Organise MSc thesis project into one folder

.gitignore

@@ -0,0 +1,38 @@
+# macOS / editors
+.DS_Store
+.obsidian/
+
+# Large or third-party files (kept locally, not in the repo)
+*.mp4
+05-course-materials/
+
+# LaTeX build artifacts
+*.aux
+*.log
+*.out
+*.toc
+*.fls
+*.fdb_latexmk
+*.synctex.gz
+*.bbl
+*.blg
+*.run.xml
+*.bcf
+
+# Personal AI-assisted reference draft — never for submission
+03-cs6513-paper/ref_draft.tex
+03-cs6513-paper/ref_draft.pdf
+
+# Python
+__pycache__/
+*.pyc
+.venv/
+
+# Experiment data, checkpoints and raw outputs (run metadata in runs/ IS tracked)
+06-experiments/data/
+06-experiments/vendor/
+06-experiments/runs/*/ckpt.pt
+06-experiments/runs/*/stdout.log
+
+# Literature-search API cache
+02-literature/search/.cache/

00-supervisor/meeting-notes-todo.md

@@ -0,0 +1,69 @@
+# Supervisor meeting notes / to-do
+
+*Copied verbatim from Cormac's notes, 28/10/2026. Meeting date: [fill in].*
+
+SLR
+MLR
+
+Zotero
+Endnote
+Better bib tex/text/tec
+
+Paired down sir protocol
+
+- review question
+- seed set
+- search strings
+— archive, google scholar
+- snowballing
+— what did these guys cite? So on so forth
+—  can have criteria for exclusion,
+— probably nothing useful on qknorm pre 2020, can haver a cut off from before that,
+— same goes for Attention is all you need
+
+- extraction table
+— here's a "roper paper",
+—— what claim, what questions were asked,
+- what questions are people asking across the literature,
+— is there anything similar to my research question? Put an Asterix on it
+— how does my question differentiate from theirs?
+— maybe im asked for a small model but theyre asking for a large one, maybe its a cost difference
+— if you do find something similar, make sure you can give two sentences on why your paper is different
+
+- in an interview, can defend your lit review by saying
+— this is how I found these papers,
+— this is how
+
+Semantic scholar
+Some automated tooling
+If you want to use llms for paper search, find a paper that backs up the tactic
+
+Based on what you read/find, review if you want to update/change any of your questions.
+
+How to ask questions that give you the scope that you want,
+Ask how not if
+
+Because theres an experimental component, you could find an interesting surprise during the experiment.
+Pre register hypothesis
+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?
+
+Whenever you do an experimental run, log it: learning rate, size, epochs, dataset, so on so forth
+- make sure you are never in the situation where you are reviewing your work and you cant remember how you came to a result
+- get into a habit of documenting experimental runs
+- perhaps automate it in a nice json/yaml format, with commit SHAs?
+
+"Technical spike", a day to get tech stuff set up.
+- nanogpt, tiny stories, etc
+- get a scaffold setup
+
+Get dates for due interims and so on
+
+Goal for next day:
+- survey for lit done
+- started on snowball
+- timeline up to me
+- maybe I want to do tech spike later/earlier
+— chat about that the next day
+- if theres something I want him to read, send it onto him (early)

01-proposal/pitch-script.md

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+# 5-Minute Pitch — Narration Script (DRAFT)
+
+**How to use this:** this is a *scaffold in your voice* — read it aloud, cut what feels unnatural,
+and make the wording your own before recording. The video is *you* presenting, so re-voicing it is
+expected. Log this AI-assisted drafting in the notebook's GenAI Transparency Log (Section 3).
+
+**Framing note:** this is an honest **verification + boundary-charting** study, *not* a novelty claim.
+Do not say "first," "nobody has studied QK-norm at small scale," or "resolves a contradiction" — a
+panel that knows Wortsman (ICLR 2024) or IMU-1 (2026) would push back. The defensible line is *pinning down
+**when** a small model needs QK-norm, with a clean reproducible sweep.*
+
+Target ≈ 700 words ≈ 5 min at a calm pace. Timings are cumulative.
+
+---
+
+**[Title — 0:00–0:15]**
+Hi, I'm Cormac Shannon. We already know that a popular, cheap architectural trick called QK-norm helps
+stabilize language-model training. My study asks a sharper, unanswered question: *when* does a small
+model actually need it — and I'll answer that with a clean, reproducible experiment.
+
+**[Slide 1 · Problem & Topic — 0:15–1:05]** Modern language models are a stack of architectural components — rotary embeddings, RMSNorm, grouped-query attention, QK-norm, and more. QK-norm simply normalizes the query and key vectors before the attention step, so the attention scores can't blow up. Now, it's *established* that this helps
+stability, especially at high learning rates — and that even holds on small models; a 2023 study swept
+learning rates at small scale, and in a 2026 paper QK-norm was the single biggest architectural win in
+seventy-million-parameter ablations. So I'm not claiming to discover that — the qualitative rule is
+already known. What's genuinely fuzzy is the *small-scale detail*: the evidence is patchy and measured
+inconsistently — some at larger scale, some using looser signals like activation spikiness or a single 
+"roughly one-and-a-half-times learning rate" number. What hasn't been done is a clean, dense
+learning-rate curve for QK-norm in isolation at ten to a hundred million parameters, and mapping how the
+point where it becomes necessary shifts as the model gets deeper.
+
+**[Slide 2 · Significance — 1:05–1:55]**
+Why does this matter? First, the field is moving toward small models — for on-device use, cost, energy,
+and reproducibility — yet the design rules for that regime are under-studied. Second, and this is the
+real payoff: if I can map the learning-rate and depth threshold where QK-norm becomes necessary, a
+small-model builder gets a concrete rule — below this learning rate you can drop it, above it you can't.
+The qualitative flip is known, but that precise threshold — and how it shifts with depth — hasn't been
+charted at this scale. Third, on evidence quality: a proper dense learning-rate
+sweep puts the shaky small-scale claims on firmer footing. And finally, because the whole thing runs on
+a laptop, I can release it openly — adding reproducible, re-runnable evidence to a literature dominated
+by expensive results almost nobody can reproduce.
+
+**[Slide 3 · Conceptual Framework — 1:55–2:45]**
+So here's my framework. My primary question: at under a hundred million parameters, does isolated
+QK-norm's stability benefit reproduce under a dense learning-rate sweep — and at what learning rate does
+it actually become necessary? My sub-question: how does that necessity threshold shift as the model gets
+deeper? That gives three testable hypotheses. One: the benefit is *learning-rate-conditional* —
+negligible, or even slightly negative, at low learning rates, but decisive at high ones. Two: QK-norm
+raises the maximum stable learning rate and lowers learning-rate sensitivity. Three: the threshold where
+it becomes necessary falls as depth increases — deeper models need it sooner.
+
+**[Slide 4 · Methodology — 2:45–3:35]**
+Methodologically this is a quantitative, controlled ablation, in Creswell's terms. One independent
+variable: QK-norm on or off. Two moderators: the learning rate, swept densely from safe to divergent,
+and model depth at a couple of values. Everything else is held fixed — width, data, tokens, optimizer,
+seeds — ~~and, importantly, I switch *off* maximal-update parametrization, because that technique
+transfers learning rates by design and would confound my result~~. The model is a plain nanoGPT-style
+transformer, ten to fifteen million parameters, on an open dataset like TinyStories. I measure the
+maximum stable learning rate, learning-rate sensitivity, steps-to-target-loss, perplexity, and — as
+mechanistic checks — attention entropy and activation spikiness, repeated over at least two seeds.
+
+**[Slide 5 · Feasibility & Timeline — 3:35–4:20]**
+On feasibility: this fits my hardware — an M4 Pro with forty-eight gigabytes, using PyTorch's MPS
+backend. A model this size takes roughly an hour to an hour and a half per run. My core matrix — QK-norm
+on or off, six learning rates, two seeds — is about twenty-four runs, a couple of overnight batches. The
+depth-threshold part is a stretch goal, a few more runs, or a rented GPU for a few dollars. I'm
+deliberately *not* training at GPT-2 scale, because an ablation only needs relative effects, so tiny
+models are the correct choice. The timeline is one milestone a week: implementation and baseline in week
+five, the core sweep in week six, analysis and the depth runs in week seven, and writing up in week eight.
+
+**[Slide 6 · Ethics & Data — 4:20–4:45]**
+Ethically this is low-risk. No human subjects and no personal or sensitive data, so no approval
+barriers. I'm using openly-licensed public datasets with no access constraints. The compute footprint is
+only a few GPU-hours — and energy efficiency is actually part of my motivation. And everything — code,
+configs, seeds, logs — will be released for reproducibility.
+
+**[Slide 7 · Summary — 4:45–5:00]**
+So, in one line: not *whether* QK-norm helps — we know it can — but pinning down exactly *when* a small
+model needs it, with a clean, reproducible, laptop-scale experiment that firms up shaky evidence and
+hands practitioners a usable rule. Thank you, and I'm happy to take questions.
+
+---
+
+*Word count ≈ 720. If you run long, the easiest trims are the third point of Slide 2 (evidence quality)
+and the mechanistic-checks detail on Slide 4.*

01-proposal/pitch-slides.md

@@ -0,0 +1,166 @@
+---
+marp: true
+theme: default
+paginate: true
+size: 16:9
+header: 'CS6513 · Research Proposal Pitch'
+style: |
+  section { font-size: 26px; }
+  h1 { color: #1a3b5d; }
+  h2 { color: #1a3b5d; }
+  strong { color: #b0451f; }
+  section.title h1 { font-size: 46px; }
+  small { color: #666; }
+---
+
+<!-- _class: title -->
+<!-- _paginate: false -->
+<!-- _header: '' -->
+
+# When Does a Small Model *Need* QK-Norm?
+
+## A reproducible, isolated learning-rate characterization of Query–Key Normalization at <100M parameters
+
+**Cormac Shannon** · ID 23366303
+MSc Immersive Software Engineering · CS6513 Research Proposal Pitch
+
+<small>Provisional working title — draft pitch</small>
+
+<!-- notes: Hi, I'm Cormac. My study takes a popular, cheap architectural trick — QK-norm — and pins
+down *exactly when* a small model actually needs it, with a clean, reproducible experiment. ~15s -->
+
+---
+
+## 1 · The Problem & Topic
+
+- Modern LLMs stack many components — RoPE, RMSNorm, GQA, SwiGLU, **QK-norm** — mostly validated
+  at **large scale**. QK-norm normalises the query & key vectors so attention logits can't explode.
+- **What's already known:** QK-norm improves training stability, *especially at high learning rates* —
+  shown by dense LR-sweeps but with QK-norm **bundled** with other fixes (Wortsman, ICLR 2024), and as
+  the **top single architectural win** in 70M ablations (IMU-1, 2026 — by cutting extreme activation outliers, *not* an LR sweep).
+- **What's still fuzzy — my focus:** the *qualitative* rule is known; a **clean, dense measurement is not**.
+  Small-scale evidence is **patchy and inconsistently measured** (larger-scale proxies; looser signals —
+  extreme activation outliers, a single "≈1.5× LR" figure, a coarse 3-point blog sweep at 1B). **Un-charted: the dense
+  LR-sensitivity curve for *isolated* QK-norm at 10–100M, and how the LR at which it becomes necessary
+  moves with model depth.**
+
+<!-- notes: Modern architectures pile on tricks proven on big models. QK-norm is one, and we already
+know it helps stability at high learning rates — even in small models. What's NOT nailed down is the
+precise small-scale picture: a clean dense learning-rate curve, and the exact point it becomes
+necessary. That's my gap. ~50s -->
+
+---
+
+## 2 · Significance — the "So What?"
+
+- **The field is going small** — on-device/edge, cost, energy, reproducibility all push toward
+  sub-100M LMs. Practitioners need concrete rules for *that* regime.
+- **A usable rule of thumb:** if I can map the learning-rate (× depth) threshold where QK-norm flips
+  from *harmless-to-drop* to *necessary*, a small-model builder gets a direct answer — "below this LR
+  you can skip it; above it you can't." The qualitative flip is known; the **precise threshold — and how
+  it moves with depth — isn't charted at this scale.**
+- **Evidence quality:** current small-scale results use *looser* metrics; a **dense LR-sensitivity
+  sweep** (à la Wortsman) puts the small-model claim on firmer footing.
+- **Reproducibility:** a **laptop-runnable, open** artifact adds trustworthy, re-runnable evidence to a
+  literature dominated by expensive, hard-to-reproduce large-scale results.
+
+<!-- notes: Why care? Small models are where deployment is heading. If I can pin the learning-rate and
+depth threshold where QK-norm becomes necessary, that's a concrete rule builders can use — and nobody's
+charted it. Plus a laptop-reproducible artifact strengthens shaky small-scale evidence. ~55s -->
+
+---
+
+## 3 · Conceptual Framework — Questions & Objectives
+
+**Primary RQ.** At <100M parameters, does *isolated* QK-norm's stability benefit reproduce under a
+**dense learning-rate sweep**, and **at what learning rate does it become necessary?**
+
+**Sub-RQ.** How does that **necessity threshold shift with model depth?**
+
+**Hypotheses**
+- **H1** — QK-norm's effect is **LR-conditional**: negligible/slightly negative at low LR, decisive at high LR.
+- **H2** — QK-norm **raises the max stable LR** and **lowers LR-sensitivity** at <100M.
+- **H3** — the LR at which QK-norm becomes necessary **falls as depth increases** (deeper ⇒ needs it sooner).
+
+**Objectives:** isolate a muP-free QK-norm toggle → dense LR sweep × a few depths → quantify
+max-stable-LR, LR-sensitivity, activation outliers → **chart the LR × depth necessity boundary** → release the artifact.
+
+<!-- notes: The core question isn't "does it help" — we know it can — it's *when*. Does the effect hold
+under a proper dense sweep at small scale, at what learning rate does it become necessary, and how does
+that move with depth. Three hypotheses built around that LR-conditionality. ~50s -->
+
+---
+
+## 4 · Methodological Approach
+
+**Quantitative, experimental — a controlled ablation** (per Creswell, Ch. 1).
+
+| Element | Choice |
+|:--|:--|
+| Independent variable | QK-norm **{on, off}** |
+| Moderators | **Learning rate** (dense, log-spaced, safe → divergent) · **depth** (2–3 values) |
+| Controls | identical width/data/tokens/optimizer (AdamW) · **μP explicitly OFF** · fixed seeds |
+| Model | plain **nanoGPT** decoder, **~10–15M params** |
+| Data | TinyStories / FineWeb-Edu slice (fixed token budget) |
+| Measures | **max stable LR** · **LR-sensitivity** (Wortsman) · steps-to-threshold · perplexity · attention entropy · extreme activation outliers |
+
+**Analysis:** loss-vs-LR curves per depth (≥2 seeds, mean ± spread); locate the divergence boundary and
+the QK-norm necessity threshold. Base = **plain nanoGPT, μP off** so QK-norm is the *only* variable.
+
+<!-- notes: A clean quantitative experiment: one thing changes — QK-norm on or off — across a dense
+learning-rate sweep at a couple of depths, everything else fixed, and crucially with muP switched off so
+I'm not confounding the learning-rate effect. ~55s -->
+
+---
+
+## 5 · Feasibility & Timeline
+
+- **Runs on my hardware:** M4 Pro (48 GB), PyTorch **MPS**. ~10–15M model on ~100–150M tokens ≈
+  **~1–1.5 h/run**. **Core** matrix: QK{2} × LR{6} × seed{2} ≈ **24 runs** → 2–3 overnight batches.
+- **Stretch** (the depth boundary): add 2 more depths near the threshold (~+18 runs) — or a rented
+  RTX 4090 (~\$0.40/hr) clears it in a few dollars.
+- **Not** GPT-2 scale — an ablation needs *relative* effects, so tiny models are correct, not a compromise.
+
+| Week | Milestone |
+|:--|:--|
+| **5** | Fork nanoGPT · isolated μP-off QK-norm toggle · reproduce baseline · pilot runs |
+| **6** | Core dense LR sweep (≥2 seeds) · log curves |
+| **7** | Analysis + (stretch) depth-threshold runs · figures |
+| **8** | Write Literature Review + Introduction · finalise notebook |
+
+<!-- notes: This fits my laptop: tiny models, about an hour a run, ~24 runs for the core over a couple
+of nights, with the depth boundary as a stretch goal or a few dollars of cloud GPU. Four weeks, one
+milestone each. ~50s -->
+
+---
+
+## 6 · Ethics & Data Access
+
+- **No human subjects, no personal or sensitive data** → no ethical-approval barriers.
+- **Public, openly-licensed datasets** (TinyStories, FineWeb-Edu) — licences to be confirmed and
+  respected; **no data-access constraints.**
+- **Low environmental footprint** — a few GPU-hours total; energy-efficiency is itself a study motivation.
+- **Reproducibility & integrity:** code, configs, seeds, and logs released openly; AI-tool use declared
+  in the research notebook per module policy.
+
+<!-- notes: Ethically low-risk: no people, no private data, only open datasets. The compute footprint is
+tiny — which is part of the point — and everything is released for reproducibility. ~35s -->
+
+---
+
+## 7 · Summary
+
+- **Not "does QK-norm help" (known) — but *when a small model needs it*, charted cleanly.**
+- A **dense, isolated, μP-free** LR sweep at <100M → the **max-stable-LR / LR-sensitivity** benefit *and*
+  the **LR × depth necessity boundary**, **reproducible on a laptop.**
+- An honest **verification-plus-boundary-charting** study: firms up patchy small-scale evidence and
+  hands practitioners a usable rule.
+
+### Thank you — questions welcome
+
+<small>Key references: Henry et al. 2020 · Dehghani et al. 2023 (ViT-22B) · Wortsman et al. (ICLR 2024) ·
+IMU-1 (2026) · OLMo 2 (2025) — *venues/details to verify against primary sources*</small>
+
+<!-- notes: In short: not whether QK-norm helps — we know it can — but pinning down exactly when a small
+model needs it, with a clean, reproducible, laptop-scale experiment that firms up shaky evidence and
+gives builders a usable rule. Thank you. ~30s -->

