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# Convolutional HFA baseline protocol
## Status and claim boundary
This protocol was frozen before observing any convolutional hierarchical-FA
accuracy endpoint. It adds a matched local-learning baseline after the
hierarchical oracle localized the ResNet bottleneck to spatial child-error
fields. HFA is not an SDIL novelty claim: recursive random feedback is prior
art, and a strong HFA result cannot rescue the failed frozen Oral-A A3 gate or
open its sealed A4 test panel.
The implementation follows the actual option-A residual DAG. Every learned
forward convolution has an independent fixed 3x3 feedback tensor; shortcut
adjoints are parameter-free, and ReLU and BatchNorm Jacobians are local. The
feedback code never reads a downstream forward convolution. In an audit-only
symmetric test, setting the feedback tensors to the forward tensors and the
readout feedback to the negative readout transpose reproduces hidden negative
gradients to below `2e-12` relative error and the exact-BP update to below
`2e-7` absolute error. Actual experiments never make this copy.
## HFA-S1: bounded short screen
Use the frozen Oral-A development split and the A2b setting: seed-0 ResNet-20,
the first 10,000 post-split training examples, 5,000 validation examples,
20 epochs, batch 128, ordinary CIFAR augmentation, cosine decay without
warmup, momentum 0.9, weight decay `1e-4`, and final validation evaluation
only. Output learning rate is 0.1. Cross hidden learning rate
`{0.01, 0.03, 0.1}` with feedback scale 1 and the single predeclared feedback
seed derived from model seed 0. A 32-example training-prefix alignment probe is
diagnostic only.
Select maximum final validation accuracy, then lower estimated MACs, then
lower hidden learning rate. The short screen opens one full validation run only
if the selected trajectory is finite and reaches at least 50% validation
accuracy. For interpretation, the already observed matched short endpoints are
DFA `37.16%`, failed-v1 SDIL `41.98%`, and BP `74.94%`; these are comparisons,
not selectable thresholds.
## HFA-S2: full validation baseline
If HFA-S1 passes, train the selected HFA setting for 200 epochs on all 45,000
development-training examples. Copy the A1 schedule exactly: batch 128,
momentum 0.9, weight decay `1e-4`, output base rate 0.1, and hidden base rate
selected by HFA-S1, with 10x drops at epochs 100 and 150 and no warmup. Evaluate
the same 5,000-example validation split only at the end. No recovery grid is
allowed.
This is a baseline endpoint, not an advancement test for SDIL. A finite result
at or above 80% is considered a strong matched local baseline that any learned
hierarchical SDIL variant must compare against. A weaker result remains
reportable and cannot trigger additional HFA tuning. Neither outcome permits
test-set evaluation.
## Accounting and stop rules
- Record fixed feedback parameters, feedback-convolution MACs, local
correlation MACs, peak memory, wall time, and zero causal queries.
- All three HFA-S1 jobs must use the same clean git commit and must not touch
CIFAR-10 test data.
- No feedback scale, seed, optimizer, architecture, or extra learning rate is
added after the S1 endpoints are read.
- HFA-S2, if opened, copies the selected S1 hidden rate mechanically and does
not select on intermediate validation accuracy.
## Audited outcome (2026-07-22)
All three HFA-S1 trajectories are finite and test-free. Validation accuracy
increases monotonically over the frozen hidden-rate grid: `39.64%` at 0.01,
`41.58%` at 0.03, and `43.52%` at 0.1. The selected 0.1 run has early-third
teaching alignment `0.040406`, zero causal queries, 267,904 fixed feedback
parameters, and estimated work `2.4242e13` MACs.
The selected endpoint exceeds matched DFA by `6.36` points and the failed-v1
short SDIL endpoint by `1.54` points, but misses the preregistered 50% HFA-S2
threshold by `6.48` points. Status is `selected_full_closed`; the 200-epoch HFA
run is not launched, no extra HFA setting is allowed, and CIFAR-10 test remains
untouched.
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