# Writing guide: section by section Read the section you are about to write, then write it into `paper/sections/.tex`. Every section is plain LaTeX without `\section` preamble tricks; cite with `\citep{key}` / `\citet{key}` (natbib), reference with `\cref{fig:x}` / `\cref{tab:x}` / `\cref{sec:x}`. General style - One claim per sentence; every claim is backed by (a) a table/figure of this paper, (b) a citation, or (c) a derivation. If none exists, delete the claim. - Use the method name macro `\ours{}` consistently. Define every symbol once, at first use. - Present tense for what the paper does ("we propose", "Table 2 shows"), past tense for what was run ("we trained for 100 epochs"). - No hype words: novel, groundbreaking, remarkable, significantly (unless a test was run), state-of-the-art (unless compared against the current best published number on the same benchmark with the same protocol, cited). - Numbers: same number of decimals as the table; units; "x pp" for percentage-point differences. Quote only values from `paper/data/summary.csv`, the generated tables, or a cited paper (then cite it in the same sentence). - Paragraphs open with a topic sentence; a reader who reads only first sentences gets the story. ## abstract.tex (150-250 words, no citations, no math) 1. Context + problem (1-2 sentences). 2. Gap: why existing methods fall short (1). 3. What we do: method in one sentence, the key idea (1-2). 4. Evidence: datasets and the headline result with a number (1-2). 5. Implication / what is released (1). ## introduction.tex (~1 page) - `\section{Introduction}\label{sec:intro}` - P1 Motivation: the task and why it matters, with 2-4 citations. - P2 The gap: what current approaches do and the specific limitation, cited. - P3 Our idea: the insight in plain words; optionally point to the teaser figure. - P4 Evidence: what was evaluated and the headline numbers. - Contributions as an `itemize` of 3-4 bullets, each concrete and checkable ("We show on X and Y that ... improves Z by N pp over B", not "extensive experiments"). - Optional last line: paper outline (skip if tight on space). ## related_work.tex (0.5-1 page) - `\section{Related Work}\label{sec:related}` - 2-4 `\paragraph{Theme.}` blocks, grouped by idea, not a list of papers. - Each paragraph: what the line of work does (cite 3-8 papers), then **how this paper differs** in one sentence. Never misrepresent a paper: describe only what its abstract/text (as summarised in research.md) says. - Cite the original paper of every baseline, dataset, model and major library used. ## method.tex (1.5-2.5 pages) - `\section{Method}\label{sec:method}` - Problem setting / notation first (`\subsection{Problem Setting}`): inputs, outputs, objective, as equations. - Then the method, in the order the code executes it. Every equation must match the code (loss terms, normalisations, hyperparameters as symbols). Read the code again while writing this; if the code differs from the "intended" method, describe the code. - An algorithm box (`algorithm` + `algorithmic`) if there is a training/inference loop. - Complexity / cost if relevant (params, FLOPs, wall-clock - only if measured). - Design choices: say why, ideally pointing to an ablation. ## experimental_setup.tex (~0.75 page) - `\section{Experimental Setup}\label{sec:setup}` Reproducibility details taken from the code/configs, not invented: - Datasets (cite each): size, splits, preprocessing, augmentation. - Baselines (cite each) and how they were tuned (same budget?). - Architecture, optimizer, learning rate + schedule, batch size, epochs/steps, weight decay, early stopping, seeds (number of runs), hardware and run time if logged. - Metrics, with direction (higher/lower is better) and how they are aggregated (mean +- std over N seeds; test-set at last epoch or at best validation epoch). - Put long hyperparameter tables in the appendix and reference them. ## results.tex (1.5-2.5 pages) - `\section{Results}\label{sec:results}` - `\subsection{Main Results}`: `\input{tables/main}` + 1 paragraph per finding. Each paragraph: observation -> numbers -> interpretation. Mention where the method does NOT win. - `\subsection{Ablations}` (if variant/ablation runs exist): what each component contributes, `\input{tables/ablation}` or a bar figure. - `\subsection{Analysis}`: learning curves, sensitivity to a hyperparameter, efficiency trade-off, qualitative examples - whatever the data supports. - Figures: `\begin{figure}[t]\centering\includegraphics[width=\linewidth]{figures/x.pdf} \caption{What is shown. What to notice. Shaded: +-1 std over N seeds.}\label{fig:x}\end{figure}`. Captions are self-contained; every figure/table is referenced in the text before it appears. - Significance: only call a difference significant if a test was run (the dagger marks in results.py tables); otherwise say "higher on average" and give std. ## discussion.tex (~0.5 page) - `\section{Discussion and Limitations}\label{sec:discussion}` - What the results mean, when to use the method, when not. - Limitations honestly and specifically: few seeds, small datasets, single domain, no hyperparameter search for baselines, compute limits, missing comparisons. Reviewers trust a paper more when it names its weaknesses. ## conclusion.tex (1 paragraph) - `\section{Conclusion}\label{sec:conclusion}` Restate the problem, the idea, the evidence; one sentence of future work. No new claims. ## statements.tex `\section*{Reproducibility Statement}`: where code/configs/seeds are, which sections/appendix give the details. `\section*{Broader Impact}` (or Ethics Statement): 3-6 honest sentences; dataset licences if relevant. Keep what the target venue requires, drop the rest. ## appendix.tex `\section{Additional Experimental Details}\label{app:details}` (full hyperparameter table, compute), `\section{Additional Results}` (per-dataset tables, more curves). Every appendix section is referenced from the main text. ## Title Specific and searchable: ": for " or a finding-style title (" improves under "). No question titles, no puns unless the user wants them. ## Self-review checklist (do it as a reviewer, write findings to paper/review.md) - Is the contribution clear after page 1? Are claims in abstract/intro all supported by results? - Are baselines fair (same data, budget, tuning)? Are the strongest relevant baselines there? - Seeds/variance reported? Test set used only for final numbers? - Are related works that do nearly the same thing cited and differentiated? - Do equations match the code? Notation consistent? All symbols defined? - Every figure readable in grayscale print, axes labelled with units, fonts >= 7pt? - Limitations stated? Anything over-claimed ("significantly", "state of the art")?