02-literature/qknorm-research-findings.md

@@ -0,0 +1,202 @@
+# Research Findings — QK-Norm at Small Scale (Option 4)
+
+*Compiled 24/07/2026. Working file for later review — not final notebook text.*
+*Updated 24/07/2026 after a second prior-art pass — see NOVELTY VERDICT.*
+
+**Study in one line:** At <100M parameters, characterise — with a *dense* learning-rate sweep —
+whether isolated Query–Key Normalization (QK-norm) widens the band of *stable* learning rates and
+speeds convergence, on commodity hardware.
+
+**Design axis:** independent variable = QK-norm {on, off}; moderator = learning rate (dense sweep,
+safe → divergent); dependent = convergence speed (steps-to-threshold), max stable LR / LR-sensitivity,
+final val perplexity, attention entropy + activation kurtosis (mechanism). Base = plain nanoGPT,
+**muP explicitly OFF**, M4 Pro, ~10–15M params, TinyStories / FineWeb-Edu.
+
+---
+
+## ⚠ NOVELTY VERDICT (read first) — updated after 2nd search pass
+
+**Blunt assessment: this is a *replication / verification* study, not a novel contribution.**
+Two rounds of searching show the effect and even the nuanced reconciliation are already established:
+
+- QK-norm's LR-stabilisation is **extensively documented**: rigorously by **Wortsman et al. 2023**
+  (formal LR-sensitivity, small→4.8B), at **70M** by **IMU-1 (2026)** where it's the *single largest*
+  architectural win, at **830M** by 2410.16682 ("~1.5× higher LR"), and in **181M–1.3B** ablations
+  (SimpleGPT / "Controlling changes to attention logits").
+- **The LR-conditional reconciliation I was going to claim as the "gap" is already the community view:**
+  a 360M analysis shows QK-norm gives *no benefit (or slightly worse) at low LR* but *prevents
+  divergence at high LR* — i.e. "shallow small models don't need it unless you push the LR." That is
+  now the accepted mechanism, not an open question.
+- A **blog (aflah02)** already isolated QK-norm and ran a (coarse, 3-point) LR sweep at **1B / 100B tokens**.
+
+**What genuinely remains un-done (thin, incremental, but real):**
+1. A **dense** LR-sensitivity *curve* for **isolated, muP-free** QK-norm specifically in the **10–100M**
+   band. Wortsman is larger & partly bundled; IMU-1 uses kurtosis not a sweep & bundles muP; the blog is
+   1B & coarse. Nobody has published the clean small-scale swept curve.
+2. **Laptop-reproducible artifact** (open code/logs/seeds runnable on an M4). Soft, but true.
+
+**Honest recommendation:** for a *research-methods module*, a well-executed reproduction/verification
+study is legitimate and low-risk — it demonstrates method, which is what's being graded. But the pitch
+must be framed as **"a rigorous, isolated, commodity-hardware verification of a known effect,"** NOT as
+a discovery. Do **not** claim novelty of technique, "first at small scale," or "resolves a contradiction."
+
+**If more novelty is wanted (optional pivot for the parent to decide):**
+- Cross QK-norm with a *less-saturated* axis — e.g. its **interaction with positional encoding**
+  (NoPE/RoPE) or with **depth/width** at fixed budget, where the LR story is less mapped; or
+- Study the **precise LR threshold** at which QK-norm flips from "slightly harmful" to "necessary" as a
+  function of model depth (a small, clean, and *not-yet-charted* boundary) — this is the most defensible
+  sharpening that stays within the current setup.
+
+---
+
+## A. CORE ACADEMIC PAPERS
+
+### 1. Henry et al. (2020) — "Query-Key Normalization for Transformers"  *(origin of QKNorm)*
+- EMNLP 2020 Findings. arXiv:2010.04245 — https://arxiv.org/abs/2010.04245
+- ℓ2-normalise Q and K before the dot product; replace 1/√d with a learnable g. For low-resource NMT.
+- **Relevance:** the technique's origin & definition. Also proves QK-norm was born on *small* models.
+
+### 2. Dehghani et al. (2023) — "Scaling Vision Transformers to 22B Parameters"  *(the "why" citation)*
+- arXiv:2302.05442 — https://arxiv.org/abs/2302.05442
+- Popularised QK-norm to stop attention-logit growth / divergence at scale.
+- **Relevance:** canonical large-scale motivation.
+
+### 3. Zhai et al. (2023, ICML) — "Preventing Attention Entropy Collapse"  *(mechanism)*
+- arXiv:2303.06296 — https://arxiv.org/abs/2303.06296
+- "Entropy collapse" (spiky attention) ↔ instability. Proposes σReparam.
+- **Relevance:** the *why*; supplies **attention entropy** as a metric to log.
+
+### 4. Wortsman et al. (2023) — "Small-scale proxies for large-scale Transformer training instabilities"  *(THE prior work)*
+- ICLR 2024 **oral**. arXiv:2309.14322 — https://arxiv.org/abs/2309.14322
+- Instabilities reproduce in small models at high LR; **qk-layernorm** + **z-loss** are "equally
+  effective" small. Introduces the **LR-sensitivity** metric. Confirms with a 4.8B run.
+- **Relevance:** the methodological template *and* the reason this study is verification, not discovery.
+  Our only deltas vs. this: smaller (10–100M), isolated QK-norm, muP-off, laptop-reproducible.
+
+### 5. IMU-1 (Grigorev et al., 2026) — "Sample-Efficient Pre-training of Small Language Models"  *(closest prior art)*
+- arXiv:2602.02522 — https://arxiv.org/abs/2602.02522
+- 430M model + **70M ablations**. QK-norm is the **single largest individual architectural win**
+  (−0.63% train loss at 70M; reduces activation **kurtosis**). BUT bundled with **muP**, NorMuon,
+  value residuals, LN-scaling — so its LR effect is *entangled with muP*. Uses kurtosis, **not** an LR sweep.
+- **Relevance:** kills the "isolation at small scale is un-done" claim. Our cleaner angle: isolate + sweep,
+  muP-off. Cite as the paper we're verifying/disentangling.
+
+### 6. OLMo 2 Team (2025) — "2 OLMo 2 Furious"  *(modern adoption)*
+- arXiv:2501.00656 — https://arxiv.org/abs/2501.00656 — RMSNorm on Q,K improves loss + stability.
+  Also used by ViT-22B, Gemma 2/3, Qwen3, Chameleon.
+
+### 7. Small-model ablations that DISPUTE the benefit  *(the tension — but see verdict)*
+- codelion, "The Optimal Architecture for Small LMs": https://huggingface.co/blog/codelion/optimal-model-architecture
+  (19 configs @ ~70M/1B tokens; "modern tricks no measurable benefit at 70M").
+- "Architectural Trade-offs in SLMs Under Compute Constraints" arXiv:2512.20877 — https://arxiv.org/abs/2512.20877
+- **Relevance:** the apparent contradiction with IMU-1 — but it's **already reconciled** by the
+  LR-conditionality finding (QK-norm helps mainly at high LR / when instability would otherwise occur).
+
+## B. ADJACENT / CONTEXT
+
+- 2410.16682 "Methods of improving LLM training stability" — https://arxiv.org/abs/2410.16682 — 830M;
+  QK-norm + softmax cap → **~1.5× higher LR** before divergence.
+- QK-Normed MLA (2026) arXiv:2606.16310 — https://arxiv.org/abs/2606.16310 — head-to-head high-LR stress
+  test: QK-norm keeps training while QK-Clip NaNs.
+- SimpleGPT (2602.01212) & "Controlling changes to attention logits" (2511.21377) — 181M–1.3B QK-norm
+  LR ablations (GA-QK-Norm / QK-Norm / baseline).
+- Yang et al. (2022) μP / μTransfer arXiv:2203.03466 — LR transfer across width; the confound we hold OFF.
+- "Enhanced QKNorm with the Lp norm" arXiv:2602.05006 — a QK-norm variant (possible extension).
+- Peri-LN (2502.02732) / HybridNorm (2503.04598) — normalization-placement neighbours.
+
+## C. NON-ACADEMIC MATERIAL
+
+**Directly on-topic (assess overlap carefully)**
+- **aflah02 — "On Stability with QK Norm during Pretraining with NoPE"**: https://aflah02.substack.com/p/on-stability-with-qk-norm-during
+  — isolates QK-norm, 3-point LR sweep (4e-5/4e-4/4e-3), **Llama-3.2-1B, FineWeb-Edu 100B**. Closest blog;
+  1B & coarse & NoPE-specific, so our <100M dense sweep still differs.
+- **Ishan Mukherjee — "QK norm is probably a free lunch"**: https://ishanjmukherjee.github.io/qk-norm
+  — advocacy/survey (adoption across Llama4/Gemma3/Qwen3/DeepSeek-V3; Marin-32B loss-spike anecdote). Not empirical.
+
+**Explainers**
+- Sebastian Raschka — "The Big LLM Architecture Comparison" (§2.2 QK-Norm):
+  https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison  ← best plain-English intro
+- Sebastian Raschka — "LLM Architecture Gallery: QK-Norm": https://sebastianraschka.com/llm-architecture-gallery/qk-norm/
+- emergentmind.com: /topics/query-key-normalization-qk-norm , /topics/attention-entropy-collapse
+
+**Code (experimental substrate)**
+- karpathy/nanoGPT: https://github.com/karpathy/nanoGPT  ← RECOMMENDED clean base (muP-off, AdamW)
+- karpathy/nanochat + Issue #362 (QK-norm vs logit softcap debate): https://github.com/karpathy/nanochat/issues/362
+- KellerJordan/modded-nanogpt: https://github.com/kellerjordan/modded-nanogpt
+
+**Video / courses** *(confirm URLs before citing)*
+- Karpathy — "Let's reproduce GPT-2 (124M)", "Let's build GPT: from scratch" (YouTube).
+- Stanford CS336 "Language Modeling from Scratch".
+
+## D. SYNTHESIS — ESTABLISHED vs WHAT'S LEFT
+
+**Established (do NOT re-argue as if new):** QK-norm bounds attention-logit growth → prevents entropy
+collapse → widens the stable-LR band, *especially at high LR*; cheap; standard in frontier models. This
+reproduces at small scale (Wortsman; IMU-1 @70M; multiple 181M–1.3B ablations; a 1B blog). The
+low-LR-no-benefit / high-LR-necessary **reconciliation is already the accepted mechanism**.
+
+**What's genuinely left (thin):** a **dense LR-sensitivity curve for isolated, muP-free QK-norm in the
+10–100M band**, released as a laptop-reproducible artifact; and (sharper) **charting the LR threshold at
+which QK-norm flips from slightly-harmful to necessary vs. model depth.**
+
+**Cleanest defensible claim:** "We reproduce, with a dense LR sweep at 10–100M on commodity hardware,
+the max-stable-LR / LR-sensitivity benefit of isolated QK-norm, and locate the LR×depth boundary at
+which it becomes necessary." Framed as **verification + boundary-charting**, not discovery.
+
+## E. QUICK REFERENCE LIST (for notebook Section 2 — verify all before final use)
+- Henry, Dahl, Bhoopchand, Bica (2020). Query-Key Normalization for Transformers. EMNLP Findings. arXiv:2010.04245
+- Dehghani et al. (2023). Scaling ViTs to 22B Parameters. arXiv:2302.05442
+- Zhai et al. (2023). Preventing Attention Entropy Collapse. ICML. arXiv:2303.06296
+- Wortsman et al. (2023). Small-scale proxies for large-scale Transformer training instabilities. ICLR 2024. arXiv:2309.14322
+- Grigorev et al. (2026). IMU-1: Sample-Efficient Pre-training of Small LMs. arXiv:2602.02522
+- OLMo 2 Team (2025). 2 OLMo 2 Furious. arXiv:2501.00656
+- (2024). Methods of improving LLM training stability. arXiv:2410.16682
+- Yang et al. (2022). Tensor Programs V (μP / μTransfer). arXiv:2203.03466
+- (2025). Architectural Trade-offs in SLMs Under Compute Constraints. arXiv:2512.20877
+
+## F. SURROUNDING LITERATURE — added 18/08/2026
+
+**`[TO VERIFY]` = surfaced via web search this round; confirm each against Scopus / the primary PDF
+before use.** arXiv IDs below were taken from live result links (reliable); author names & venues
+come from search summaries (treat as unconfirmed). Papers without an arXiv ID need the ID found too.
+
+### F.1 Why attention logits explode — massive activations & attention sinks (mechanism depth)
+- Sun et al. (2024), "Massive Activations in Large Language Models," ICML 2024. arXiv:2402.17762 —
+  a few tokens develop extreme-magnitude activations → dominant key vectors → "attention sink"; the
+  logit-blow-up mechanism QK-norm targets. `[TO VERIFY]`
+- "When Attention Sink Emerges in Language Models: An Empirical View." arXiv:2410.10781 `[TO VERIFY]`
+- "The Spike, the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks" (2026).
+  arXiv:2603.05498 — "write–sink–cancel" cycle. `[TO VERIFY]`
+- "A Single Layer to Explain Them All: Understanding Massive Activations in LLMs" (2026).
+  arXiv:2605.08504 `[TO VERIFY]`
+- *Relevance:* mechanistic spine for Component 2 — links Zhai (entropy collapse) + IMU-1 (kurtosis);
+  QK-norm = magnitude control.
+
+### F.2 Competing / complementary logit-control & stability techniques (supports the "bundled" gap)
+- Attention logit soft-capping — Gemma 2 report: z ← c·tanh(z/c), c=50 (attn)/30 (final). The direct
+  alternative to QK-norm (cf. nanochat issue #362). `[TO VERIFY — find arXiv ID + exact citation]`
+- z-loss — PaLM (Chowdhery et al., 2022): auxiliary loss keeping softmax normalizer log Z ≈ 0; the fix
+  Wortsman bundles *with* qk-layernorm. `[TO VERIFY — find arXiv ID]`
+- NormFormer (Shleifer et al., 2021), extra normalization. arXiv:2110.09456 `[TO VERIFY authors]`
+- Peri-LN (2025) arXiv:2502.02732; HybridNorm (2025) arXiv:2503.04598 — normalization-placement
+  neighbours. `[TO VERIFY]`
+- *Relevance:* QK-norm is one of several logit-control methods, almost always studied bundled →
+  reinforces the isolated-comparison gap (Component 3).
+
+### F.3 Recent QK-norm-specific work (2025–2026) — it's a live topic
+- QK-Normed MLA (2026). arXiv:2606.16310 — QK-norm in Multi-head Latent Attention; reaffirms QK-norm
+  "permits significant increases in learning rate" (supports H2); proposes per-head LR scaling (QuacK)
+  where QK-norm doesn't apply. `[TO VERIFY]`
+- "Controlling changes to attention logits" (2025). arXiv:2511.21377 `[TO VERIFY]`
+- "Enhanced QKNorm with the Lp norm" (2026). arXiv:2602.05006 — QK-norm variant (possible extension).
+  `[TO VERIFY]`
+
+### F.4 Reproducible small-model pretraining & data (method + significance)
+- TinyStories (Eldan & Li, 2023). arXiv:2305.07759 — sub-10M-param models produce coherent English;
+  grounds both dataset choice and the tiny-model scale. `[TO VERIFY]`
+- Cramming (Geiping & Goldstein, ICML 2023). arXiv:2212.14034 — near-BERT on a single GPU in one day;
+  precedent for the accessible/laptop-reproducible framing. `[TO VERIFY]`
+- BabyLM Challenge (Warstadt et al.). arXiv:2301.11796 (findings arXiv:2504.08165) — sample-efficient
+  pretraining benchmark; relevant to the depth-vs-width data-constrained fallback. `[TO VERIFY]`
+- SmolLM2 (Allal et al., 2025) — over-training small models; context for why small models matter and
+  are trained long. `[TO VERIFY — find citation/arXiv ID]`

