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authorYurenHao0426 <Blackhao0426@gmail.com>2026-07-22 17:28:47 -0500
committerYurenHao0426 <Blackhao0426@gmail.com>2026-07-22 17:28:47 -0500
commit24e299161c6040f64ace6181c1ee1c543851eff1 (patch)
treeeca323a185c20083648eb56bd5b86f7b5eaebbbc /ROADMAP.md
parentd945c4213e57135c53bc18ed9811c7a61f1f761a (diff)
docs: record dynamic projection stability and short pass
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@@ -515,6 +515,25 @@ margins retain finite tensors but violate loss, signal-ratio, weight, and
momentum envelopes by orders of magnitude. No validation protocol opens. This
rules out fixed sign-only predictor bias as the missing gain-control mechanism.
+**Dynamic neutral projection D1: passed.** `DYNAMIC_INNOVATION.md` freezes a
+single fast local controller rather than another margin grid. Every task batch
+provides a paired instruction-off soma/apical observation; the fast affine fit
+removes the current neutral residual coupling without reading task instruction
+or changing the slow predictor. All 352 training-only steps and states remain
+finite, maximum loss is `2.9281`, and the controller reduces a growing
+`0.005833` neutral/traffic ratio to at most `3.03e-8`. No held-out evaluation
+occurs, so this mechanics pass opens D2 without changing the score.
+
+**Dynamic neutral projection D2: passed.** The sole frozen 20-epoch record
+reaches `83.58%` validation accuracy, above clean KP's `82.66%` and 73.58
+points above both failed MT-1 controls. Early alignment is `0.883830`, final
+feedback cosine is `0.904056`, all projection certificates pass, cost is
+`1.3261x` BP MACs plus explicitly reported elementwise work, and test remains
+untouched. Because this is one short seed, the score stays 5/10. The
+predeclared 200-epoch D3 validation endpoint is now open; only a complete D3
+pass can make the 5-to-6 score update eligible and open independent test
+confirmation.
+
Prepare convolutional local-update primitives and ResNet-20/32/56 protocols early. Queue frozen
runs opportunistically on authorized idle GPUs. Because BurstCCN already reports CIFAR-10 and
ImageNet scaling, dataset scale alone is not novel. The oral-level target is a memorable joint