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# SDIL ICLR 2027 evidence roadmap
This document predeclares the evidence gates used to decide which claims survive into a paper.
It is intentionally stricter than a list of experiments: a failed gate narrows the claim rather
than triggering selective seed removal or post-hoc protocol changes.
## Order of work
1. **Accept bar:** establish a load-bearing innovation mechanism, useful-depth scaling, fair cost,
theory, and the closest baselines.
2. **Oral bar B:** connect the mechanism to the signatures and new predictions motivated by
Harnett et al.'s somato-dendritic residuals.
3. **Oral bar A:** demonstrate the same mechanism in standard CNN/ResNet families. The runner is
developed early so long jobs can use otherwise idle, explicitly authorized GPUs without
delaying stages 1–2.
## Frozen accept claims and gates
### C1. Innovation is necessary under naturally mixed apical traffic
The apical compartment must contain ordinary non-teaching traffic generated by the model or task,
not only an additive nuisance chosen to match the predictor. Candidate traffic includes
back-propagating somatic activity, top-down contextual state, and task-irrelevant feedback shared
between neutral and teaching periods.
Required controls use the same forward architecture, initialization, data order, and update norm:
- raw apical signal;
- raw apical signal matched per sample to the innovation norm;
- somato-dendritic innovation;
- innovation with the predictor trained during task periods rather than neutral periods;
- no-traffic control, where raw and innovation should agree.
**Gate C1:** across at least two independently generated traffic families, innovation must improve
held-out accuracy by at least 2 percentage points over norm-matched raw feedback at a predeclared
traffic level, without harming the no-traffic setting by more than 0.5 points. The predictor must
remove measurable soma-predictable traffic while retaining causal teaching alignment.
### C2. SDIL scales when depth is useful
Flattened CIFAR is retained as a preservation result, not as evidence that added depth is used.
The primary controlled task must make BP accuracy increase with depth. Post-training block lesions
and residual-branch statistics must verify that the deep model actually uses its extra blocks.
**Gate C2:** BP must gain at least 5 points from the shallow to the deep configuration. SDIL must
recover at least 70% of that paired gain; the strongest competing local rule must recover no more
than 50%, or SDIL must beat it by at least 2 points at the deep endpoint. Removing the final third
of trained blocks must reduce SDIL performance, ruling out an effective shallow-network solution.
### C3. Causal calibration is genuinely amortized
Report wall time, forward-equivalent work, number of scalar loss queries, peak device memory, and
the perturbation batch expansion. Sweep perturbation frequency and number of directions under both
fixed epoch and fixed query/FLOP budgets.
**Gate C3:** a protocol using at least 10x fewer perturbation loss queries than the current
16-directions-every-4-steps setting must preserve at least 90% of its gain over DFA. Pareto claims
must hold in a hardware-independent cost coordinate as well as GTX-1080 wall time.
### C4. The comparison set contains the closest alternatives
The main comparison must include exact BP, FA, DFA, direct/unamortized node perturbation, learned
direct node-perturbation feedback (Lansdell et al. 2020), Dual Prop, and BurstCCN. The Lansdell
method is the exact no-traffic/P=0 backbone of the current implementation, not merely a related
baseline. BurstCCN is already demonstrated on CIFAR-10 and ImageNet and already models the Harnett
BCI signatures, so it is the strongest dendritic/scaling comparator. EP remains an informative
relaxation-based baseline. Forward-Forward and PEPITA are appendix context unless they become
competitive under the frozen protocol.
Each method receives a documented validation budget. Native-protocol and exact-architecture
comparisons are labelled separately; neither substitutes for a matched compute/query comparison.
### C5. Theory predicts the observed regimes
The minimum theory package contains:
1. bias and variance of simultaneous multi-layer perturbation, including cross-layer interference;
2. a smooth-loss descent bound separating angular alignment, gain calibration, and curvature;
3. an innovation/SNR result for subtracting soma-predictable apical traffic;
4. an explicit complexity table covering learning phases, transport, loss queries, FLOPs, and
memory.
Numerical simulations must test the predicted scaling with depth, width, directions, perturbation
scale, and predictor timescale.
## Protocol discipline
- Hyperparameters are selected on a validation split created only from the training set.
- The test set is evaluated for frozen candidate protocols, never used to choose schedules.
- Main results use five model seeds. Synthetic task claims additionally vary the task/teacher seed.
- Every completed finite trajectory is retained; crashes and excluded runs are reported with a
reason fixed before inspecting accuracy.
- Pilot results remain versioned but cannot be pooled with frozen runs.
- Every result records source revision, dirty state, protocol identifier, data split hash, logical
loss queries, forward-equivalent work, peak memory, and wall time.
## Oral bar B: biological bridge
After C1–C5 pass, test whether the model reproduces the qualitative Harnett signatures:
- dendritic activity contains information absent after conditioning on somatic activity;
- the residual decodes outcome/reward-related events;
- residual sign follows the neuron's causal contribution to the objective;
- residual predicts subsequent activity change or desired velocity;
- selectively disrupting the residual impairs learning more than matched nonspecific disruption.
The paper needs at least one prediction not used to construct SDIL. A preferred route is to request
the original data/analysis from the authors and test the prediction out of sample; otherwise the
claim is explicitly computational rather than a fit to cortical data.
## Oral bar A: standard deep architectures
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
result: near-BP accuracy in a standard architecture, a win over or materially simpler/lower-cost
regime than BurstCCN, and a nondominated accuracy–hardware-independent-cost point. Scale alone is
also insufficient if C1 does not show that somato-dendritic innovation is load-bearing.
## Stop conditions
- Do not extend flattened-CIFAR MLP depth merely to obtain a larger depth number.
- Do not add MNIST EP seeds unless a protocol bug invalidates the completed five-seed panel.
- Do not promote a wall-time frontier that disappears under loss-query or FLOP accounting.
- Do not describe the algorithm as a cortical implementation if simultaneous perturbation or
supervision assumptions remain biologically unsupported.
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