03-cs6513-paper/README.md

@@ -0,0 +1,59 @@
+# CS6513 Paper — Introduction + Mini Literature Review
+
+IEEE conference-format manuscript for CS6513. Source: `main.tex`.
+
+## Build
+
+```sh
+./paper/build.sh          # run from the repo root
+```
+
+`build.sh` auto-detects a TeX engine (and picks up MacTeX / TeX Live even if it
+isn't on your `PATH`). It tries, in order: `latexmk` → `pdflatex` (two passes) →
+`tectonic`. Output: `paper/main.pdf`.
+
+### Toolchain
+
+No system TeX install is needed if you use **tectonic** (what this repo was
+built with):
+
+```sh
+brew install tectonic
+```
+
+Tectonic is a single self-contained binary (~20 MB) that downloads the exact TeX
+packages the document needs from CTAN on first run and caches them.
+
+Alternatives:
+- **MacTeX** (full TeX Live, ~4 GB): `brew install --cask mactex-no-gui`
+- **BasicTeX** (small, ~100 MB; may need `sudo tlmgr install ...` for extras)
+- **Overleaf** (zero install): upload/clone this `paper/` folder and compile `main.tex`.
+
+## Required LaTeX packages
+
+All standard and bundled with a normal TeX distribution (and auto-fetched by
+tectonic):
+
+| Package | Why |
+|---|---|
+| `IEEEtran` (document class) | IEEE conference format — `IEEEtran.cls` is committed here so the repo is self-contained |
+| `cite` | citation handling |
+| `amsmath`, `amssymb`, `amsfonts` | math symbols (μ, ×, →, etc.) |
+| `graphicx` | figures (none yet) |
+| `textcomp` | text symbols |
+| `xcolor` | colour |
+| `url` | URLs in the bibliography |
+
+## Notes learned while building
+
+- **The first tectonic run needs a network connection** — it fetches packages
+  from CTAN. Later builds use the cache and are fast/offline.
+- **Transient `HTTP 429 Too Many Requests`** from tectonic's package server can
+  interrupt that first build. It's not a document error — **just re-run
+  `build.sh`**; tectonic resumes from what it already cached.
+- **`Underfull \hbox (badness ...)`** warnings are cosmetic — short lines (e.g.
+  bullet lists) that don't fill the justified column. Harmless, and they go away
+  as the draft becomes prose. There are currently no real errors.
+- Build artifacts (`*.aux`, `*.log`, …) and the compiled `main.pdf` are
+  git-ignored. Commit the **final** submission PDF explicitly (`git add -f`) if
+  you want it tracked.

03-cs6513-paper/build.sh

@@ -0,0 +1,50 @@
+#!/usr/bin/env bash
+# Build a CS6513 IEEE .tex file (-> matching .pdf).
+# Usage:
+#   ./paper/build.sh              # builds main.tex   (the submission)
+#   ./paper/build.sh ref_draft    # builds ref_draft.tex (personal reference)
+#   ./paper/build.sh main.tex     # .tex extension optional
+set -euo pipefail
+cd "$(dirname "$0")"
+
+# Which document to build (default: main). Accept an optional first arg, with or
+# without a trailing ".tex".
+MAIN="${1:-main}"
+MAIN="${MAIN%.tex}"
+
+if [ ! -f "$MAIN.tex" ]; then
+  echo "No such file: $MAIN.tex (in $(pwd))" >&2
+  exit 1
+fi
+
+# Pick up a TeX toolchain that may be installed but not on PATH (MacTeX / TeX Live).
+for d in /Library/TeX/texbin /usr/local/texlive/*/bin/* /opt/homebrew/bin; do
+  if [ -d "$d" ]; then PATH="$d:$PATH"; fi
+done
+export PATH
+
+if command -v latexmk >/dev/null 2>&1; then
+  echo "Building with latexmk..."
+  latexmk -pdf -interaction=nonstopmode -halt-on-error "$MAIN.tex"
+elif command -v pdflatex >/dev/null 2>&1; then
+  echo "Building with pdflatex (two passes to resolve \\cite references)..."
+  pdflatex -interaction=nonstopmode -halt-on-error "$MAIN.tex"
+  pdflatex -interaction=nonstopmode -halt-on-error "$MAIN.tex"
+elif command -v tectonic >/dev/null 2>&1; then
+  echo "Building with tectonic..."
+  tectonic "$MAIN.tex"
+else
+  cat >&2 <<'EOF'
+No LaTeX engine found (looked for latexmk, pdflatex, tectonic).
+Install one, then re-run this script:
+  Tectonic  (lightest, self-contained ~30MB, fetches packages on demand):
+      brew install tectonic
+  BasicTeX  (small TeX Live ~100MB; may need `sudo tlmgr install` for extras):
+      brew install --cask basictex
+  MacTeX    (full TeX Live ~4GB, everything included):
+      brew install --cask mactex-no-gui
+EOF
+  exit 1
+fi
+
+echo "OK -> $(pwd)/$MAIN.pdf"

03-cs6513-paper/draft.md

@@ -0,0 +1,30 @@
+# Abstract.
+
+Modern language-model architectures ship many components which have been validated using large models, however, whether the same benefits are reproduced for smaller models is actively disputed. This paper aims to produce a controlled, isolated study of one such component: Query-Key Normalisation (QK-norm) against sub 100M parameter models, defining when it becomes necessary (at which learning rate and model depths).
+
+# Index Terms.
+
+small language models
+transformer architecture
+training stability
+query–key normalization
+learning rate
+ablation study
+
+# Introduction.
+
+The transformer architecture has quickly become a domainant choice in the natural language processing (NLP) and machine learning (ML) field. While popularly used in large scale models boasting hundreds of billions to trillions of parameters, the transformer architecture has also seen growing success in smaller models, running inference on edge-compute. These models are cheaper to run and store on low-end hardware, efficient models can run with only a few tens of millions of parameters. 
+Modern LLM Architecture are the culmination of many components, including but not limited to: rotary position embeddings, RMSNorm, grouped-query attention, SwiGLU, and QK-norm. ALmost all of which have been validated against models billions of parameters in size [2], [5]. Although we know these techniques provided benefit in large models, it is not clearly established that these components carry their benefits to small models. Guidance for composing sub-100M parameter models is limited, and for several of the components mentioned above, the evidence from literature is contradictory regarding their benefits. Gaining clarity in this area is important, small-model pretraining is a active area of research (sample-efficient work[6], compute-constrained ablations [7]), choosing the right components matters when compute budget is tightest.
+
+QK-Norm is a modifcation to a transformers self-attention step. First proposed by Henry et al. [1] for use in low-resource neural machine translation [1]. The technique scales both the query and key vectors to a fixed magnitude before their dot product is calculated to create their attention logits. Without this step, the attention logits can grow without bound after training for many steps, causing the proceeding softmax step to create a near one-hot ourcome. The learning gradients become vanishingly small, and the training loss can start spiking or diverge, requiring the learning rate to be reduced [3], [4]. Recent mechanical studies associate the reason behind logit growth to "massive-activations", wherein a small number of tokens gain extreme-mangitude activations and become the domainant influence over the key vector, forming "attention sinks" [10], [11]. QK-Norm resolves this issue by preventing unbound growth of these vectors, thus removing the failure mode.
+QK-Norm was popularised by the 22-billion-parameter Vision Transformer (ViT-22B) of Dehghani et al. [2], the technique has since grown to become a near standard step in frontier language models. For example, OLMo 2 utilised RMSNorm to the Q and K vectors and found improvements to their models stability and training loss [5]. The technique has since been adopted by Gemma 2/3, Qwen3, and Chameleon. 
+However, QK-norm stands among several logit-control techniques: Gemma 2 soft-capped their attention logits using a smooth saturating function [12], Z-loss maintains softmax normalisers near zero preventing drifting [13], and NormFormer adds an extra normalisation layer inside each block [14]. Each of these techniques are frequently studied together, so the impact of just QK-Norm's is difficult to observe.
+Literature regarding small models is sparse, and of what exists, often entangled. Wortsman et al. provides a solid methodological template. Wortsman found that instabilities found in large models can be reproduced in small models at high learning rates, QK-layernorm paired with z-loss can resolve these issues. They found this behaviour transfers across models sizes. They also introduce a learning-rate (LR) sensitivity metric that measures how far a model's final loss deviates from its optimum as the learning rate is adjusted. Recently, IMU-1 by Grigorev et al. found in 70M parameter models, the single most postively impactful architecture change was QK-Norm. This was measured via reducing the activiation kurtosis [6]. However, in their study, they paired QK-Norm alongside maximal update parametrisation (µP), NorMuon, and value residuals. 
+It is important ot note that these papers do not stand without objection. Other literature finds that those sames ablations (RMSNorm, rotary position embeddings, grouped-query attention) provide no measurable benefit to models sized at 70M parameters [7]. The two literatures disagree at effectively the same scale, while neither paper conducts the isolated learning-rate experiment that would settle the matter. 
+From this, learning rate arises as the right axis to work along. Maximal-update parametrisation details that optimal learning rate can transfer across model widths [8]. Learning rate is the hyperparameter that these stability effects are dependant on. Therefore, a faithful examination of QK-Norm requires us to study it as it relates to learning rate while maximal-update parametrisation is disabled, ensuring that its effects do not mask the behaviour we are studying.
+In regards to evidence inspecting the behaviour of small models, there are two limitations surounding them. First, evidence studying the impact on small models is confounded, Wortsman pairs qk-norm with z-loss and uses small models as a proxy for larger models, while IMU-1 pairs it with several other techniques (µP + NorMuon + value residuals) measuring its effectiveness through activation kurtosis at a single fixed depth and never sweeping learning rate [6]. Second, the opposing works test against a compute-optimal budget and does not isolate QK-norm or sweep learning as we desire [7]. The arising issue is that there is no study isolating the behaviour of QK-norm through different learning rates or at what model depth it bcomes necessary. A second gap revolves around accessibility, most texts harnesses expensive GPUs, making reproducability difficult. A laptop-reproducable study on its own is a contribution on its own. 
+This study therefore aims to isolate the effect of QK-norm and to determine at what scale a small language model benefits from it. It ask, at fewer than 100M parameters, does QK-norm - on its own - improve model convergense and raise the maximum stable learning rate? Additionally, at what learning rate does it become necessary? As a final subquestion, how does that threshold shift as the model depth is changed?
+Three hypothesis can there be derived: First, the effect of QK-norm is learning-rate conditional, negligible impact or marginally harmful at low learning rates but highly impactful at higher ones (H1); that QK-Norm raises the max stable learning rate and lowers learning-rate-sensitivity (H2); and that the learning rate at which QK-norm becomes necessary lowers as depth increases.
+The study will investigate these hypothesis by conducting controlled ablations/changes on a nanoGPT [9] project with maximal update parametrisation disabled, AdamW set as the optimiser, while toggling QK-norm as the sole independant variable through a dense sweep of learning rates, and additionally against a small range of model depths at a model scale of roughly 10-15M parameters.
+Results will be measured through the learning-rate sensitivity [4] , the maximum stable learning rate, steps-to-threshold, perplexity, attention entropy and activation kurtosis.
+The project will be working with small models, which has a supported precident to produce cohesive text by using the TinyStories [16] body of text, which can train on a single-GPU as establish in the Cramming studying [17]. The project is designed to run on a consumer laptop (Apple M4 Pro chip), with code, seeds, and logs distributed.

