# Orchestra-Adapted Writing Principles

Use this reference when `paper-plan` needs help shaping the paper's story or when `paper-write` needs stronger drafting and revision guidance.

This is the expanded English counterpart to the detailed Chinese version. It is not a new workflow phase. Its purpose is to provide a stronger writing model on top of the existing `insleep` pipeline.

## Contents

- [When to Read](#when-to-read)
- [The Narrative Principle](#the-narrative-principle)
- [Time Allocation and Reviewer Reading Order](#time-allocation-and-reviewer-reading-order)
- [How to Write the Abstract](#how-to-write-the-abstract)
- [Introduction Structure](#introduction-structure)
- [Sentence-Level Clarity](#sentence-level-clarity)
- [Micro-Level Writing Tactics](#micro-level-writing-tactics)
- [Word Choice and Precision](#word-choice-and-precision)
- [Mathematical Writing](#mathematical-writing)
- [Figure Design](#figure-design)
- [Common Mistakes](#common-mistakes)
- [Pre-Submission Checklist](#pre-submission-checklist)

## When to Read

- Read before locking the framing of the paper.
- Read before drafting the Abstract and Introduction.
- Read when Related Work feels like a literature dump.
- Read when the prose feels generic, templated, or overly AI-shaped.
- Read when the structure looks fine on paper but the draft still feels unconvincing.

## The Narrative Principle

### Neel Nanda's Core View

A paper should be a **short, rigorous, evidence-backed technical story**, not a pile of experiments.

By the end of the Introduction, the reader should clearly understand:

- **The What**: the 1-3 specific claims the paper makes,
- **The Why**: the evidence that supports those claims,
- **The So What**: why the community should care.

### Andrej Karpathy's Complement

A strong paper “sells” **one thing** that was previously absent or non-obvious. The full paper should be organized around that single contribution.

### Practical Rules

- If the core contribution cannot be stated in one sentence, the framing has not converged.
- Every section should serve the same story instead of launching a second one.
- Experiments, related work, and discussion are there to support the main claim, not to operate as independent mini-papers.

### One-Sentence Contribution Test

If you cannot write something like the following, the framing is still too loose:

- “We prove that X converges under assumption Y.”
- “We show that method A improves B by 15% on benchmark C.”
- “We identify failure mode D and propose mechanism E that removes it.”

If the one-sentence contribution is hard to write, the usual causes are:

- the contribution is still too vague,
- the evidence is not yet tightly coupled to the claims,
- or the paper does not yet know what story it is telling.

## Time Allocation and Reviewer Reading Order

### Where Effort Should Go

A useful rule of thumb is to spend roughly the same amount of time on:

1. the Abstract,
2. the Introduction,
3. the Figures,
4. everything else combined.

This is not an exaggeration. Many reviewers form a preliminary judgment before they read the full methods section carefully.

### Common Reviewer Reading Order

Most reviewers encounter the paper in this order:

1. Title
2. Abstract
3. Introduction
4. Figures, especially Figure 1
5. The rest

### Writing Implications

- Put disproportionate effort into the title, abstract, introduction, and hero figure.
- Do not bury the main contribution after Section 3.
- Make the value of the paper legible before the reader reaches the full method.
- If the first two pages are unclear, later brilliance may never be seen.

## How to Write the Abstract

### Sebastian Farquhar's Five-Sentence Formula

Prefer a compact five-part abstract:

1. What you achieved
2. Why the problem is important and difficult
3. How you approached it
4. What evidence supports the claim
5. What number, result, or guarantee the reader should remember

### What a Good Abstract Should Do

- Enter the paper's specific contribution in the first one or two sentences.
- Include at least one explicit quantitative result.
- Be understandable without the main text.
- Avoid undefined acronyms.
- Avoid depending on citations to explain itself.

### A Good Abstract Sketch

```text
We prove that X converges linearly under assumption Y.
This addresses a long-standing question about why optimization remains stable in an apparently non-convex setting.
Our analysis reduces the training dynamics to Z, which yields a tractable theoretical structure.
We validate the prediction on datasets A and B and observe close agreement between theory and experiment.
Compared with prior methods, we reduce error by 15% and provide the first convergence guarantee in this setting.
```

### Openings to Delete

If the first sentence could fit almost any ML paper, delete it.

For example:

- “Large language models have achieved remarkable success...”
- “In recent years, deep learning has...”
- “Neural networks have revolutionized...”

The problem is not just that these openings sound stale. They carry **too little information** to help a reviewer judge the paper's specific contribution.

