Add pre-registration draft, timeline skeleton and AI-use log

Claude-Session: https://claude.ai/code/session_01S8KN7NuUCm8nXLmy6eDuVv

06-experiments/preregistration.md

@@ -0,0 +1,60 @@
+# Pre-registration — QK-norm at <100M parameters
+
+*Draft v0.1, 28/10/2026. Write the final version **before** the main sweep, commit it, and don't
+edit it afterwards: changes go in the amendments table at the bottom, with a date and reason.
+The commit timestamp is the proof of when it was registered.*
+
+Supervisor: "You have a question 1, and then you will have an answer 1 (or a set?) — i.e. does this
+happen? If so, does this hold?" Each hypothesis below says what result would support it and what
+would count against it, so a surprise is recognisable as a surprise.
+
+## Research question
+
+> At <100M parameters, does isolated QK-norm improve training convergence and raise the maximum
+> stable learning rate — and at what learning rate does it become necessary?
+> Sub-question: how does that threshold shift with model depth?
+
+*(Wording from the notebook, 21/08/2026. See `02-literature/protocol.md` section 1 for a "how" rewrite to consider.)*
+
+## Hypotheses (from the notebook, 21/08/2026)
+
+| | Hypothesis | Supported if… | Not supported if… |
+|---|---|---|---|
+| H1 | QK-norm's effect is LR-conditional: negligible or slightly negative at low LR, decisive at high LR | `[Your call: e.g. |Δ final val loss| < X at the lowest LRs AND QK-on clearly better or alone stable at the highest LRs]` | `[…]` |
+| H2 | QK-norm raises the maximum stable LR and lowers LR-sensitivity | `[e.g. max stable LR(on) > max stable LR(off) across all seeds; LR-sensitivity(on) < (off)]` | `[…]` |
+| H3 | The LR at which QK-norm becomes necessary falls as depth increases | `[e.g. the threshold LR decreases monotonically over the depths tested]` | `[…]` |
+
+## Definitions to fix before running
+
+These need to be decided up front, because they change the answer. Suggestions follow the
+literature in `02-literature/`; the choices are yours.
+
+| Term | Options / suggestion | Decision |
+|---|---|---|
+| **Diverged** | loss NaN/inf, or final loss above the initial loss (Wortsman et al. use a final-loss threshold) | `[ ]` |
+| **Max stable LR** | largest grid LR where all seeds finish without diverging | `[ ]` |
+| **LR-sensitivity** | Wortsman et al. (2023): expected deviation of final loss from the optimum over an LR range, `E_η[min(ℓ(η), ℓ₀) − ℓ*]` — **check the exact formula in the paper** | `[ ]` |
+| **Becomes necessary** | lowest LR where QK-off diverges (or is worse by > X) and QK-on doesn't | `[ ]` |
+| **Convergence speed** | steps to reach val loss L* | `[ ]` |
+| Model sizes / depths | e.g. depth ∈ {2, 4, 8, 12} at fixed width; report non-embedding params | `[ ]` |
+| LR grid | e.g. log-spaced, 3e-4 … 3e-1, ~10 points ("dense" vs prior 3-point sweeps) | `[ ]` |
+| Seeds | ≥ 2 per cell (notebook says 2); 3 if time allows | `[ ]` |
+| Tokens / steps per run | fixed across all cells; constrained by the ~1–1.5 h/run budget | `[ ]` |
+| Held fixed | AdamW (β=0.9, 0.95), wd 0.1, warmup, cosine schedule, batch, grad-clip 1.0, μP off, fp32 | `[ ]` |
+| Mechanism metrics | max attention logit, attention entropy, activation kurtosis (needs logging hooks, not built yet) | `[ ]` |
+
+## Analysis plan
+
+- Plot final val loss vs LR (log axis), QK on/off, one panel per depth, seeds as points.
+- Report divergence counts per cell.
+- `[Your call: any statistical test, or descriptive only + seed spread.]`
+
+## Exploratory (not pre-registered)
+
+Anything noticed during the runs that isn't H1–H3 goes here, clearly labelled as exploratory.
+
+## Amendments
+
+| Date | Change | Reason |
+|---|---|---|
+| | | |