03-cs6513-paper/draft.tex

@@ -0,0 +1,235 @@
+%% =====================================================================
+%%  draft.tex  --  LaTeX conversion of draft.md (the author's own prose)
+%% =====================================================================
+%%  This is a FORMAT conversion only: the Introduction/Abstract prose is
+%%  reproduced VERBATIM from draft.md (Cormac's own words, typos included --
+%%  nothing here is copy-edited). Errors were REPORTED back in chat rather than
+%%  silently fixed, so the repo keeps showing your own incremental writing (the
+%%  point of the anti-AI audit). Only structure was added: markdown headings ->
+%%  IEEE sections, and the numeric [1]..[17] citations -> \cite{} keys.
+%%
+%%  CITATIONS FILLED (draft.md named the source but gave no [n]) -- both flagged
+%%  inline with "% [FILLED CITATION ...]" so you can confirm/adjust:
+%%    * "Wortsman ... methodological template"  -> \cite{wortsman2024}
+%%    * Gap para, "Wortsman pairs qk-norm with z-loss" -> \cite{wortsman2024}
+%%
+%%  Number mapping (key -> printed number). qknmla (was [15] in main.tex) is
+%%  never cited in this draft, so it was REMOVED; LaTeX renumbers the rest from
+%%  \bibitem order, so tinystories/cramming are now [15]/[16] (were [16]/[17]).
+%%  NB: this draft's numbering therefore no longer matches main.tex's 17-item list.
+%%    [1]=henry2020  [2]=vit22b     [3]=zhai2023   [4]=wortsman2024 [5]=olmo2
+%%    [6]=imu1       [7]=slmtradeoffs [8]=mup      [9]=nanogpt      [10]=sun_massive
+%%    [11]=attnsink  [12]=softcap   [13]=zloss     [14]=normformer  [15]=tinystories
+%%    [16]=cramming
+%%
+%%  Title/author block carried over from main.tex (draft.md had none).
+%%  Build:  ./paper/build.sh draft
+%% =====================================================================
+
+\documentclass[conference]{IEEEtran}
+\IEEEoverridecommandlockouts
+\usepackage{cite}
+\usepackage{amsmath,amssymb,amsfonts}
+\usepackage{graphicx}
+\usepackage{textcomp}
+\usepackage{xcolor}
+\usepackage{url}
+
+\begin{document}
+
+% Title/author carried over from main.tex (draft.md did not include them).
+\title{When Does a Small Language Model Need QK-Norm?\\
+An Isolated Learning-Rate Characterization of Query--Key Normalization\\
+at Sub-100M Parameters\\
+% {\footnotesize \textsuperscript{}(working title -- draft)}
+}
+
+\author{\IEEEauthorblockN{Cormac Shannon}
+\IEEEauthorblockA{\textit{MSc Immersive Software Engineering} \\
+\textit{University of Limerick}\\
+Limerick, Ireland \\
+23366303}
+}
+
+\maketitle
+
+\begin{abstract}
+Modern language-model architectures ship many components which have been
+validated using large models, however, whether the same benefits are reproduced
+for smaller models is actively disputed. This paper aims to produce a controlled,
+isolated study of one such component: Query-Key Normalisation (QK-norm) against
+sub 100M parameter models, defining when it becomes necessary (at which learning
+rate and model depths).
+\end{abstract}
+
+\begin{IEEEkeywords}
+small language models, transformer architecture, training stability,
+query--key normalization, learning rate, ablation study
+\end{IEEEkeywords}
+
+\section{Introduction}
+
+% [Context & Hook]
+The transformer architecture has quickly become a dominant choice in the natural
+language processing (NLP) and machine learning (ML) field. While popularly used in
+large scale models boasting hundreds of billions to trillions of parameters, the
+transformer architecture has also seen growing success in smaller models, running
+inference on edge-compute. These models are cheaper to run and store on low-end
+hardware, efficient models can run with only a few tens of millions of parameters.
+
+Modern LLM Architecture are the culmination of many components, including but not
+limited to: rotary position embeddings, RMSNorm, grouped-query attention, SwiGLU,
+and QK-norm. Almost all of which have been validated against models billions of
+parameters in size \cite{vit22b, olmo2}. Although we know these techniques provided
+benefit in large models, it is not clearly established that these components carry
+their benefits to small models. Guidance for composing sub-100M parameter models is
+limited, and for several of the components mentioned above, the evidence from
+literature is contradictory regarding their benefits. Gaining clarity in this area
+is important, small-model pretraining is an active area of research (sample-efficient
+work \cite{imu1}, compute-constrained ablations \cite{slmtradeoffs}), choosing the
+right components matters when compute budget is tightest.
+
+% [Literature review]
+QK-Norm is a modification to a transformers self-attention step. First proposed by
+Henry et al. \cite{henry2020} for use in low-resource neural machine translation.
+The technique scales both the query and key vectors to a fixed
+magnitude before their dot product is calculated to create their attention logits.
+Without this step, the attention logits can grow without bound after training for
+many steps, causing the subsequent softmax step to create a near one-hot outcome.
+The learning gradients become vanishingly small, and the training loss can start
+spiking or diverge, requiring the learning rate to be reduced \cite{zhai2023,
+wortsman2024}. Recent mechanical studies associate the reason behind logit growth to
+``massive-activations'', wherein a small number of tokens gain extreme-magnitude
+activations and become the domainant influence over the key vector, forming
+``attention sinks'' \cite{sun_massive, attnsink}. QK-Norm resolves this issue by
+preventing unbound growth of these vectors, thus removing the failure mode.
+
+QK-Norm was popularised by the 22-billion-parameter Vision Transformer (ViT-22B) of
+Dehghani et al. \cite{vit22b}, the technique has since grown to become a near
+standard step in frontier language models. For example, OLMo 2 utilised RMSNorm to
+the Q and K vectors and found improvements to their models stability and training
+loss \cite{olmo2}. The technique has since been adopted by Gemma 2/3, Qwen3, and
+Chameleon.
+
+However, QK-norm stands among several logit-control techniques: Gemma 2 soft-capped
+their attention logits using a smooth saturating function \cite{softcap}, Z-loss
+maintains softmax normalisers near zero preventing drifting \cite{zloss}, and
+NormFormer adds an extra normalisation layer inside each block \cite{normformer}.
+Each of these techniques are frequently studied together, so the impact of just
+QK-Norm alone is difficult to observe.
+
+Literature regarding small models is sparse, and of what exists, often entangled.
+Wortsman et al. provides a solid methodological template \cite{wortsman2024}. % [FILLED CITATION -- draft.md named Wortsman but gave no [n] here]
+Wortsman found that instabilities found in large models can be reproduced in small
+models at high learning rates, QK-layernorm paired with z-loss can resolve these
+issues. They found this behaviour transfers across model sizes. They also introduce
+a learning-rate (LR) sensitivity metric that measures how far a model's final loss
+deviates from its optimum as the learning rate is adjusted. Recently, IMU-1 by
+Grigorev et al. found in 70M parameter models, the single most positively impactful
+architecture change was QK-Norm. This was measured via reducing the activation
+kurtosis \cite{imu1}. However, in their study, they paired QK-Norm alongside maximal
+update parametrisation ($\mu$P), NorMuon, and value residuals.
+
+It is important to note that these papers do not stand without objection. Other
+literature finds that some of the same ablations (RMSNorm, rotary position embeddings,
+grouped-query attention) do not transfer well to models sized at 70M parameters 
+\cite{slmtradeoffs}. The two literatures disagree at effectively the same
+scale, while neither paper conducts the isolated learning-rate experiment that would
+settle the matter.
+
+From this, learning rate arises as the right axis to work along. Maximal-update
+parametrisation details that optimal learning rate can transfer across model widths
+\cite{mup}. Learning rate is the hyperparameter that these stability effects are
+dependant on. Therefore, a faithful examination of QK-Norm requires us to study it
+as it relates to learning rate while maximal-update parametrisation is disabled,
+ensuring that its effects do not mask the behaviour we are studying.
+
+% [Problem statement & gap]
+In regards to evidence inspecting the behaviour of small models, there are two
+limitations surrounding them. First, evidence studying the impact on small models is
+confounded, Wortsman pairs qk-norm with z-loss and uses small models as a proxy for
+larger models \cite{wortsman2024}, while IMU-1 pairs it with several other techniques % [FILLED CITATION -- draft.md named Wortsman but gave no [n] here]
+($\mu$P + NorMuon + value residuals) measuring its effectiveness through activation
+kurtosis at a single fixed depth and never sweeping learning rate \cite{imu1}.
+Second, the opposing works test against a compute-optimal budget and does not isolate
+QK-norm or sweep learning as we desire \cite{slmtradeoffs}. The arising issue is that
+there is no study isolating the behaviour of QK-norm through different learning rates
+or at what model depth it becomes necessary. A second gap revolves around
+accessibility, most texts harness expensive GPUs, making reproducibility difficult.
+A laptop-reproducible study on its own is a worthwhile contribution.
+
+% [Proposed contribution: objective + research questions]
+This study therefore aims to isolate the effect of QK-norm and to determine at what
+scale a small language model benefits from it. It asks, at fewer than 100M parameters,
+does QK-norm --- on its own --- improve model convergence and raise the maximum
+stable learning rate? Additionally, at what learning rate does it become necessary?
+As a final subquestion, how does that threshold shift as the model depth is changed?
+
+% [Hypotheses]
+Three hypotheses can there be derived: First, the effect of QK-norm is learning-rate
+conditional, negligible impact or marginally harmful at low learning rates but highly
+impactful at higher ones (H1); that QK-Norm raises the max stable learning rate and
+lowers learning-rate-sensitivity (H2); and that the learning rate at which QK-norm
+becomes necessary lowers as depth increases (H3).
+
+% [Method]
+The study will investigate these hypothesis by conducting controlled ablations/changes
+on a nanoGPT \cite{nanogpt} project with maximal update parametrisation disabled,
+AdamW set as the optimiser, while toggling QK-norm as the sole independent variable
+through a dense sweep of learning rates, and additionally against a small range of
+model depths at a model scale of roughly 10-15M parameters.
+
+% [Metrics]
+Results will be measured through the learning-rate sensitivity \cite{wortsman2024},
+the maximum stable learning rate, steps-to-threshold, perplexity, attention entropy
+and activation kurtosis.
+
+% [Data + feasibility]
+The project will be working with small models, which has a supported precedent to
+produce cohesive text by using the TinyStories \cite{tinystories} body of text, which
+can train on a single-GPU as established in the Cramming study \cite{cramming}. The
+project is designed to run on a consumer laptop (Apple M4 Pro chip), with code, seeds,
+and logs distributed.
+
+% =====================================================================
+% Bibliography carried over from main.tex, minus qknmla (which this draft never
+% cites) -> 16 entries. LaTeX numbers them by \bibitem order, so removing the
+% uncited entry renumbers every \cite automatically. ALL entries still need
+% Scopus / primary-PDF checks -- see the % [TO VERIFY] tags.
+% =====================================================================
+\begin{thebibliography}{00}
+\bibitem{henry2020} A. Henry, P. R. Dahl, et al., ``Query-key normalization for
+transformers,'' in \textit{Findings of EMNLP}, 2020. arXiv:2010.04245. % [TO VERIFY authors]
+\bibitem{vit22b} M. Dehghani et al., ``Scaling vision transformers to 22 billion
+parameters,'' 2023. arXiv:2302.05442.
+\bibitem{zhai2023} S. Zhai et al., ``Stabilizing transformer training by
+preventing attention entropy collapse,'' in \textit{ICML}, 2023. arXiv:2303.06296.
+\bibitem{wortsman2024} M. Wortsman et al., ``Small-scale proxies for large-scale
+transformer training instabilities,'' in \textit{ICLR (oral)}, 2024.
+arXiv:2309.14322. % [TO VERIFY full author list]
+\bibitem{olmo2} OLMo~2 Team, ``2 OLMo 2 furious,'' 2025. arXiv:2501.00656.
+% [TO VERIFY preferred citation format / authors]
+\bibitem{imu1} G. Grigorev et al., ``IMU-1: Sample-efficient pre-training of
+small language models,'' 2026. arXiv:2602.02522. % [TO VERIFY authors; preprint]
+\bibitem{slmtradeoffs} ``Architectural trade-offs in small language models under
+compute constraints,'' 2025. arXiv:2512.20877. % [TO VERIFY title + authors]
+\bibitem{mup} G. Yang et al., ``Tensor programs V: Tuning large neural networks
+via zero-shot hyperparameter transfer,'' 2022. arXiv:2203.03466.
+\bibitem{nanogpt} A. Karpathy, ``nanoGPT,'' GitHub repository, 2022--.
+\url{https://github.com/karpathy/nanoGPT}. % software; check IEEE format for repos
+\bibitem{sun_massive} M. Sun et al., ``Massive activations in large language
+models,'' in \textit{ICML}, 2024. arXiv:2402.17762. % [TO VERIFY]
+\bibitem{attnsink} ``When attention sink emerges in language models: an empirical
+view,'' 2024. arXiv:2410.10781. % [TO VERIFY authors]
+\bibitem{softcap} Gemma Team, ``Gemma 2,'' technical report, 2024. % [TO VERIFY] arXiv ID + exact soft-capping citation
+\bibitem{zloss} A. Chowdhery et al., ``PaLM: Scaling language modeling with
+pathways,'' 2022. % [TO VERIFY] z-loss source; confirm arXiv ID
+\bibitem{normformer} S. Shleifer et al., ``NormFormer: Improved transformer
+pretraining with extra normalization,'' 2021. arXiv:2110.09456. % [TO VERIFY authors]
+\bibitem{tinystories} R. Eldan and Y. Li, ``TinyStories: How small can language
+models be and still speak coherent English?,'' 2023. arXiv:2305.07759. % [TO VERIFY]
+\bibitem{cramming} J. Geiping and T. Goldstein, ``Cramming: Training a language
+model on a single GPU in one day,'' in \textit{ICML}, 2023. arXiv:2212.14034. % [TO VERIFY]
+\end{thebibliography}
+
+\end{document}

