results: close hierarchical task-scalar calibration gate

1 files changed, 29 insertions, 0 deletions

diff --git a/RESULTS.md b/RESULTS.md
index d13a58c..64596e5 100644
--- a/RESULTS.md
+++ b/RESULTS.md
@@ -754,6 +754,35 @@ points, so HFA-S2 is closed without further tuning. The result confirms that
 spatial hierarchy is useful but that fixed random hierarchy alone does not
 solve standard-depth credit.
 
+### Hierarchical task-causal feedback calibration
+
+V4 perturbs all 267,904 hierarchical feedback parameters in one simultaneous
+parameter-space direction and uses two antithetic task-loss queries to estimate
+the local Q/R delta-rule moments. Its mechanics are exact, but the frozen
+400-event causal-capture screen is negative:
+
+| feedback rule | early alignment | all-layer alignment | max norm ratio | finite |
+|:--|--:|--:|--:|:--:|
+| fixed HFA | -0.000263 | 0.011279 | 1.06 | yes |
+| V4, etaA 0.1 | -0.000180 | 0.017336 | 1.05 | yes |
+| V4, etaA 1.0 | **0.000348** | 0.007922 | 58.48 | yes |
+| V4, etaA 10.0 | NaN | NaN | NaN | no |
+
+The selector chooses etaA 1.0 on early alignment among finite candidates, but
+it misses early `0.05`, all-layer `0.10`, matched early gain `+0.04`, and the
+norm-range gate. The high-rate record also makes the all-record finite check
+fail. Even the stable etaA 0.1 candidate has mean parameter prediction/target
+cosine only `0.000668` after 400 events.
+
+Every learned record consumes 800 logical batch-loss queries and an estimated
+`1.2254e13` MACs before any forward-weight training. Thus V4-2 accuracy and
+V4-3 full validation remain closed. The supported diagnosis is that spatial
+hierarchy supplies the right representation, but a single global task scalar
+is too noisy to calibrate this 267,904-parameter path at the frozen budget.
+The next comparator must change the information source rather than reopen the
+rate/query grid, for example local response-based learned FA or weight
+mirroring. No test endpoint was evaluated.
+
 ## 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>`.