## Introduction Structure

### Basic Requirements

In two-column conference papers, the Introduction is usually best at about 1-1.5 pages.

It should satisfy the following:

- the method should start appearing by page 2-3 at the latest,
- the Introduction should include 2-4 contribution bullets,
- the central story should already make sense before technical detail arrives.

### Recommended Structure

1. **Opening hook**
   - What problem does the paper address?
   - Why does it matter now?

2. **Background / challenge**
   - Why is the problem hard?
   - What has prior work tried, and why is it insufficient?

3. **Approach overview**
   - What does this paper do differently?
   - What is the key insight?

4. **Contribution bullets**
   - 2-4 items
   - specific and falsifiable
   - ideally no longer than 1-2 lines each

5. **Results preview**
   - surface the strongest result early
   - tell the reader what is worth remembering

6. **Optional roadmap**
   - briefly describe the remaining sections

### Contribution Bullets: Good vs Bad

Good:

- We prove that X converges in O(n log n) under assumption Y.
- We introduce architecture Z, which reduces memory by 40%.
- We improve method A by 15% on benchmark C.

Bad:

- We study problem X.
- We perform extensive experiments.
- We make several contributions to the field.

The problem with the “bad” bullets is not grammar. It is that a reviewer cannot cleanly agree, disagree, or challenge them.

## Sentence-Level Clarity

### The Core Insight from Gopen and Swan

Readers have strong structural expectations about prose. If you repeatedly violate those expectations, readers spend effort decoding the sentence instead of understanding the idea.

### Seven Key Principles

#### 1. Keep Subject and Verb Close

Weak:

```text
The model, which was trained on 100M tokens and then fine-tuned with several domain-specific modifications, achieves strong results.
```

Strong:

```text
The model achieves strong results after training on 100M tokens and fine-tuning with domain-specific modifications.
```

#### 2. Put Important Information Near the End

Weak:

```text
Accuracy improves by 15% when using attention.
```

Strong:

```text
When using attention, accuracy improves by 15%.
```

#### 3. Put Context at the Start

Weak:

```text
A new attention mechanism is introduced to solve the alignment problem.
```

Strong:

```text
To address the alignment problem, we introduce a new attention mechanism.
```

#### 4. Move from Old to New

Readers track arguments more easily when the sentence begins with what is already familiar and ends with what is newly important.

#### 5. One Unit, One Function

- A paragraph should ideally do one main job.
- If a sentence is carrying two layers of logic at once, it probably wants to become two sentences.

#### 6. Put Actions in Verbs

Weak:

```text
We performed an analysis of the results.
```

Strong:

```text
We analyzed the results.
```

#### 7. Set the Stage Before New Material

Before presenting an equation, theorem, or experimental result, tell the reader why it matters.

### Fast Revision Questions

When revising a paragraph, ask:

- Is the subject separated from the verb by too much material?
- Does the sentence begin with context?
- Does the sentence end on the point that matters most?
- Is this paragraph trying to do two jobs at once?

## Micro-Level Writing Tactics

### Reduce Ambiguous Pronouns

When `this`, `it`, or `these` could be unclear, replace them with a specific noun.

Weak:

```text
This shows the method is robust.
```

Strong:

```text
These ablation results show that the method is robust to label noise.
```

### Move Verbs Earlier

Readers parse sentences faster when the main verb arrives early.

### Remove Low-Information Fillers

These words can usually be deleted:

- actually
- very
- really
- quite
- basically
- essentially
- Importantly,
- Notably,
- It is worth noting that

### Paragraph Shape

A useful paragraph skeleton is:

- first sentence: the point,
- middle: support,
- last sentence: reinforcement or transition.

Do not bury the key sentence in the middle.

## Word Choice and Precision

### Zachary Lipton Style: Remove Needless Hedging

Unless uncertainty is genuine, avoid overusing:

- may
- can
- might
- potentially

Excessive hedging often reads less like rigor and more like self-doubt.

### Replace Vague Terms with Specific Ones

| Vague Term | Better Alternative |
|-----------|--------------------|
| performance | accuracy / F1 / latency / throughput |
| improves | increases by X% / reduces by Y |
| large | 1B parameters / 100M tokens |
| fast | 3x faster / 50ms latency |
| good results | 92% accuracy / 0.85 F1 |

### Terminology Consistency

Do not rename the same concept across the paper.

For example, avoid mixing:

- model / network / architecture
- training / learning / optimization
- sample / instance / example

Choose the best term and keep it stable.

