1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
# Oral-A recovery: dynamic innovation on standard ResNet depth
This branch does not reopen the failed learned-vectorizer A3/A4 protocol in
`ORAL_A.md`. It asks a different question that became available only after the
reciprocal Kolen--Pollack substrate and fast instruction-off neutral projection
were established: does load-bearing somato-dendritic innovation retain
standard-image performance, credit alignment, and a positive depth benefit as
the same canonical CIFAR ResNet family grows?
No recovery task endpoint may be generated until this protocol and its
executable gate are committed. Execution is hard-gated on both a complete D4
accept pass (`7/10`) and a complete oral-B R2 pass (`8/10`). Thus the required
order remains accept bar, biological oral-B, then standard-depth oral-A.
## Fixed panel and reuse boundary
Use CIFAR-10 ResNet-20/32/56 with width 16, BatchNorm, option-A shortcuts,
batch size 128, all 50,000 training examples, 200 epochs, SGD momentum 0.9,
weight decay `1e-4`, base learning rate 0.1 with 10-fold drops at epochs 100
and 150, no warmup, and model/data-loader seeds 10--14. Test is evaluated
exactly once at the endpoint; validation and intermediate test evaluations are
zero.
The matched methods are:
1. exact BP on the identical forward architecture;
2. tuned DFA at its already selected hidden rate 0.03 and output rate 0.1;
3. clean reciprocal Kolen--Pollack at hidden/output rate 0.1; and
4. dynamic innovation on the same reciprocal substrate, with traffic ratio 4,
traffic seed `5000 + model_seed`, 64 instruction-off calibration examples,
one closed-form neutral projection, and no later predictor update.
The already frozen D4 clean-KP and dynamic records at ResNet-20/seeds 10--14
are reused verbatim rather than rerun. The recovery therefore adds exactly 50
records: BP and DFA at depth 20, and all four methods at depths 32 and 56.
Together with D4 this forms an exact 60-cell panel. All new records must share
one clean tracked source revision; all D4 records retain their separate frozen
source revision. No completed record may be deleted, replaced, or pooled with
a pilot.
Fixed HFA remains closed after its preregistered short gate failed, while FF,
PEPITA, canonical EP, BurstCCN, and Dual Prop retain their method-native
audits. They are reported as separate baselines rather than silently moved
onto this matched standard-ResNet grid.
## Frozen mechanism and cost invariants
Every dynamic record must retain all D4 invariants: finite 200-epoch
trajectory; mean early-third credit alignment measured only diagnostically;
final feedback/forward cosine at least 0.98 and epoch-151--200 mean at least
0.97; instruction-to-teaching RMS error at most `1e-4`; maximum post-projection
traffic ratio and absolute soma slope at most `1e-5`; zero instruction leakage
into neutral observations; exactly 64 predictor-update examples; exactly one
neutral observation per ordinary example; and zero task-loss queries.
At every depth, dynamic and clean KP must each use at most `1.34x` the matched
BP MAC estimate. Dynamic elementwise projection work is reported separately
and must be positive. Peak allocated memory may not exceed 8 GiB on the
authorized GTX 1080 devices. Wall time is reported but is never substituted
for MACs, queries, or memory.
Every record must identify an NVIDIA GTX 1080 and visibility restricted to the
authorized timan107 physical GPU 5 or 7.
## Frozen oral-A gate
The five model/data seeds are the independent paired units. One-sided 95%
Student-t bounds use four degrees of freedom. Oral-A passes only if every
record and every audited value is finite and all of the following hold:
1. Mean BP accuracy is at least 90% at every depth and every dynamic endpoint
is at least 87%, preventing joint collapse from satisfying a relative gate.
2. At each depth, mean dynamic accuracy is within 2 points of matched BP and
the one-sided 95% upper bound on the paired deficit is at most 3 points.
3. At each depth, mean dynamic accuracy is within 1.5 points of clean KP and
the upper bound on that paired deficit is at most 2.5 points.
4. At depth 56, dynamic exceeds tuned DFA by at least 2 points on average and
the one-sided lower bound on the paired advantage is at least 1 point.
5. Dynamic gains at least 0.5 points from depth 20 to 56 on average, its
one-sided lower bound is nonnegative, and at least four of five paired
seeds improve. Its mean depth gain must also be no more than 1 point below
BP's paired depth gain.
6. Mean dynamic early-third alignment is at least 0.85 at every depth; every
depth-56 seed is at least 0.80; and the depth-56 mean retains at least 90%
of the depth-20 mean.
7. Every mechanism, query, MAC, elementwise-work, memory, source, split, and
evaluation invariant above passes without exception.
The executable runner and immutable complete-grid analyzer are
`experiments/oral_a_dynamic_scaling.py` and
`experiments/analyze_oral_a_dynamic_scaling.py`. A complete pass raises the
strict reviewer score from 8 to 9 and establishes oral-A standard-depth
scaling for dynamic innovation. Any failure is retained, leaves the score at
8, and closes this branch without depth, seed, optimizer, or threshold repair.
|
