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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, Dual Prop,
+and a burst/dendritic credit-assignment method such as BurstCCN. 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. The oral-level target is a memorable joint result:
+near-BP accuracy in a genuinely deep standard model, a win over the strongest local baseline, and
+a nondominated accuracy–hardware-independent-cost point. Scale alone is not enough if C1 does not
+show that somato-dendritic innovation is the load-bearing operation.
+
+## 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.