03-cs6513-paper/main.tex

@@ -0,0 +1,513 @@
+%% =====================================================================
+%%  CS6513 -- Introduction + Mini Literature Review (IEEE conference)
+%%  WORKING DRAFT -- ROUGH PASS (page limit deliberately ignored for now)
+%%
+%%  STATUS: This is a SCAFFOLD, not final text. The bullets below are
+%%  ideas / papers / arguments to be turned into flowing prose IN YOUR
+%%  OWN WORDS over several commits. Do NOT submit the bullets as-is.
+%%  Remember: declare this AI-assisted outlining in the notebook GenAI log.
+%%
+%%  Target final structure (2 pages incl. refs), weaving together:
+%%    1. Context & Hook   2. Literature Context (mini lit review)
+%%    3. Problem & Gap    4. Proposed Contribution   5. Roadmap
+%% =====================================================================
+%%
+%%  ABOUT THESE COMMENTS:
+%%  Anything after a "%" is a LaTeX comment -- ignored by the compiler, so it
+%%  NEVER appears in the PDF. The annotations below are learning notes for the
+%%  author; they are safe to keep, and can be stripped later. Convention used:
+%%    % DEFN: <term>  -- plain-language definition of jargon
+%%    % EXPL: ...      -- expands on a concept / why it matters
+%%    % NOTE: ...      -- drafting instruction to self
+%%    % [TO VERIFY]    -- citation detail to confirm against Scopus / the PDF
+%%
+%%  ---- MINI GLOSSARY (core terms, defined once here) --------------------
+%%  Transformer : the neural-network design behind modern language models
+%%      (Vaswani et al. 2017, "Attention Is All You Need"). Processes all tokens
+%%      in parallel using "self-attention" (below).
+%%  Token       : the unit of text a model reads/predicts -- a word or word-piece
+%%      (e.g. "walking" -> "walk" + "ing"). Text is a sequence of tokens.
+%%  Parameters  : the model's learned numbers (weights). "Model size" = how many
+%%      there are. 100M = 100 million. Frontier LLMs have 10^9-10^12 (billions+).
+%%  Self-attention : the mechanism that lets each token "look at" other tokens.
+%%      For every token it builds three vectors -- a QUERY, a KEY, and a VALUE.
+%%  Query / Key / Value (Q/K/V) : query = "what am I looking for", key = "what do
+%%      I offer", value = "what I pass on". A token attends to others by comparing
+%%      its query to their keys.
+%%  Attention logit : the raw match score between a query and a key (their dot
+%%      product). Large logits -> the next step (softmax) becomes extreme.
+%%  Softmax     : turns a list of scores into probabilities (all positive, sum 1).
+%%      Used to decide how much each token attends to each other token.
+%%  Learning rate (LR) : the step size during training. Too low = slow; too high
+%%      = unstable / diverging. The central variable in THIS study.
+%%  Ablation    : an experiment that removes or toggles ONE component to measure
+%%      its specific effect -- the core method here (QK-norm on vs off).
+%%  Perplexity  : a standard quality score for language models (= exp of the
+%%      average prediction loss). LOWER is better.
+%%  -----------------------------------------------------------------------
+
+\documentclass[conference]{IEEEtran}
+% DEFN: \documentclass[conference]{IEEEtran} -- load the official IEEE conference
+%       style (the IEEEtran.cls file in this folder). Sets the two-column layout,
+%       fonts and margins the assignment requires; do not change.
+% DEFN: \usepackage{...} -- pulls in an add-on ("package") of extra commands.
+%       Each line below is one such add-on; comments say what it gives us.
+\IEEEoverridecommandlockouts
+\usepackage{cite}                      % tidies up citation numbers, e.g. [2]-[4]
+\usepackage{amsmath,amssymb,amsfonts}  % maths symbols: the arrows, <, mu, etc.
+\usepackage{graphicx}                  % lets us include figures/images (none yet)
+\usepackage{textcomp}                  % extra text symbols
+\usepackage{xcolor}                    % coloured text (if ever needed)
+\usepackage{url}                       % formats web links in the bibliography
+
+\begin{document}
+% EXPL: everything the reader SEES lives between \begin{document} and
+%       \end{document}. Above that line is the "preamble" (setup only).
+
+% DEFN: \title{...} sets the paper title. The "\\" forces a line break.
+% DEFN: QK-Norm (Query-Key Normalization) -- the technique under study: before
+%       attention compares a query to a key, rescale both to a controlled length
+%       so their match score (logit) cannot blow up. Full explanation in Sec. below.
+% DEFN: "Isolated" -- we change ONLY QK-norm, holding everything else fixed, so any
+%       effect is attributable to it alone (see "ablation" in the glossary).
+% DEFN: "Sub-100M parameters" -- models with fewer than 100 million weights; small
+%       enough to train on a laptop (frontier models are 100-1000x+ larger).
+\title{When Does a Small Language Model Need QK-Norm?\\
+An Isolated Learning-Rate Characterization of Query--Key Normalization\\
+at Sub-100M Parameters\\
+{\footnotesize \textsuperscript{}(working title -- rough draft)}}
+
+\author{\IEEEauthorblockN{Cormac Shannon}
+\IEEEauthorblockA{\textit{MSc Immersive Software Engineering} \\
+\textit{University of Limerick}\\
+Limerick, Ireland \\
+23366303}
+}
+
+\maketitle
+
+% DEFN: abstract -- the ~150-word summary at the very top of a paper. A reader
+%       decides from this alone whether to read on. Convention: write it LAST.
+% NOTE(abstract): write LAST, once the argument is settled. Rough placeholder only.
+\begin{abstract}
+% ROUGH -- rewrite in own words.
+% EXPL: "components validated only at large scale" -- modern models are built from
+%       many add-on parts (see the architecture list in Sec. Introduction) that
+%       were tested mainly on huge models; nobody checked they still help small ones.
+% DEFN: \emph{...} -- emphasis (renders as italics).
+Modern language-model architectures bundle many components validated only at
+large scale; whether they help \emph{small} models is under-studied and, for
+some components, actively disputed. This paper motivates a controlled,
+isolated study of one such component -- Query--Key Normalization (QK-norm) --
+at sub-100M-parameter scale, characterizing when (at which learning rates and
+depths) it becomes necessary. % TODO: 1-2 sentences on method + what we expect.
+% EXPL: "depth" = number of stacked transformer layers; "necessary" = the point
+%       where turning QK-norm off makes training fail/diverge.
+\end{abstract}
+
+% DEFN: keywords -- a handful of index terms so search engines/databases can file
+%       the paper. Pick the phrases a reader would search to find work like yours.
+\begin{IEEEkeywords}
+small language models, transformer architecture, training stability,
+query--key normalization, learning rate, ablation study
+\end{IEEEkeywords}
+
+% =====================================================================
+\section{Introduction}
+% For a 2-page intro-only paper this whole section IS the paper. Below it is
+% broken into the 5 required components as comment banners; in the final
+% version they should read as ONE flowing argument (few/no subheadings).
+
+%% -------------------------------------------------------------------
+%% COMPONENT 1 -- CONTEXT & HOOK  (broad domain + why relevant NOW)
+%% -------------------------------------------------------------------
+% NOTE: 1 short paragraph. Funnel from broad -> the tension that motivates us.
+% DEFN: NLP = Natural Language Processing (getting computers to work with human
+%       language); ML = Machine Learning. LLM = Large Language Model.
+% DEFN: "on-device / edge" -- running a model locally on a phone/laptop/sensor
+%       instead of a data-centre; motivates SMALL models (limited memory + power).
+% EXPL: \textbf{...} = bold. This whole block is bullet notes; final version is
+%       ONE flowing paragraph that funnels broad -> specific.
+\begin{itemize}
+  \item Broad domain: transformer language models now dominate NLP/ML; but the
+        field is increasingly moving toward \textbf{small / efficient} models
+        (on-device, edge, cost, energy, and \emph{reproducibility}).
+        % DEFN: reproducibility -- whether other researchers can re-run your
+        %       experiment and get the same result. A core scientific value, and
+        %       easier to guarantee with small models anyone can afford to train.
+  \item Modern LLM architectures are an accretion of components -- rotary
+        position embeddings, RMSNorm, grouped-query attention, SwiGLU,
+        \textbf{QK-norm} -- almost all validated at the billion-parameter scale
+        \cite{olmo2,vit22b}.
+        % EXPL: "accretion of components" = built up by stacking many small
+        %       add-ons over the years. The four named ones (context only; NOT
+        %       what we study):
+        % DEFN: rotary position embeddings (RoPE) -- how a model encodes WORD
+        %       ORDER, by rotating the query/key vectors by an angle set by each
+        %       token's position.
+        % DEFN: RMSNorm -- a "normalization" layer that rescales a vector to a
+        %       standard size to keep numbers well-behaved during training (a
+        %       cheaper variant of LayerNorm).
+        % DEFN: grouped-query attention (GQA) -- a memory-saving trick where
+        %       several attention "heads" share one set of keys/values.
+        % DEFN: SwiGLU -- a high-performing choice of activation function (the
+        %       little non-linear step inside each layer's feed-forward block).
+        % DEFN: \cite{key} -- inserts a numbered reference; "key" points at a
+        %       \bibitem in the bibliography at the bottom of the file.
+  \item Hook / tension: it is \emph{not} established that these components carry
+        down to small models. Design rules for the sub-100M regime are thin,
+        and for several components the evidence is contradictory (see below).
+        % EXPL: THE HOOK. "regime" = a size range with its own behaviour. The
+        %       whole paper hangs on this tension: proven-big does not mean
+        %       proven-small.
+  \item Why now: small-model pretraining is a live research area
+        (sample-efficiency work \cite{imu1}; compute-constrained ablations
+        \cite{slmtradeoffs}); getting the component choices right matters most
+        exactly where the compute budget is smallest.
+        % DEFN: pretraining -- the big first training phase where a model learns
+        %       language from lots of raw text (before any task-specific tuning).
+        % DEFN: sample-efficiency -- getting good results from LESS training data.
+\end{itemize}
+
+%% -------------------------------------------------------------------
+%% COMPONENT 2 -- LITERATURE CONTEXT  (mini lit review: what's been done)
+%% -------------------------------------------------------------------
+% NOTE: the meat. Synthesize (don't list). Group by theme, not by paper.
+% EXPL: COMPONENT 2 is the "mini literature review": what is already known.
+%       Group by THEME (what QK-norm is / who uses it / small-scale evidence /
+%       the contradiction / why LR), not one-paragraph-per-paper.
+\begin{itemize}
+  \item \textbf{What QK-norm is + why it exists.}
+    \begin{itemize}
+      \item Origin: Henry et al. \cite{henry2020} -- normalize the query and key
+            vectors before the attention dot product (for low-resource NMT).
+            % DEFN: "normalize ... before the dot product" -- rescale the query and
+            %       key vectors to a fixed length first, so their dot product (the
+            %       match score) stays in a controlled range. THAT is QK-norm.
+            % DEFN: dot product -- a single number measuring how aligned two vectors
+            %       are; here it is the query-key match score = the attention logit.
+            % DEFN: NMT -- Neural Machine Translation (automatic translation). QK-norm
+            %       was first proposed there, on small models -- useful for our story.
+      \item Mechanism/problem it solves: without it, query/key norms grow during
+            training $\rightarrow$ large attention logits $\rightarrow$ softmax
+            saturates (near one-hot) $\rightarrow$ unstable/diverging loss,
+            forcing smaller learning rates.
+            % EXPL: the core causal chain, in words --
+            %   1. "norms grow" = the Q/K vectors get longer (bigger magnitude);
+            %   2. so their dot products (attention logits) get very large;
+            %   3. SOFTMAX SATURATES: given one huge score, softmax puts nearly all
+            %      weight on a single token -> output is "one-hot" (a spike);
+            %   4. at a spike the gradient (learning signal) ~vanishes, so training
+            %      becomes unstable or DIVERGES (loss shoots up), which forces you
+            %      to use a smaller, slower learning rate.
+            % DEFN: one-hot -- a vector that is 1 in a single spot and 0 everywhere
+            %       else; here it means attention collapses onto ONE token.
+            % DEFN: diverging loss -- training "blowing up": the error metric
+            %       increases uncontrollably instead of decreasing.
+            % EXPL: QK-norm breaks step 1->2, so the rest of the chain never fires.
+            %       This is exactly why it should let you train at HIGHER LR (our H2).
+      \item Deeper mechanism (why logits grow): ``massive activations'' -- a few
+            tokens acquire extreme-magnitude activations that become dominant key
+            vectors and form ``attention sinks'' \cite{sun_massive,attnsink};
+            QK-norm bounds Q/K magnitude, limiting this. % [TO VERIFY]
+            % DEFN: activation -- an internal value a neuron/layer outputs as data
+            %       flows through the network.
+            % DEFN: massive activations -- a handful of these internal values become
+            %       hugely larger than the rest (extreme outliers).
+            % DEFN: attention sink -- a token that soaks up a large share of every
+            %       other token's attention regardless of relevance (a side-effect of
+            %       those massive activations). This is the mechanism QK-norm curbs.
+      \item Popularized at scale for stability: ViT-22B \cite{vit22b}.
+            % DEFN: ViT-22B -- a 22-billion-parameter Vision Transformer (Google).
+            %       Widely cited as the work that made QK-norm mainstream for
+            %       stabilizing very large models.
+      \item Complementary mechanistic account: ``attention entropy collapse''
+            -- low attention entropy co-occurs with instability
+            \cite{zhai2023}. (QK-norm bounds logits $\rightarrow$ keeps entropy up.)
+            % DEFN: attention entropy -- a spread-out measure of the attention
+            %       weights. HIGH entropy = attention shared across many tokens;
+            %       LOW entropy = concentrated on a few (spiky). "Collapse" = it
+            %       drops too low, which coincides with unstable training. Same story
+            %       as softmax saturation, viewed through an information-theory lens.
+    \end{itemize}
+  \item \textbf{QK-norm is now standard in frontier models.}
+    \begin{itemize}
+      \item OLMo 2 applies RMSNorm to Q and K and reports improved training loss
+            + stability \cite{olmo2}. Also adopted by Gemma~2/3, Qwen3, Chameleon.
+            % TODO: pick 1-2 to cite precisely; verify each.
+            % DEFN: "frontier models" -- the current top-tier, largest LLMs. Naming
+            %       several (OLMo 2, Gemma, Qwen3, Chameleon) shows QK-norm is not
+            %       niche -- it is standard practice -> the study is relevant.
+    \end{itemize}
+  \item \textbf{QK-norm is one of several logit-control methods} (matters for the gap).
+    \begin{itemize}
+      \item Alternatives/complements: attention logit soft-capping (Gemma~2,
+            $z\!\leftarrow\!c\tanh(z/c)$) \cite{softcap}; z-loss, which keeps the
+            softmax normalizer near zero \cite{zloss}; NormFormer's extra
+            normalization \cite{normformer}. % [TO VERIFY]
+            % EXPL: these are RIVAL ways to tackle the same "logits blow up" problem:
+            % DEFN: logit soft-capping -- squash every attention logit through
+            %       z <- c*tanh(z/c), which smoothly caps it at +/-c so it can never
+            %       explode ($ ... $ is inline maths; \tanh is the tanh function).
+            % DEFN: z-loss -- a small extra penalty added to the training objective
+            %       that keeps the softmax's normalizing sum near 1 (stops it drifting
+            %       and destabilizing training).
+            % DEFN: NormFormer -- a recipe that adds a few extra normalization layers
+            %       inside each block to steady training.
+      \item These are almost always studied \emph{bundled together}, so QK-norm's
