docs: close one-sided stability branch

4 files changed, 36 insertions, 0 deletions

diff --git a/RESULTS.md b/RESULTS.md
index 84ccae6..7c9ddd3 100644
--- a/RESULTS.md
+++ b/RESULTS.md
@@ -949,6 +949,18 @@ the forward local feedback mode. The linearized update in `THEORY.md` exposes
 the multiplicative term `D W C` and proves that any positive effective scalar
 mode has momentum transition spectral radius above one.
 
+The separately frozen training-only S0 screen then tests whether a one-sided
+neutral-regression upper margin is sufficient. None of
+`{0.001,0.003,0.01,0.03}` is eligible. The two smaller margins produce
+nonfinite BatchNorm running variances and `1e17`-scale weights. The two larger
+margins keep tensors finite for 352 steps but produce maximum losses of
+`1.40e5`/`4.97e6` and weights of `8.83e6`/`1.19e8`, violating the predeclared
+loss, signal-ratio, weight, and momentum envelopes. Every candidate preserves
+the four-to-one intervention, passes its one-sided residual-soma certificate,
+freezes the predictor, restores loader state, and evaluates no validation or
+test example. Sign-only dissipativity is therefore closed before endpoint
+access; no margin is selected and the score remains 5/10.
+
 ## How to run
 `experiments/run.py --mode {bp,fa,dfa,sdil} --dataset {mnist,fmnist,cifar10} --depth D --residual {0,1} --act {tanh,gelu,silu,relu}`
 Batteries: `experiments/run_v2.sh <ds> "<depths>" <res> <act> "<seeds>" <ep> <pfx>`.
diff --git a/REVIEW_SCORECARD.md b/REVIEW_SCORECARD.md
index 035448f..5443510 100644
--- a/REVIEW_SCORECARD.md
+++ b/REVIEW_SCORECARD.md
@@ -103,6 +103,7 @@ retroactively reopened by success on standard vision benchmarks.
 | 2026-07-22 / mixed-traffic MT-0 | Exact raw/matched/innovation mechanics pass; synthetic ResNet-20 matched batch peaks at 0.857 GB allocated on GTX 1080 without task data | 5 → 5 | Removes implementation, graph, and memory objections before endpoint access; supplies no evidence yet that innovation is useful |
 | 2026-07-22 / `f310ad5` mixed-traffic MT-1 | All three frozen conditions become nonfinite in epoch 1 and end at 10%; ratio calibration, predictor warmup, zero-query, and cost checks pass | 5 → 5 | Fails to make innovation load-bearing on a standard ResNet and closes MT-2/MT-3; the prior mechanism-only evidence survives but empirical support does not improve |
 | 2026-07-22 / `6531aed`, `d818fc2` MT-1 diagnosis | Training-only localization finds the first active failure at stem steps 23/69/74 for raw/matched/innovation; residual coupling yields the verified multiplicative operator `D W C` | 5 → 5 | Sharpens the failure into an operator-stability requirement and improves soundness of the negative analysis, but adds no successful held-out SDIL endpoint |
+| 2026-07-22 / `8d28fd9` stability S0 | A frozen training-only four-margin grid has no eligible candidate; sign-certified margins either overflow BN state or produce enormous transient loss and parameter growth | 5 → 5 | Closes fixed one-sided predictor bias and motivates a two-sided dynamic gain condition, but no validation endpoint or empirical support is gained |
 
 Future rows are appended only after an audited frozen stage. A score staying flat is informative:
 engineering, theory exposition, or visualization may make the paper more defensible without
diff --git a/ROADMAP.md b/ROADMAP.md
index dd521e4..35b1ed4 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -508,6 +508,13 @@ respectively, with no validation or test evaluation. This narrows any future
 independent method to operator-level gain control or dissipativity; more
 endpoint tuning of the same residual-RMS gate is not justified.
 
+The follow-up S0 one-sided stability branch also closes before held-out access.
+Its frozen four-margin training-prefix grid has no eligible candidate: small
+margins still overflow BN state and grow weights to `1e17`, while larger
+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.
+
 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
diff --git a/STABLE_INNOVATION.md b/STABLE_INNOVATION.md
index d499dc5..ecaf224 100644
--- a/STABLE_INNOVATION.md
+++ b/STABLE_INNOVATION.md
@@ -64,3 +64,19 @@ one-sided predictor branch without extending the grid. A pass only authorizes
 writing a separately frozen validation protocol with a new evaluation
 boundary; it supplies no accuracy claim and leaves the paper score at 5/10.
 
+## Audited S0 outcome
+
+S0 fails with no eligible margin. `delta=0.001` and `0.003` first make
+BatchNorm running variances nonfinite at steps 252 and 301; their maximum
+forward/feedback weights reach `8.16e16` and `1.48e17`. `delta=0.01` and
+`0.03` keep every tensor finite for 352 steps but have maximum minibatch losses
+of `1.40e5` and `4.97e6`, maximum used/instruction RMS ratios of 538.4 and
+6.59, and maximum weights of `8.83e6` and `1.19e8`. All four pass the
+one-sided slope certificate, frozen-predictor, traffic-calibration, loader, and
+zero-held-out-evaluation checks.
+
+The result shows that sign control alone is insufficient: a discrete momentum
+mode also has a lower stability boundary, and time-varying deep covariances can
+drive an initially negative effective mode beyond it. The grid is closed
+without another margin. No validation protocol is opened and the reviewer
+score remains 5/10.