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

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

06-experiments/README.md

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+# 06-experiments
+
+Plain nanoGPT + TinyStories on an M4 Pro (PyTorch MPS), with QK-norm as a single on/off switch
+and every run logged automatically.
+
+| Path | What |
+|---|---|
+| `preregistration.md` | Hypotheses and how each will be tested, written **before** the main runs |
+| `run.py` | Launch a run. Writes `runs/<id>/run.json` + metrics and adds a row to `runs/index.csv` |
+| `configs/` | Run configs (`spike.py` = tech-spike smoke test only) |
+| `nanogpt/` | Vendored nanoGPT; `README-thesis.md` lists every change from upstream |
+| `data/prepare_tinystories.py` | Downloads + tokenises TinyStories, records SHA-256 checksums |
+| `runs/` | One folder per run (`run.json`, `config.json`, `metrics.jsonl` tracked; checkpoints and raw stdout ignored) |
+| `runs/index.csv` | One row per run: status, QK-norm, LR, depth, seed, best val loss, diverged-at, commit |
+
+## Setup (once)
+
+```sh
+cd 06-experiments
+uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -r requirements.txt
+.venv/bin/python data/prepare_tinystories.py --spike   # 22 MB, ~1 min
+.venv/bin/python data/prepare_tinystories.py           # full dataset, ~2.2 GB download
+```
+
+## Running
+
+```sh
+.venv/bin/python run.py my-run-name configs/spike.py qk_norm=True learning_rate=3e-3 seed=1 \
+    --note "why I'm running this"
+```
+
+**Habit:** commit before running. `run.py` records the commit SHA. If there are uncommitted
+changes, it saves them as `diff.patch` in the run folder, so a result can always be traced back
+to the exact code, but a clean commit is easier to cite.
+
+## What gets logged (`run.json`)
+
+name, note, status (`completed` / `diverged` / `failed` / `interrupted`), start/end time, exact
+command, requested + resolved config (LR, size, iters, batch, seed, qk_norm…), git commit +
+dirty flag, upstream nanoGPT commit, dataset name + SHA-256 of raw files + token counts,
+machine / macOS / Python / torch versions, and a summary (param count, first/last/best loss,
+iteration where it diverged).
+
+## Tech-spike results (28/10/2026)
+
+See `runs/index.csv`. Pipeline check only: tiny model, 300 steps, small data subset.
+
+## Known issues / decisions still open
+
+- **Vocabulary size dominates the parameter count.** With GPT-2's 50k BPE vocab, a 4-layer
+  256-wide model has ~16M params, ~13M of them in the embedding. For a "10–15M param" model the
+  non-embedding count is far smaller than it sounds. `[Your call: report non-embedding params
+  (as Kaplan et al. and Wortsman et al. do), and/or train a smaller tokenizer on TinyStories.]`
+- MPS runs in float32 (nanoGPT only autocasts on CUDA). Fine for this study; note it in methods.
+- nanoGPT's `estimate_mfu` assumes an A100, so the `mfu` number in the logs is meaningless on a Mac.

06-experiments/preregistration.md

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+# 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 |
+|---|---|---|
+| | | |