+            isolated effect is rarely attributed. % [TO VERIFY framing]
+            % EXPL: KEY SETUP FOR THE GAP -- if papers turn on QK-norm AND z-loss AND
+            %       soft-capping at once, you cannot tell how much QK-norm ALONE did.
+            %       "attributed" = credited to a specific cause. Our isolation fixes this.
+    \end{itemize}
+  \item \textbf{Evidence at / near small scale (the closest work).}
+    \begin{itemize}
+      \item Wortsman et al. \cite{wortsman2024} -- instabilities reproduce in
+            \emph{small} models at high LR; qk-layernorm (+ z-loss) mitigate them
+            and ``transfer down''; introduces the \textbf{LR-sensitivity} metric.
+            KEY methodological template for us.
+            % EXPL: the closest prior work + the method we borrow. "transfer down"
+            %       = the same fix that works on big models also works on small ones.
+            % DEFN: qk-layernorm -- Wortsman's name for QK-norm (LayerNorm on Q,K).
+            % DEFN: LR-sensitivity -- their metric for HOW MUCH the final loss worsens
+            %       as you vary the learning rate. Low sensitivity = robust to LR
+            %       choice = a flatter, more forgiving curve. We reuse this metric.
+      \item IMU-1 (Grigorev et al.) \cite{imu1} -- at 70M, QK-norm is the
+            \emph{single largest} architectural win (measured via reduced
+            activation kurtosis) -- but bundled with $\mu$P, NorMuon, value
+            residuals, etc.
+            % EXPL: strongest recent "QK-norm helps small models" datapoint (70M =
+            %       70-million-parameter model) -- BUT confounded (see the Gap).
+            % DEFN: kurtosis -- a statistic for how "outlier-heavy" a set of numbers
+            %       is. High kurtosis = a few extreme values (the massive activations
+            %       above). QK-norm LOWERING kurtosis = it is taming those outliers.
+            % DEFN: NorMuon / value residuals -- other training tweaks IMU-1 turned on
+            %       at the same time; because they are bundled in, IMU-1 cannot isolate
+            %       QK-norm's own contribution.
+    \end{itemize}
+  \item \textbf{The countervailing line (the contradiction).}
+    \begin{itemize}
+      \item Compute-constrained small-model ablations report that modern
+            components (RoPE, RMSNorm, GQA) give ``no measurable benefit at
+            $\sim$70M'' \cite{slmtradeoffs}. Directly disputes the above.
+            % EXPL: THE CONFLICT that motivates the paper: one camp says QK-norm is
+            %       the biggest small-model win (IMU-1), another says modern parts do
+            %       nothing at ~70M. Both look at the same size -> something is untested.
+            % DEFN: "compute-constrained" -- trained under a fixed, limited compute
+            %       budget (so, e.g., only one learning-rate setting was tried).
+    \end{itemize}
+  \item \textbf{Why learning rate is the right axis.}
+    \begin{itemize}
+      \item $\mu$P / $\mu$Transfer \cite{mup}: optimal LR transfers across width;
+            LR is the hyperparameter these stability effects hinge on. Motivates
+            studying QK-norm \emph{as a function of LR}, and switching $\mu$P OFF
+            so LR effects are not masked.
+            % DEFN: $\mu$P (muP, "maximal update parametrization") -- a special way of
+            %       scaling a network so the BEST learning rate stays the same as you
+            %       grow model width; $\mu$Transfer = tuning LR on a small model then
+            %       reusing it on a big one. ($\mu$ = the Greek letter mu.)
+            % EXPL: why we turn $\mu$P OFF -- it deliberately makes results LESS
+            %       sensitive to learning rate, which would hide the very LR effect we
+            %       want to measure. Off = a clean, unmasked view. (Also a difference
+            %       from IMU-1, which left it ON.)
+            % DEFN: hyperparameter -- a training setting you choose by hand (like the
+            %       learning rate), as opposed to a weight the model learns itself.
+      \item Recent work keeps reaffirming the LR link -- e.g. QK-Normed MLA reports
+            QK-norm ``permits significant increases in learning rate'' \cite{qknmla}.
+            % [TO VERIFY] -- supports H2
+            % EXPL: a 2026 paper independently restating that QK-norm lets you train at
+            %       higher LR -- direct external support for our hypothesis H2 below.
+    \end{itemize}
+\end{itemize}
+
+%% -------------------------------------------------------------------
+%% COMPONENT 3 -- PROBLEM STATEMENT & GAP ANALYSIS
+%% -------------------------------------------------------------------
+% NOTE: name the gap crisply; it must fall straight out of Component 2.
+% EXPL: COMPONENT 3 = the hole in the literature the study fills. Every claim here
+%       should already have been set up in Component 2 (no new surprises).
+\begin{itemize}
+  \item The positive small-scale evidence is \textbf{confounded}: Wortsman
+        bundles qk-norm with z-loss and frames small models as a \emph{proxy}
+        for large-scale behaviour; IMU-1 bundles it with $\mu$P + NorMuon +
+        value residuals and measures it via activation statistics, not an LR
+        sweep, at a single (fixed) depth.
+        % DEFN: confounded -- when two changes are made at once so you cannot tell
+        %       which caused the effect; the classic flaw an ablation is meant to avoid.
+        % DEFN: proxy -- a stand-in. Wortsman used small models only as a cheap
+        %       stand-in for big-model behaviour, not as the object of interest.
+        % DEFN: LR sweep -- training the same model repeatedly across a range of
+        %       learning rates to map out how the outcome depends on LR. The thing
+        %       prior work at this scale did NOT do.
+  \item The negative evidence \cite{slmtradeoffs} tests a \emph{single}
+        compute-optimal budget, does not isolate QK-norm, and does not sweep LR.
+        % DEFN: compute-optimal budget -- the amount of training data/steps that is
+        %       "best value" for a given model size (Chinchilla-style rule of thumb).
+        %       Testing only this one point can miss effects that appear at other LRs.
+  \item So the two literatures \emph{disagree at the same scale} while neither
+        does the clean experiment.
+        % EXPL: one-line crux -- the disagreement persists because the decisive
+        %       experiment (isolate + sweep LR) has not been run.
+  \item \textbf{GAP:} no \emph{dense}, \emph{isolated} ($\mu$P-off)
+        learning-rate-sensitivity characterization of QK-norm at 10--100M, and
+        no map of how the LR at which QK-norm becomes \emph{necessary} moves
+        with model depth.
+        % EXPL: the headline gap, four qualifiers doing the work:
+        %   dense    = many closely-spaced LR values (a smooth curve, not 3 points);
+        %   isolated = only QK-norm toggled, nothing else;
+        %   muP-off  = the masking factor removed (see above);
+        %   at 10-100M = the small-scale band nobody has charted this way.
+  \item Secondary gap: reproducibility -- most evidence is expensive / CUDA-only;
+        a laptop-reproducible study is itself a contribution.
+        % DEFN: CUDA -- NVIDIA's GPU software stack; "CUDA-only" work cannot run on
+        %       an Apple laptop, so it is hard for others to reproduce cheaply.
+\end{itemize}
+
+%% -------------------------------------------------------------------
+%% COMPONENT 4 -- PROPOSED CONTRIBUTION  (objective, RQs, method)
+%% -------------------------------------------------------------------
+% NOTE: be explicit and honest -- this is VERIFICATION + boundary-charting,
+% not a novel-technique claim. Do not say "first".
+% EXPL: COMPONENT 4 = what WE do about the gap: objective, questions, hypotheses,
+%       method. "boundary-charting" = mapping exactly WHERE QK-norm starts to
+%       matter (which LR, which depth), rather than claiming a new technique.
+\begin{itemize}
+  \item Objective: cleanly isolate QK-norm and chart \emph{when a small model
+        needs it}.
+  \item Research question: at $<$100M params, does isolated QK-norm improve
+        convergence and raise the maximum stable LR -- and at what LR does it
+        become necessary? Sub-question: how does that threshold shift with depth?
+        % DEFN: convergence -- training successfully settling to a low, stable loss.
+        %       "Faster convergence" = reaching a target loss in fewer steps.
+        % DEFN: max stable LR -- the highest learning rate at which training still
+        %       succeeds instead of diverging. A key thing QK-norm should raise.
+  \item Hypotheses (state as such):
+        % DEFN: hypothesis -- a specific, testable prediction stated BEFORE running
+        %       the experiment. H1-H3 are what we expect the data to show.
+    \begin{itemize}
+      \item H1 -- effect is LR-conditional: negligible/slightly negative at low
+            LR, decisive at high LR.
+            % EXPL: at gentle LRs nothing diverges, so QK-norm barely helps (or
+            %       slightly hurts); at aggressive LRs it is what keeps training alive.
+      \item H2 -- QK-norm raises max stable LR and lowers LR-sensitivity.
+            % EXPL: i.e. it widens the band of usable LRs and flattens the loss-vs-LR
+            %       curve (more forgiving to the LR you pick).
+      \item H3 -- the ``necessity'' LR falls as depth increases.
+            % EXPL: prediction that DEEPER models (more layers) destabilize sooner, so
+            %       they need QK-norm at lower LRs than shallow ones.
+    \end{itemize}
+  \item High-level method: controlled ablation on \textbf{plain nanoGPT}
+        \cite{nanogpt}, $\mu$P off, AdamW; single independent variable
+        QK-norm\{on,off\} $\times$ dense LR sweep $\times$ depth; $\sim$10--15M
+        params; measures = LR-sensitivity \cite{wortsman2024}, max-stable-LR,
+        steps-to-threshold, perplexity, plus attention entropy / activation
+        kurtosis as mechanistic checks.
+        % DEFN: nanoGPT -- Andrej Karpathy's minimal, readable codebase for training
+        %       small GPT models; "plain" = without the extra speed-run tricks that
+        %       would confound the experiment. Our experimental substrate.
+        % DEFN: AdamW -- the standard optimizer (the algorithm that updates weights
+        %       using gradients); "W" = with decoupled weight decay.
+        % DEFN: independent variable -- the ONE thing we deliberately change
+        %       (QK-norm on vs off). "$\times$" here means we cross it with every LR
+        %       and every depth -> a grid of runs.
+        % DEFN: steps-to-threshold -- how many training steps to first reach a chosen
+        %       loss value; a concrete convergence-speed number.
+        % DEFN: mechanistic checks -- extra measurements (attention entropy, kurtosis)
+        %       that reveal WHY an effect happens, not just THAT it happens.
+  \item Data + precedent: small models learn coherent language from tiny corpora
+        (TinyStories) \cite{tinystories}; single-GPU near-BERT training is an
+        established accessible-research precedent (Cramming) \cite{cramming}. % [TO VERIFY]
+        % DEFN: corpus (pl. corpora) -- the body of text a model trains on.
+        % DEFN: TinyStories -- a dataset of very simple stories on which even
+        %       sub-10M-param models produce fluent English; justifies our tiny scale.
+        % DEFN: Cramming -- a paper training a BERT-quality model on ONE GPU in ONE
+        %       day; the precedent for "serious results on modest hardware".
+        % DEFN: BERT -- an influential 2018 language model; "near-BERT" = almost as
+        %       good as BERT.
+  \item Feasibility/repro: runs on a consumer laptop (Apple M4 Pro); code, seeds,
+        logs released.
+        % DEFN: seed -- the number that fixes the random choices in a training run;
+        %       sharing seeds lets anyone reproduce the exact run. Running multiple
+        %       seeds also shows an effect is real, not a fluke of one random draw.
+\end{itemize}
+
+%% -------------------------------------------------------------------
+%% COMPONENT 5 -- PAPER ROADMAP  (standard closing paragraph)
+%% -------------------------------------------------------------------
+% NOTE: rewrite to match the final section list once it exists.
+% EXPL: COMPONENT 5 = the "roadmap" paragraph. A standard academic convention:
+%       one or two sentences telling the reader how the rest of the paper is laid
+%       out. "Section II ... Section III ..." are the numbered parts that follow.
+%       (Roman numerals are how IEEEtran auto-numbers \section headings.)
+\begin{itemize}
+  \item ``The remainder of this paper is structured as follows: Section II
+        reviews related work on QK-norm and small-model training stability;
+        Section III details the experimental design; Section IV reports results;
+        and Section V concludes.'' % adjust to actual sections.
+\end{itemize}
+
+% =====================================================================
+% NOTE(refs): inline thebibliography for now (matches template). Switch to a
+% .bib file later if we want. EVERY entry below still needs its exact
+% authors/venue/pages checked against the primary PDF -- during this project
+% AI-surfaced citation details were wrong more than once. arXiv IDs are the
+% most reliable field; treat author lists / venues as [TO VERIFY].
+% =====================================================================
+% DEFN: thebibliography -- the reference list. Each \bibitem{key} defines one
+%       reference; \cite{key} in the text above prints its number. The {00} just
+%       tells LaTeX the labels are up to two digits wide.
+% DEFN: arXiv -- a free repository of (often not-yet-peer-reviewed) research
+%       papers. "arXiv:2010.04245" is a permanent ID: go to arxiv.org/abs/2010.04245.
+%       Reliable for finding the paper; the AUTHOR/VENUE text still needs checking
+%       (hence the [TO VERIFY] tags) -- run each through Scopus before submission.
+\begin{thebibliography}{00}
+
+\bibitem{henry2020} A. Henry, P. R. Dahl, et al., ``Query-key normalization for
+transformers,'' in \textit{Findings of EMNLP}, 2020. arXiv:2010.04245. % [TO VERIFY authors]
+
+\bibitem{vit22b} M. Dehghani et al., ``Scaling vision transformers to 22 billion
+parameters,'' 2023. arXiv:2302.05442.
+
+\bibitem{zhai2023} S. Zhai et al., ``Stabilizing transformer training by
+preventing attention entropy collapse,'' in \textit{ICML}, 2023.
+arXiv:2303.06296.
+
+\bibitem{wortsman2024} M. Wortsman et al., ``Small-scale proxies for large-scale
+transformer training instabilities,'' in \textit{ICLR (oral)}, 2024.
+arXiv:2309.14322. % [TO VERIFY full author list]
+
+\bibitem{olmo2} OLMo~2 Team, ``2 OLMo 2 furious,'' 2025. arXiv:2501.00656.
+% [TO VERIFY preferred citation format / authors]
+
+\bibitem{imu1} G. Grigorev et al., ``IMU-1: Sample-efficient pre-training of
+small language models,'' 2026. arXiv:2602.02522. % [TO VERIFY authors; preprint]
+
+\bibitem{slmtradeoffs} ``Architectural trade-offs in small language models under
+compute constraints,'' 2025. arXiv:2512.20877. % [TO VERIFY title + authors]
+
+\bibitem{mup} G. Yang et al., ``Tensor programs V: Tuning large neural networks
+via zero-shot hyperparameter transfer,'' 2022. arXiv:2203.03466.
+
+\bibitem{nanogpt} A. Karpathy, ``nanoGPT,'' GitHub repository, 2022--.
+\url{https://github.com/karpathy/nanoGPT}. % software; check IEEE format for repos
+
+% --- added 18/08/2026: surrounding literature; ALL need Scopus/PDF checks ---
+\bibitem{sun_massive} M. Sun et al., ``Massive activations in large language
+models,'' in \textit{ICML}, 2024. arXiv:2402.17762. % [TO VERIFY]
+\bibitem{attnsink} ``When attention sink emerges in language models: an empirical
+view,'' 2024. arXiv:2410.10781. % [TO VERIFY authors]
+\bibitem{softcap} Gemma Team, ``Gemma 2,'' technical report, 2024. % [TO VERIFY] find arXiv ID + exact citation for logit soft-capping
+\bibitem{zloss} A. Chowdhery et al., ``PaLM: Scaling language modeling with
+pathways,'' 2022. % [TO VERIFY] z-loss source; confirm arXiv ID
+\bibitem{normformer} S. Shleifer et al., ``NormFormer: Improved transformer
+pretraining with extra normalization,'' 2021. arXiv:2110.09456. % [TO VERIFY authors]
+\bibitem{qknmla} ``QK-Normed MLA: QK normalization without full key caching,''
+2026. arXiv:2606.16310. % [TO VERIFY authors]
+\bibitem{tinystories} R. Eldan and Y. Li, ``TinyStories: How small can language
+models be and still speak coherent English?,'' 2023. arXiv:2305.07759. % [TO VERIFY]
+\bibitem{cramming} J. Geiping and T. Goldstein, ``Cramming: Training a language
+model on a single GPU in one day,'' in \textit{ICML}, 2023. arXiv:2212.14034. % [TO VERIFY]
+
+\end{thebibliography}
+
+\end{document}

