# Notes This folder collects working notes for the project tentatively titled: **Distributional Capacity Bounds for Feedback Alignment in Multilayer Perceptrons** Use this directory as the project memory. Keep rough ideas here before promoting them into a paper draft or experiments. ## Start here (current state) - [36_evidence_ledger.md](36_evidence_ledger.md): **the writing base** — full evidence chain, corrected headline numbers, figure sources, claim discipline. - [38_mnist_real_data_validation.md](38_mnist_real_data_validation.md): e0 theorem + estimator replicated on MNIST (no fit). - [39_teacher_test_gap.md](39_teacher_test_gap.md): teacher-task test gap — optimization cost invariant, test effect task-dependent (sign flip). - [37_wrapup_corrections.md](37_wrapup_corrections.md): pre-writing audit fixes (e0 sampler now real FA backward; closed-form comparison now data/init-matched — corr 0.977 retired, 0.933 matched / 0.982 ensemble). - [35_current_results_consolidated.md](35_current_results_consolidated.md): contribution snapshot (numbers corrected per note 37). - [34_closed_form_soft_ramp_theorem.md](34_closed_form_soft_ramp_theorem.md): closed-form gap law that quantifies the burden and explains the soft ramp. - [18_contribution_roadmap.md](18_contribution_roadmap.md): contribution roadmap (includes the tangent-operator estimator). ## Files - [00_project_overview.md](00_project_overview.md): high-level thesis, contribution map, and scope. - [01_theory_notes.md](01_theory_notes.md): notation, theorem candidates, proof sketches, and open theoretical gaps. - [02_experiment_notes.md](02_experiment_notes.md): validation plan, metrics, datasets, and plotting ideas. - [03_paper_outline.md](03_paper_outline.md): draft paper structure and section-level writing plan. ## Note Conventions - Prefer formulas and precise claims over prose-only ideas. - Mark unproven statements as `Conjecture` or `Hypothesis`. - Mark results ready for paper text as `Candidate theorem`, `Candidate proposition`, or `Candidate corollary`. - Keep experiment ideas close to the metric they test. - When a note depends on an assumption, write the assumption explicitly.