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# SDIL project introduction deck: visual contract
- **Artifact:** Seven-slide, 16:9 English project introduction deck.
- **Audience:** Machine-learning researchers new to SDIL.
- **Core claim:** A local learner can use the soma-unpredicted component of a mixed apical signal; residualization protects an inherited scalable credit path from predictable traffic.
- **Reader questions:** What problem does mixed feedback create? How does the learning rule work? Is residualization load-bearing? Does the combined system scale? Which component supplies each capability?
- **Evidence layers:** biological motivation and rule (slide 2), causal ablation (slide 3), standard-ResNet confirmation (slide 4), standard-depth scaling (slide 5), dynamical-task evidence (slide 6), attribution and next experiment (slide 7).
- **Source figures:** `results/figs/figure3_innovation.png`, `results/figs/figure4_resnet_confirmation.png`, `results/figs/figure5_bci_v2.png`, and `results/figs/figure6_standard_depth_scaling.png`.
- **Statistics:** Five seeds for the controlled traffic and ResNet panels; six task clusters by five model seeds for the synthetic BCI panel.
- **Visual grammar:** White background, black text, plain typography, full experimental figures, and cropped experimental panels. One equation slide carries the method. The deck adds zero process diagrams and zero redrawn result charts.
- **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.
- **Attribution:** Reciprocal KP supplies clean-setting scale. SDIL supplies soma-conditioned traffic removal and the associated mechanism evidence.
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