04-cs6513-notebook/msc-research-notebook-outline.rmd

@@ -0,0 +1,232 @@
+# MSc Research Notebook
+
+- **Student Name:** Cormac Shannon
+- **Student ID:** 23366303
+- **Proposed Research Title:** When Does a Small Language Model Need QK-Norm? An Isolated Learning-Rate Characterization of Query–Key Normalization at <100M Parameters *(working title)*
+- **GitHub Repository Link:** https://github.com/NikScorch/MSc_Research_Project
+
+------------------------------------------------------------------------
+
+## 1. Project Overview & Evolution Track
+
+Use this section to map out how your project evolves over the block. Whenever you change your research question, methodology, or scope, update this log.
+
+### Current Working Research Question(s)
+
+> *Last Updated: 21/08/2026*
+
+**Primary research question:**
+
+> *"At <100M parameters, does isolated Query–Key Normalization (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 necessity threshold shift with model depth?
+
+**Hypotheses:**
+
+- **H1** - QK-norm's effect is learning-rate-conditional: negligible or slightly negative at low LR, decisive at high LR.
+- **H2** - QK-norm raises the maximum stable LR and lowers LR-sensitivity at <100M.
+- **H3** - the LR at which QK-norm becomes necessary falls as model depth increases.
+
+**Honest framing (important):** This is a *verification + boundary-charting* study, **not** a novel discovery. QK-norm's stabilising effect is already established (Wortsman, ICLR 2024; IMU-1, 2026). What is genuinely un-charted is a **dense**, **isolated** (μP-free) learning-rate-sensitivity curve at 10–100M, plus how the "necessity" threshold moves with depth. I am not claiming novelty of technique or "first at small scale" - see the Critical Source Evaluation Log.
+
+**Feasibility anchor:** Small models only (~10–15M params). Controlled ablation on an M4 Pro (48 GB, PyTorch MPS): ~1–1.5 h/run (to validate with a Week-5 pilot); core matrix QK-norm{on,off} × LR{~6} × seed{2} ≈ 24 runs. Measures: max-stable-LR, LR-sensitivity (Wortsman metric), steps-to-threshold, perplexity, plus attention entropy and activation kurtosis as mechanistic checks.
+
+**Experimental platform:** **plain nanoGPT** (<https://github.com/karpathy/nanoGPT>), with **μP switched off** and standard AdamW, so QK-norm is the *only* variable. (Karpathy's *nanochat* was trialled in Week 2 but bundles speedrun tricks/optimisers that would confound an isolated QK-norm study - see Pivot Log.)
+
+### The Pivot Log
+
+Document any major changes in direction here. (e.g., *"Switched from quantitative survey to qualitative interviews because sample access fell through on 15/10/2026"*).
+
+| Date | Previous Direction | New Direction | Reason for Pivot / Academic Justification |
+|:-----------------|:-----------------|:-----------------|:-----------------|
+| 08/07/2026 | "Study architectural changes to LLMs" (unbounded scope) | Small-model (~10–100M param) controlled single-lever ablations | Training frontier LLMs is infeasible within an MSc compute/time budget. Small-model ablations preserve the research question while remaining reproducible - consistent with Creswell (Ch.1) criteria for selecting an approach and Vogt (2008) on letting the problem dictate feasible methods. |
+| 17/07/2026 | Small-model single-lever ablation (lever TBD) | Focused specifically on Query–Key Normalization (QK-norm) × learning rate | After a literature scan of candidate levers, QK-norm chosen as a cheap, ubiquitous, well-scoped component with a clean controlled design that is feasible on an M4 Pro - unlike over-training or nanoGPT-speedrun-stack studies, which need cloud GPUs or CUDA-only tooling. |
+| 24/07/2026 | Framed as a "novel study of QK-norm at small scale" | Reframed as a verification + boundary-charting study; base switched from nanochat to plain nanoGPT (μP off) | Prior-art checks showed QK-norm's benefit is already established (Wortsman 2024; IMU-1 2026), so the claim was honestly rescoped to a dense, isolated, laptop-reproducible LR-sensitivity curve + depth threshold. Plain nanoGPT with μP off isolates the single variable; nanochat/modded-nanoGPT bundle confounds. |
+
+------------------------------------------------------------------------
+
+## 2. Critical Source Evaluation Log
+
+Do not just copy and paste citations. For every core paper that shapes your final Introduction, document your active reading process here. Also note papers that looked promising but you decided, on review, to exclude them. Note reasons for exclusion.
+
+### Xiao & Watson (2019) - Guidance on Conducting a Systematic Literature Review
+
+- **Full IEEE/APA Citation:** Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. *Journal of Planning Education and Research*, 39(1), 93–112. *[verify volume/issue/pages against the PDF before final use]*
+- **Why did you select this paper?** It provides a step-by-step, repeatable process for running a literature review, which I need to structure the search that will feed my Introduction and thesis.
+- **Key Findings & Methodology:** Lays out the SLR stages (formulate the problem → develop/validate the protocol → search the literature → screen for inclusion → assess quality → extract/analyse data → report findings). Emphasises transparency and reproducibility of the search.
+- **Limitations/Weaknesses Identified:** Oriented toward planning/social-science literature; the database and screening advice needs adapting to CS venues (e.g. arXiv, ACL Anthology, IEEE Xplore, NeurIPS/ICML proceedings).
+- **Direct Connection to my Project:** Gives me a defensible protocol for finding and filtering the architecture/ablation papers, so my literature map (per Creswell Ch.2) is systematic rather than ad hoc.
+
+### Wortsman et al. (2024) - Small-scale proxies for large-scale Transformer training instabilities
+
+- **Full IEEE/APA Citation:** Wortsman, M., et al. (2024). Small-scale proxies for large-scale Transformer training instabilities. *ICLR 2024 (oral)*. arXiv:2309.14322. *(arXiv ID + venue confirmed; verify full author list before final use.)*
+- **Why did you select this paper?** The closest methodological template: it reproduces training instabilities (incl. attention-logit growth) in small models and introduces the *LR-sensitivity* metric I will use.
+- **Key Findings & Methodology:** qk-layernorm (+ z-loss) mitigate high-LR instabilities and transfer down to small models; dense learning-rate sweeps at small scale, confirmed with one large run.
+- **Limitations/Weaknesses Identified:** qk-norm is studied *bundled* with other fixes and framed as a *proxy* for large-scale behaviour, not isolated at <100M.
+- **Direct Connection to *Your* Project:** Defines both my method (LR-sensitivity, dense LR sweeps) and my niche (isolate QK-norm, μP off, dense curve at 10–100M).
+
+### IMU-1 (2026) - Sample-Efficient Pre-training of Small Language Models
+
+- **Full IEEE/APA Citation:** Grigorev, G., et al. (2026). IMU-1: Sample-Efficient Pre-training of Small Language Models. arXiv:2602.02522. *(arXiv ID, lead author, and "QK-norm = largest single architectural win" claim confirmed; preprint - verify any published version.)*
+- **Why did you select this paper?** Closest subject-matter prior art: its 70M ablations find QK-norm the single largest architectural win.
+- **Key Findings & Methodology:** At 70M, QK-norm reduces extreme activation kurtosis and gives the biggest single-intervention loss improvement - but bundled with μP, NorMuon, value residuals, etc., and measured via activation statistics, *not* an LR sweep.
+- **Limitations/Weaknesses Identified:** Fixed depth; QK-norm's LR effect entangled with μP; no dense LR sweep.
+- **Direct Connection to *Your* Project:** The paper I am disentangling - isolating QK-norm (μP off) and adding the LR sweep it did not do.
+
+### Henry et al. (2020) - Query-Key Normalization for Transformers
+
+- **Full IEEE/APA Citation:** Henry, A., et al. (2020). Query-Key Normalization for Transformers. *EMNLP 2020 Findings*. arXiv:2010.04245. *[verify]*
+- **Why did you select this paper?** Origin and definition of QK-norm (on small transformers, for low-resource machine translation).
+- **Key Findings & Methodology:** ℓ2-normalise queries and keys before the dot product; a learnable scale replaces 1/√d; fixes softmax saturation.
+- **Limitations/Weaknesses Identified:** Machine translation, not language modelling; small models but a different task.
+- **Direct Connection to *Your* Project:** Defines the exact technique under study.
+
+### Dehghani et al. (2023) - Scaling Vision Transformers to 22 Billion Parameters (ViT-22B)
+
+- **Full IEEE/APA Citation:** Dehghani, M., et al. (2023). Scaling Vision Transformers to 22 Billion Parameters. arXiv:2302.05442. *[verify full author list / venue]*
+- **Why did you select this paper?** The work usually credited with popularising QK-norm at scale, and my "why QK-norm matters in practice" citation.
+- **Key Findings & Methodology:** To train a 22B-parameter Vision Transformer without divergence, the authors normalise the queries and keys (QK-norm) before the attention dot product, curbing attention-logit growth that otherwise destabilised training; QK-norm is presented as an enabling stability fix at scale.
+- **Limitations/Weaknesses Identified:** Vision, not language modelling, and a single ultra-large model - it says nothing about whether QK-norm helps below 100M.
+- **Direct Connection to *Your* Project:** Establishes why the technique exists and matters at scale, leaving open exactly the small-scale, isolated question I address.
+
+### OLMo 2 Team (2025) - 2 OLMo 2 Furious
+
+- **Full IEEE/APA Citation:** OLMo 2 Team (Walsh, P., Soldaini, L., Groeneveld, D., et al.) (2025). 2 OLMo 2 Furious. arXiv:2501.00656. *[verify preferred citation format]*
+- **Why did you select this paper?** A fully-open, modern LM report showing QK-norm is now standard practice, not a niche trick.
+- **Key Findings & Methodology:** OLMo 2 applies RMSNorm to the query and key projections before attention (QK-norm) and uses a "reordered norm" (normalising layer *outputs* rather than inputs) plus z-loss; the authors report these normalisation changes prevent attention logits from growing too large and improve training stability and loss.
+- **Limitations/Weaknesses Identified:** Billion-scale; QK-norm is bundled with reordered-norm and z-loss, so its isolated contribution is not separable here.
+- **Direct Connection to *Your* Project:** Confirms contemporary adoption (relevance) and, again, bundles QK-norm with other stability fixes - precisely the confound my isolated ablation removes.
+
+### Bhatti (2025) - Architectural Trade-offs in Small Language Models Under Compute Constraints
+
+- **Full IEEE/APA Citation:** Bhatti, S. S. (2025). Architectural Trade-offs in Small Language Models Under Compute Constraints. arXiv:2512.20877. *(single-author preprint - verify before final use.)*
+- **Why did you select this paper?** The counter-evidence to the "QK-norm / modern-components help small models" line: it reports that techniques successful in large models do not necessarily transfer to the small regime.
+- **Key Findings & Methodology:** A systematic small-model study under strict compute budgets, building up from a linear next-token predictor through MLPs, self-attention and multi-layer transformers, evaluated on character-level Tiny Shakespeare and word-level PTB / WikiText-2 (test NLL vs parameters vs FLOPs). Finds attention beats MLPs per-FLOP even at small scale, that adding depth/context without enough optimisation can hurt, and - the key point for me - that large-model techniques do not automatically transfer down (demonstrated for rotary position embeddings).
+- **Limitations/Weaknesses Identified:** Very small char/word corpora and a progression-of-architectures design rather than an isolated component ablation; the clearest "no-transfer" result is for RoPE specifically. It does not isolate QK-norm and does not sweep the learning rate.
+- **Direct Connection to *Your* Project:** Supplies the "modern components don't automatically transfer to small scale" half of the contradiction that motivates my gap - but because it centres on RoPE and tiny datasets rather than an isolated QK-norm LR sweep, it leaves my exact question open.
+
+*Further papers to evaluate:* the mechanistic works (Sun et al. 2024, massive activations, arXiv:2402.17762; the attention-sink study, arXiv:2410.10781); Zhai et al. 2023 (attention entropy collapse, arXiv:2303.06296); the competing logit-control methods (Gemma 2 soft-capping; PaLM z-loss; NormFormer); and Yang et al. 2022 (μP, why LR is the axis, arXiv:2203.03466).
+
+*(Duplicate this block as you discover new literature throughout the course)*
+
+------------------------------------------------------------------------
+
+## 3. Generative AI & Digital Tool Transparency Log
+
+In line with academic integrity policies, any use of digital tools (AI search engines, LLMs, translation, or advanced editing software) to support your research process must be declared below. **Note: Using AI to write your final text from scratch is prohibited. Using tools for brainstorming, search, or copy-editing must be documented.**
+
+| Date | Tool Used (e.g., ChatGPT, Elicit, Claude) | Task Performed (e.g., Literature search, refining a sentence, brainstorming variables) | The Exact Prompt or Input Query Used | How you critically evaluated/modified the output before using it |
+|:--------------|:--------------|:--------------|:--------------|:--------------|
