Abstract: same doctrine

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

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             matched backprop twin, models up to 135M, with backprop twins,
             multi-seed discipline, and hardware-relevant ablations (quantization, analog faults, nudge operating
             windows, energy accounting) at every stage. Scaling a physical learning rule also surfaces new science:
-            we identified a width-scaling loss in the EP gradient invisible to per-step alignment metrics, built an
-            instrument that measures it in 90 minutes per candidate recipe, mapped its dose-response law, and
-            demonstrated an estimator-side treatment that recovers 97% of it without touching the model or the
-            cost budget.
+            the frozen recipe fails one width step up, with sharp measurable structure. Locating the mechanism,
+            fitting a transfer law, and validating it blind at the next width is the current phase; the
+            instruments for that campaign (a 90-minute estimator-quality screen and a stability probe) are built
+            and calibrated.
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