# 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.