+| 08/07/2026 | Claude (Claude Code, Opus 4.8) | Summarising Week 1 & 2 course materials; brainstorming and scoping a research topic; scaffolding this notebook's structure and drafting starting points for the reflections | "Review the research notebook and course materials from both weeks and assess what work is needed by Friday"; "I want to explore how architectural changes affect LLM training/performance - help me scope it" | Chose to keep the research question deliberately broad rather than accept a premature narrowing; adopted the small-model feasibility constraint after weighing compute limits. |
+| 10/07/2026 | Claude (Claude Code, Opus 4.8) | Scoping the topic toward small-transformer architectural ablations; assessing Karpathy's nanochat as a candidate platform; outlining an initial pitch | "Explore how architectural changes affect LLM training/performance"; shared the nanochat repo link for assessment | Adopted the small-model/feasibility framing; treated the pitch outline as a scaffold to re-voice; sanity-checked nanochat's claims against its README before trusting them. |
+| 24/07/2026 | Claude (Claude Code, Opus 4.8) | Literature search on QK-norm and small-model architecture; assessing four candidate ablation directions; drafting a research-findings file, pitch slides and narration script | "What's been done in this area - does technique X hold on setting Y?"; "pin down what to ablate"; "are the pitch and script honest - do they need changes?" | **Fact-checked the outputs against primary sources and caught real errors** - a fabricated quote and two wrong venue attributions - which I corrected; rescoped the pitch from a novelty claim to an honest verification study; re-voiced all narration. Reference details remain flagged *[verify]* pending my own primary-source checks. |
+| 18/08/2026 | Claude (Claude Code, Opus 4.8) | Setting up the LaTeX/IEEE writing environment for the Introduction: creating the paper folder, a build script (`build.sh`) that auto-detects a TeX engine, a README, and annotating the IEEE template with plain-language comments defining unfamiliar terms | "Create the IEEE folder structure from the downloaded template"; "does LaTeX support comments? add comments defining terms the layman may not know"; "make build.sh optionally take an argument for which .tex to compile" | Read and understood each shell command before running it; installed the TeX engine (tectonic) myself and confirmed the script compiled the PDF; treated the template comments as private learning aids (they never appear in the output). No paper prose was AI-written. |
+| 20–21/08/2026 | Claude (Claude Code, Opus 4.8) | Converting my own hand-written Introduction draft (`draft.md`, my prose) into IEEE LaTeX (`draft.tex`) - a format-only conversion - and cross-checking that my in-text citation numbers pointed at the right references | "Convert my draft.md into a .tex file; make a new file, don't overwrite main.tex"; "verify I'm referencing the correct document in each place"; "remove references that are never used and update the numbering" | The prose is my own words, kept verbatim (typos included); AI only added LaTeX structure and mapped [n]→\cite. I reviewed the citation-accuracy report, acted on the flagged errors myself, and verified the two newly added sources (TinyStories, Cramming) against arXiv before accepting them. |
+| 21/08/2026 | Claude (Claude Code, Opus 4.8) | Auditing this notebook before submission - checking formatting/consistency, fixing citation-year/date/title mismatches, and scaffolding additional weekly-log and source-evaluation blocks for me to complete | "Is the notebook correctly formatted? what work is needed before submission?"; "do the consistency fixes"; "help me fill out the weekly logs and paper evaluations" | Accepted the mechanical fixes after checking each against my git history and the primary sources; wrote all reflective content and critical evaluations in my own words - AI supplied structure and factual paper summaries only (e.g. it re-checked OLMo 2 and the Bhatti paper against arXiv and flagged that I had overstated the latter), which I verified and re-voiced. |
+
+------------------------------------------------------------------------
+
+## 4. Weekly Activity & Reflection Logs
+
+Update this section at the end of *every* week. Keep reflections concise, focusing on **actions taken**, **obstacles hit**, and **next steps**.
+
+### Week 01: Focus Area - Research Approach Selection
+
+- **Activities Completed:**
+  - Reviewed Week 1 materials: the CS6513 introduction, Creswell (6e) Ch.1 *The Selection of a Research Approach*, and the articles by Vogt (2008), Finlay (2012), and Miller (2006).
+  - Studied the three research approaches (quantitative, qualitative, mixed methods) and the worldview → design → methods framework.
+- **Key Insights / Discoveries:**
+  - Vogt's "dictatorship of the problem" reframed method selection as problem-led rather than preference-led - directly relevant to keeping my topic feasible.
+  - My intended ML/LLM topic maps most naturally onto a quantitative/experimental design.
+- **Obstacles Encountered & How You Overcame Them:**
+  - No fixed thesis topic yet, this has been addressed by settling on a broad-but-directional area to refine over the block
+- **Plan for Next Week:**
+  - Engage with the Week 2 literature-review materials and continue shaping a working research question.
+
+### Week 02: Focus Area - Review of the Literature
+
+- **Activities Completed:**
+  - Reviewed Week 2 materials: Creswell Ch.2 *Review of the Literature* and the articles by Xiao & Watson (2019), Winchester & Salji (2016), and Basten (2018).
+  - Established a provisional working research question in the LLM-architecture space and recorded the feasibility constraints (small-model ablations) in Section 1.
+  - Started the Critical Source Evaluation Log and the GenAI Transparency Log.
+  - Explored Karpathy's *nanochat* repository (a hackable, single-node GPT training/inference pipeline) as a candidate platform for running the planned ablations. Built a small model as a trial run.
+  - Submitted the GitHub repository + research-notebook link on Brightspace.
+- **Key Insights / Discoveries:**
+  - Xiao & Watson's systematic-review steps give me a concrete, defensible process for the literature search I'll run over the coming weeks.
+  - Training the model on my machine took longer than anticipated. I will need to be careful to schedule time for when the model is training.
+- **Obstacles Encountered & How You Overcame Them:**
+  - Scope: "architectural changes to LLMs" is too broad and compute-infeasible. Narrowed the framing to single-lever ablations on small models and logged the scoping decision in the Pivot Log.
+- **Plan for Next Week:**
+  - Run an initial systematic search for architecture/ablation papers and add 3–4 to Section 2; begin narrowing toward one architectural lever, a dataset, and a metric set ahead of the Week 04 pitch.
+  - Assess nanochat (vs nanoGPT) as the experimental base: reproduce the baseline run and confirm what a single-lever architectural change would involve.
+
+### Week 03: Focus Area - Literature Search & Topic Exploration
+
+- **Activities Completed:**
+  - Ran a systematic search for architecture/ablation papers and explored four candidate ablation directions: (1) whether "modern tricks don't help at 70M" survives over-training; (2) leave-one-out of nanoGPT-speedrun tricks; (3) depth-vs-width under data constraints; (4) QK-normalization × learning rate.
+  - Assessed each for feasibility on my M4 Pro.
+- **Key Insights / Discoveries:**
+  - The over-training and speedrun-stack directions are compute- or tooling-infeasible on a laptop (cloud GPUs / CUDA-only kernels); the QK-norm and depth-vs-width directions are laptop-feasible.
+- **Obstacles Encountered & How You Overcame Them:**
+  - Had to narrow four different directions to choose from. Over-training suvival and leave-one-out speedruns require more compute than I have reasonably available. Additionally some nanogpt speedrun tactics require CUDA compatible GPUs, so they were removed from the choice pool. Leaving QK-norm and depth-vs-width as the laptop-feasible options.
+- **Plan for Next Week:**
+  - Narrow to a single feasible lever and prepare the Research Proposal Pitch.
+
+### Week 04: Focus Area - Narrowing & Research Proposal Pitch
+
+- **Activities Completed:**
+  - Narrowed to **QK-norm × learning rate at <100M** and ran prior-art verification (Wortsman 2024; IMU-1 2026; the contested small-model claims).
+  - Found the effect is already established, so honestly rescoped the study to a **verification + boundary-charting** design and switched the planned platform from nanochat to plain nanoGPT (μP off).
+  - Drafted and revised the 5-minute Research Proposal Pitch (slides + narration script) and this notebook.
+- **Key Insights / Discoveries:**
+  - Prior-art scrutiny matters: an initial "novel" framing did not survive it, and I had to catch and correct AI-assistant citation errors rather than trust them.
+- **Obstacles Encountered & How You Overcame Them:**
+  - Separating genuine novelty from a saturated area took several search passes and careful fact-checking.
+- **Plan for Next Week:**
+  - Fork nanoGPT, implement the isolated μP-off QK-norm toggle, reproduce a baseline, and run a pilot LR-sweep to validate the per-run time estimate.
+
+### Week 05: Focus Area
+
+This was a low-activity period.
+
+- **Activities Completed:**
+  - Limited progress this week. background reading, upskilling on ML fundementals.
+- **Key Insights / Discoveries:**
+  - No major discoveries, reinforced my understanding of ML, learned more about AlexNET etc.
+- **Obstacles Encountered & How You Overcame Them:**
+  - The Week-4 implementation plan (fork nanoGPT, μP-off toggle, pilot LR-sweep) did not start on schedule. Will start this after submission of the literature review and feedback is received.
+- **Plan for Next Week:**
+  - Likely little/no work will be done, personal events clash.
+
+### Week 06: Focus Area
+
+Low activity week, personal calendar conflicted.
+
+- **Activities Completed:**
+  - Reread some papaer abstracts to keep the project in my mind.
+- **Key Insights / Discoveries:**
+  - No major discoveries.
+- **Obstacles Encountered & How You Overcame Them:**
+  - Implementation and writing continued to slip. Personal calander clashed with the project. Aiming to catch up in the final week.
+- **Plan for Next Week:**
+  - Aim to complete literature review. Improve formatting and any errors in this document.
+
+### Week 07: Focus Area - Writing the Introduction & Mini Literature Review
+
+- **Activities Completed:**
+  - Set up an IEEE-format LaTeX writing pipeline (paper scaffold, `build.sh`, README) and compiled the PDF locally on the M4 Pro.
+  - Wrote the full Introduction + mini literature review in my own words, structured around the five required components (context/hook, literature context, gap, contribution + hypotheses, roadmap).
+  - Broadened the reference set from the core papers to the surrounding literature - the mechanism (massive activations / attention sinks), the competing logit-control methods (soft-capping, z-loss, NormFormer), and the data/feasibility precedents (TinyStories, Cramming) - mapping each citation to the specific claim it supports.
+  - Caught up and consolidated this notebook (expanded source evaluations, GenAI log, weekly logs).
+- **Key Insights / Discoveries:**
+  - Drafting the review made the shape of the gap concrete: the positive and negative small-model evidence disagree at roughly the same scale, yet neither isolates QK-norm or sweeps the learning rate - which sharpened my contribution.
+  - Citation-checking caught real problems (an uncited reference; and a paper - Bhatti 2025 - whose findings I had characterised too strongly), reinforcing that no reference is safe until read against the primary source.
+- **Obstacles Encountered & How You Overcame Them:**
+  - Recovering from the earlier low-activity period meant compressing a lot of writing and verification into a short window. Lost access to Scopus, was unable to resolve this. As a result, a handful of papers are unverified.
+- **Plan for Next Week:**
+  - Regain access to Scopus, reach out for support.
+  - Resolve the remaining *[verify]* citation details against Scopus / primary PDFs, correct the Bhatti characterisation in the draft Introduction, and return to the implementation that slipped (nanoGPT fork, μP-off QK-norm toggle, pilot LR-sweep).
+
+*(Continue adding weekly blocks as the module progresses)*
+
+[**NOTE:**]{.underline} Make sure to regular commits to the GitHub repository as the research notebook is updated. These commits will also form part of the evaluation of your work.

README.md

@@ -0,0 +1,34 @@
+# MSc Thesis — QK-Norm at Small Scale
+
+**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
+(`../Research Project/`, github.com/NikScorch/MSc_Research_Project) is untouched.
+This folder is not a git repo yet.
+
+## Layout
+
+| Folder | What's in it |
+|---|---|
+| `00-supervisor/` | Supervisor meeting notes and to-dos |
+| `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) |
+| `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) |
+
+### `03-cs6513-paper/` — which file is which
+
+| File | Status |
+|---|---|
+| `draft.md` → `draft.tex` / `draft.pdf` | **The submitted paper.** Cormac's own prose; `.tex` is a format conversion of the `.md` |
+| `main.tex` / `main.pdf` | Earlier bullet-point scaffold (AI-assisted outline, not submitted) |
+| `ref_draft.tex` / `ref_draft.pdf` | Personal AI-assisted reference draft — never for submission; kept out of the git repo |
+| `build.sh`, `IEEEtran.cls`, `README.md` | Build tooling: `./build.sh draft` |
+
+## Not copied
+
+- `../Research Project/msc-research-notebook-outline.md` — byte-identical duplicate of the `.rmd`
+- `.obsidian/`, `.DS_Store`, `.git/` — editor/system/VCS metadata