - rys
+  - layer-duplication
+  - reasoning-circuits
+  - evaluation-only
+  - cross-architecture
+---
+
+# SmolLM2-1.7B-Instruct — RYS evaluation
+
+**Evaluation-only repository — contains no model weights.** This card documents the layer-duplication (RYS) sweep results for [`HuggingFaceTB/SmolLM2-1.7B-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) as part of the cross-architecture v2 corpus.
+
+**Sweep data + cross-architecture analysis:** [`john-broadway/rys-sovereign-collection-v2`](https://huggingface.co/datasets/john-broadway/rys-sovereign-collection-v2)
+
+## Headline
+
+First published RYS negative result: zero of 30 configurations boost reasoning >5%. Notable because sibling SmolLM2-135M and SmolLM2-360M both respond normally — the 1.7B size-point is uniquely anomalous within this family.
+
+## Sweep results — best configuration
+
+|              | Baseline | Δ at best config |
+|---|---:|---:|
+| Math         | 0.477 | −6.19 |
+| EQ           | 77.66 | +1.09 |
+| Reasoning    | 58.82%  | +0.00 |
+
+**Best configuration:** (15,18) block-3 (best combined Δ; still negative overall)  
+**Peak reasoning Δ:** +0.00% (zero configurations boost reasoning >5%)  
+**Reasoning-boosting configurations:** 0 of 30 configs
+
+## Mechanism: falsification boundary (no recoverable suppression)
+
+Hypothesis: SmolLM2 1.7B-scale heavy-synthetic training produces uniformly-capable layers without specialized circuits to duplicate. The sibling SmolLM2-135M and SmolLM2-360M variants both respond normally (+17.65% and +23.53% reasoning lift). The 1.7B size-point is uniquely anomalous within this family. Caveat: the 1.7B sweep used a narrower block-size search [3,4] than other sweeps — a re-sweep with full blocks would resolve whether the narrower search caused the result or whether it is a genuine size-specific anomaly.
+
+## Position in the v2 curve
+
+Across the **21 model variants** spanning 10 architecture families in the v2 corpus, Pearson r(baseline reasoning, peak reasoning Δ) = **−0.726**. Weak baselines lift more, in their weakest dimension. SmolLM2-1.7B-Instruct's placement on the curve: baseline reasoning 58.82%, peak Δ +0.00% (zero configurations boost reasoning >5%).
+
+## RYS method
+
+RYS ("Repeat Your Self") duplicates a contiguous block of transformer layers so hidden states pass through the same circuit twice. **No training, no weight changes, no merging.**
+
+- Original RYS method: [David Ng](https://dnhkng.github.io/posts/rys/)
+- Sweep + probe toolkit: [`alainnothere/llm-circuit-finder`](https://github.com/alainnothere/llm-circuit-finder)
+- Hardware used for these sweeps: NVIDIA DGX Spark (GB10)
+
+## No weights here — why
+
+Negative-result publication is the point here — SmolLM2-1.7B is a falsification data point, not a deployment candidate. The card documents the **evaluation evidence** (sweep JSONL, cross-architecture position, the sibling-checks against 135M and 360M that respond normally). No RYS-modified weights would change the conclusion: the 1.7B size-point in this family does not respond to RYS.
+
+For context on what IS deployable in the collection, see the v1 cross-scale (Qwen2.5 + Qwen3-32B) and v2 Qwen3-family cohort linked in the [v2 dataset card](https://huggingface.co/datasets/john-broadway/rys-sovereign-collection-v2).
+
+## Citation
+
+```bibtex
+@misc{broadway2026rys_v2,
+  author = {Broadway, John and {Claude (Opus 4.6, 4.7)}},
+  title  = {RYS Sovereign Collection v2: Cross-architecture Sweep Corpus},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/john-broadway/rys-sovereign-collection-v2}
+}
+```
+
+## Attribution
+
+John Broadway, with collaboration from Claude (Opus 4.6 in April 2026 sweep generation; Opus 4.7 in May 2026 cross-architecture analysis and publication). Original RYS method by [David Ng](https://dnhkng.github.io/posts/rys/) on Qwen2-72B; sweep + probe toolkit by [alainnothere](https://github.com/alainnothere/llm-circuit-finder).
+
+## License
+
+This evaluation card content: MIT. The base model `HuggingFaceTB/SmolLM2-1.7B-Instruct` retains its original license.