/aris-paper-plan

name: aris-paper-plan
description: "Generate a structured paper outline from review conclusions and experiment results. Use when user says \"写大纲\", \"paper outline\", \"plan the paper\", \"论文规划\", or wants to create a paper plan before writing."
argument-hint: "[topic-or-narrative-doc]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, WebSearch, WebFetch, mcp__codex__codex, mcp__codex__codex-reply
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

Paper Plan: From Review Conclusions to Paper Outline

Generate a structured, section-by-section paper outline from: **$ARGUMENTS**

Constants

• **REVIEWER_MODEL = `gpt-5.4`** — Model used via Codex MCP for outline review. Must be an OpenAI model.
• **TARGET_VENUE = `ICLR`** — Default venue. User can override (e.g., `/aris-paper-plan "topic" — venue: NeurIPS`). Supported: `ICLR`, `NeurIPS`, `ICML`, `CVPR`, `ACL`, `AAAI`, `ACM`, `IEEE_JOURNAL` (IEEE Transactions / Letters), `IEEE_CONF` (IEEE conferences).
• **MAX_PAGES** — Page limit. For ML conferences: main body to Conclusion end (excluding references, appendix). ICLR=9, NeurIPS=9, ICML=8. **For IEEE venues: references ARE included in page count.** IEEE journal Transactions ≈ 12-14 pages total, Letters ≈ 4-5 pages total; IEEE conference ≈ 5-8 pages total (including references).

Inputs

The skill expects one or more of these in the project directory:

1. **NARRATIVE_REPORT.md** or **STORY.md** — research narrative with claims and evidence 2. **GPT54_AUTO_REVIEW.md** — auto-review loop conclusions 3. **Experiment results** — JSON files in `figures/`, screen logs, tables 4. **IDEA_REPORT.md** — from idea-discovery pipeline (if applicable) 5. **Compact files** (if available): `IDEA_CANDIDATES.md`, `findings.md`, `EXPERIMENT_LOG.md` — preferred over full files when present, saves context window

If none exist, ask the user to describe the paper's contribution in 3-5 sentences.

Orchestra-Guided Writing Overlay

Keep the existing `insleep` workflow and outputs, but use the shared references below to improve the quality of the story and outline.

• Read `../ml-paper-writing/references/writing-guide.md` when framing the one-sentence contribution, Abstract, Introduction, Related Work, or hero figure.
• Read `../ml-paper-writing/references/checklists.md` before freezing the outline for a specific venue.
• Only load these references when needed; do not paste their full contents into the working draft.

Workflow

Step 1: Extract Claims and Evidence

**First check for `CLAIMS_FROM_RESULTS.md`** — if it exists (generated by `/aris-result-to-claim` at the end of Workflow 2), use it as the starting point for claims. This file contains validated claims already mapped to experiment evidence. Merge with any additional claims from the narrative documents below.

If `CLAIMS_FROM_RESULTS.md` does not exist, extract claims from scratch:

Read all available narrative documents and extract:

1. **Core claims** (3-5 main contributions) 2. **One-sentence contribution** (the single sentence that best states what the paper contributes) 3. **Evidence** for each claim (which experiments, which metrics, which figures) 4. **Known weaknesses** (from reviewer feedback) 5. **Suggested framing** (from review conclusions)

Build a **Claims-Evidence Matrix**:

| Claim | Evidence | Status | Section |
|-------|----------|--------|---------|
| [claim 1] | [exp A, metric B] | Supported | §3.2 |
| [claim 2] | [exp C] | Partially supported | §4.1 |

Step 2: Determine Paper Type and Structure

Based on TARGET_VENUE and paper content, classify and select structure.

Before committing to a structure, apply the narrative principle from `../ml-paper-writing/references/writing-guide.md`:

• The paper should tell one coherent technical story.
• By the end of the Introduction, the outline should make the **What**, **Why**, and **So What** explicit.
• Front-load the most important material: title, abstract, introduction, and hero figure. Reviewers often form a judgment before reading the full method.

**IMPORTANT**: The section count is FLEXIBLE (5-8 sections). Choose what fits the content best. The templates below are starting points, not rigid constraints.

**Empirical/Diagnostic paper:**

1. Introduction (1.5 pages)
2. Related Work (1 page)
3. Method / Setup (1.5 pages)
4. Experiments (3 pages)
5. Analysis / Discussion (1 page)
6. Conclusion (0.5 pages)

**Theory + Experiments paper:**

1. Introduction (1.5 pages)
2. Related Work (1 page)
3. Preliminaries & Modeling (1.5 pages)
4. Experiments (1.5 pages)
5. Theory Part A (1.5 pages)
6. Theory Part B (1.5 pages)
7. Conclusion (0.5 pages)
— Total: 9 pages

Theory papers often need 7 sections (splitting theory into estimation + optimization, or setup + analysis). The total page budget MUST sum to MAX_PAGES.

Theory papers should:

• Include **proof sketch** locations (not just theorem statements)
• Plan a **comparison table** of prior theoretical bounds vs. this paper's bounds
• Identify which proofs go in appendix vs. main body

**Method paper:**

1. Introduction (1.5 pages)
2. Related Work (1 page)
3. Method (2 pages)
4. Experiments (2.5 pages)
5. Ablation / Analysis (1 page)
6. Conclusion (0.5 pages)

Step 3: Section-by-Section Planning

For each section, specify:

### §0 Abstract
- **What we achieve**: [the paper's specific contribution, not field-level background]
- **Why it matters / is hard**: [why this problem is important and non-trivial]
- **How we do it**: [approach in one sentence]
- **Evidence**: [what supports the claim]
- **Most remarkable result**: [strongest quantitative or theoretical result]
- **Estimated length**: 150-250 words
- **Self-contained check**: can a reader understand this without the paper?

### §1 Introduction
- **Opening hook**: [1-2 sentences that motivate the problem]
- **Gap / challenge**: [what's missing in prior work, and why prior work is insuffi