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+# SDIL project introduction deck: visual contract
+
+- **Artifact:** Seven-slide, 16:9 project introduction deck.
+- **Audience:** Machine-learning researchers with no prior SDIL context.
+- **Core claim:** A local learner should use the soma-unpredicted component of a mixed apical signal; this residualization protects an inherited scalable credit path from predictable traffic.
+- **Reader questions:** Why is raw feedback insufficient? What exactly is new? Is the update BP-free? Is residualization necessary? Does the combined method scale? Which evidence is controlled or synthetic?
+- **Evidence layers:** problem and mechanism (slides 2–3), causal ablation (slide 4), standard-depth scaling and cost (slide 5), dynamical-task evidence (slide 6), attribution and boundary (slide 7).
+- **Source data:** `results/figs/figure3_innovation.png`, `results/figs/figure5_bci_v2.png`, `results/oral_a_dynamic_scaling_v2_gate.json`, and the evidence-bound manuscript.
+- **Statistics:** Five seeds for the controlled traffic and ResNet panels; six task clusters by five model seeds for the synthetic BCI panel. Values shown are audited means or paired outcomes already reported in the manuscript.
+- **Visual grammar:** Direct mechanism diagrams, two result figures, and editable line charts. SDIL is blue, reciprocal KP is green, DFA is orange, BP is dark gray.
+- **Exact method labels:** SDIL, raw apical signal, somatic prediction, innovation, reciprocal KP, BP, DFA, local update, neutral observation.
+- **Output:** Editable PPTX, PDF export, generation source, and rendered QA contact sheet.
+- **Claim boundary:** Clean scaling is attributed to reciprocal KP; SDIL-specific evidence is robustness under predictable mixed traffic and the synthetic BCI mechanism test.
+