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

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

00-supervisor/timeline.md

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+# Thesis timeline
+
+*Skeleton, 28/10/2026. Supervisor: "timeline up to me". Interim and final deadlines aren't
+known yet, so get them from the handbook / supervisor, then fill in the dates.*
+
+## Fixed dates (to find out)
+
+| Milestone | Date | Source |
+|---|---|---|
+| Interim report / review 1 | `[ ]` | |
+| Interim report / review 2 | `[ ]` | |
+| Thesis submission | `[ ]` | |
+| Viva / presentation | `[ ]` | |
+
+## Plan
+
+| Phase | What "done" means | Status | Target |
+|---|---|---|---|
+| Organise project | `thesis/` repo, git | ✅ 28/10 | |
+| Lit: protocol | `02-literature/protocol.md` reviewed and owned | draft ✅ | `[ ]` |
+| Lit: survey + snowball round 1 | screening decisions made in `screening.csv` | AI first pass ✅, your decisions pending | next meeting |
+| Lit: snowball round 2 | from the `*` includes | not started | `[ ]` |
+| Lit: extraction table | full-text read of includes, "how mine differs" for each `*` | not started | `[ ]` |
+| Revisit research questions | after extraction | | `[ ]` |
+| Tech spike | nanoGPT + TinyStories on MPS, run logging | ✅ 28/10 (smoke tests only) | |
+| Pre-registration | `06-experiments/preregistration.md` decisions filled + committed | draft ✅ | `[ ]` before main runs |
+| Pilot | time one full-size run; fix per-run budget | | `[ ]` |
+| Mechanism logging | attention-logit / entropy / kurtosis hooks | | `[ ]` |
+| Main sweep | QK on/off × LR grid × depth × seeds | | `[ ]` |
+| Analysis + write-up | | | `[ ]` |
+
+## Next meeting
+
+- [ ] survey for lit done (screening decisions)
+- [ ] started on snowball (round 1 done; show the flow table in protocol.md section 7)
+- [ ] timeline (this file, with dates)
+- [ ] tech spike: done early, discuss
+- [ ] send anything for him to read **early**, e.g. `protocol.md`

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 |
+|---|---|---|
+| | | |

AI-USE-LOG.md

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+# GenAI use log
+
+Record of significant AI-assisted sessions on the thesis, as with the CS6513 notebook's
+transparency log. `[Check the thesis handbook's GenAI policy and adjust this format to match.]`
+
+| Date | Tool | What it did | What I (Cormac) decided / checked |
+|---|---|---|---|
+| 28/10/2026 | Claude Code (Opus 5.5) | Organised files into `thesis/` + git. Generated `references.bib` from arXiv metadata and found reference errors in the CS6513 paper (`citation-check.md`). Wrote literature-search scripts; ran logged searches (OpenAlex, arXiv) and one snowball round (Semantic Scholar); drafted `protocol.md`; made a first-pass title/abstract screening suggestion (`ai_proposal` column). Set up nanoGPT + TinyStories + run logging (`06-experiments/`); added the `qk_norm` flag; ran 4 smoke-test runs. Drafted `preregistration.md` and `timeline.md` skeletons | `[Your note]` |

README.md

@@ -3,21 +3,25 @@
 **Working title:** When Does a Small Language Model Need QK-Norm? An Isolated
 Learning-Rate Characterization of Query–Key Normalization at <100M Parameters
 
-Organised copy of the project, made 28/10/2026. Everything here is a **copy** — the
-originals are still in the parent folder, and the CS6513 git repo
+Organised copy of the project, made 28/10/2026, and tracked in its own git repo (no remote yet).
+The originals are still in the parent folder, and the CS6513 git repo
 (`../Research Project/`, github.com/NikScorch/MSc_Research_Project) is untouched.
-This folder is not a git repo yet.
+
+Not in git (see `.gitignore`): the pitch video (52 MB), `05-course-materials/`, datasets,
+checkpoints, the API cache and the AI-assisted `ref_draft`.
 
 ## Layout
 
 | Folder | What's in it |
 |---|---|
-| `00-supervisor/` | Supervisor meeting notes and to-dos |
+| `00-supervisor/` | Supervisor meeting notes and to-dos, `timeline.md` |
 | `01-proposal/` | CS6513 proposal pitch: Marp slides (`pitch-slides.md`), script, exported PDF, recorded video |
-| `02-literature/` | `qknorm-research-findings.md` — prior-art scan and novelty verdict (24/07/2026) |
+| `02-literature/` | Lit review: `protocol.md`, search log, screening sheet, extraction table, `references.bib` — see its README |
 | `03-cs6513-paper/` | CS6513 Introduction + Lit Review paper (IEEE format), see below |
 | `04-cs6513-notebook/` | CS6513 research notebook (`.rmd` source + exported PDF) |
 | `05-course-materials/` | CS6513 lecture slides and set readings (wk1 methods, wk2 lit review incl. Xiao 2017 SLR guidance) |
+| `06-experiments/` | nanoGPT + TinyStories, QK-norm toggle, automatic run logging, `preregistration.md` — see its README |
+| `AI-USE-LOG.md` | GenAI transparency log |
 
 ### `03-cs6513-paper/` — which file is which