### Vocabulary Signaling

Some verbs make the work sound like a loose combination of existing pieces:

- combine
- modify
- extend
- expand

Stronger alternatives are often:

- develop
- propose
- introduce
- characterize

This is not about mechanical substitution. It is about how wording changes a reviewer's intuition about whether the work is a real contribution.

## Mathematical Writing

### Core Principle

The goal of mathematical writing is not to sound sophisticated. It is to let the reader **follow** the argument.

Prefer the following:

1. state assumptions formally before the theorem,
2. pair proofs and derivations with intuition,
3. keep notation consistent,
4. define symbols at first use.

### Recommended Notation Habits

```latex
% Scalars: lowercase italic
$x$, $y$, $\alpha$, $\beta$

% Vectors: lowercase bold
$\mathbf{x}$, $\mathbf{v}$

% Matrices: uppercase bold
$\mathbf{W}$, $\mathbf{X}$

% Sets: uppercase calligraphic
$\mathcal{X}$, $\mathcal{D}$

% Named functions: roman
$\mathrm{softmax}$, $\mathrm{ReLU}$
```

### Common Mathematical Writing Mistakes

- presenting equations without telling the reader why they matter,
- introducing assumptions too late,
- reusing symbols with different meanings across sections,
- moving all proof intuition to the appendix and leaving only bare statements in the main text.

For theory papers especially, **intuition and rigor** should coexist.

## Figure Design

### Why Figure 1 Matters

Figure 1 is often one of the first artifacts a reviewer studies after the abstract.

It should usually do at least one of the following:

- explain the core system or method idea,
- show the strongest comparison that justifies the paper,
- or provide the simplest visual summary of the main claim.

### Design Principles

1. **Figure 1 is crucial**
2. **captions should be self-contained**
3. **do not place a decorative title inside the figure**
4. **plots should use vector graphics whenever possible**

### Accessibility

Account for color-vision deficiency.

Do:

- use colorblind-safe palettes,
- avoid red-green pairings,
- make sure the figure still works in grayscale,
- use line styles and markers in addition to color.

### Caption Rules

- A reader should understand the point of the figure from the caption alone.
- State what is being compared.
- State what the reader should notice.
- Do not make the caption depend on the surrounding paragraph for essential meaning.

## Common Mistakes

### Structural Mistakes

| Mistake | Fix |
|--------|-----|
| Introduction longer than 1.5 pages | Move background to Related Work |
| Method buried too late | Front-load the contribution and compress the intro |
| Missing contribution bullets | Add 2-4 concrete claims |
| Experiments not tied to claims | State what each experiment tests |

### Writing Mistakes

| Mistake | Fix |
|--------|-----|
| Generic abstract opening | Start from the paper's actual contribution |
| Inconsistent terminology | Keep one name per concept |
| Too much passive voice | Prefer active constructions |
| Hedging everywhere | Keep hedging only where uncertainty is real |

### Figure Mistakes

| Mistake | Fix |
|--------|-----|
| Raster plots | Use PDF / EPS or other vector output |
| Red-green color schemes | Switch to colorblind-safe palettes |
| Titles inside figures | Move the title into the caption |
| Captions that require the main text | Rewrite them to be self-contained |

### Citation Mistakes

| Mistake | Fix |
|--------|-----|
| Related Work as paper-by-paper summary | Reorganize by method family or research question |
| Missing important references | Proactively expand the search |
| AI-generated citations | Use a verification workflow |
| Inconsistent key or style format | Normalize the bibliography |

## Pre-Submission Checklist

### Narrative

- [ ] The contribution can be stated in one sentence.
- [ ] The Introduction makes the What / Why / So What clear.
- [ ] Every major experiment supports a clear claim.

### Structure

- [ ] The abstract follows the five-sentence formula.
- [ ] The Introduction stays within about 1-1.5 pages.
- [ ] The method starts by page 2-3.
- [ ] There are 2-4 concrete contribution bullets.
- [ ] Limitations are clearly stated.

### Writing

- [ ] Terminology is consistent.
- [ ] There are no generic field-background openings.
- [ ] Unnecessary hedging has been removed.
- [ ] All key figures have self-contained captions.

### Technical

- [ ] Citations are verified.
- [ ] Error bars and statistical reporting are clear.
- [ ] Compute resources are documented.
- [ ] Code / data availability is stated.

## Final Sentence

**A paper is not just a written record of experiments. It is a technical conclusion organized into a story that a reviewer is willing to believe.**
