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., /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. review-stage/AUTO_REVIEW.md — auto-review loop conclusions (fall back to ./AUTO_REVIEW.md if not found)
3. Experiment results — JSON files in figures/, screen logs, tables
4. idea-stage/IDEA_REPORT.md — from idea-discovery pipeline (if applicable) (fall back to ./IDEA_REPORT.md if not found)
5. Compact files (if available): idea-stage/IDEA_CANDIDATES.md (fall back to ./IDEA_CANDIDATES.md if not found), 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 ../shared-references/writing-principles.md when framing the one-sentence contribution, Abstract, Introduction, Related Work, or hero figure.
• Read ../shared-references/venue-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.

Discipline-Aware Mode

At startup, check for PRISM_PROFILE.md in the project root. If found, load it and adapt behavior:

1. Paper structure: Replace the default empirical/theory/method templates (Step 2) with the discipline's paper structure from the profile. Examples:
   • Law: Introduction → Doctrinal Framework → Normative Analysis → Comparative Law → Case Analysis → Reform Proposal → Conclusion
   • Economics: Introduction → Institutional Background → Data → Empirical Strategy → Results → Robustness → Conclusion
   • Medicine: Introduction → Methods → Results → Discussion (IMRAD)
   • Social Sciences (qualitative): Introduction → Literature Review → Methodology → Findings → Discussion → Conclusion
2. Page budgets: Use the discipline's section-level page allocations instead of ML defaults.
3. Citation style: Set the citation package and format per discipline (Bluebook for law, APA for social sciences, Vancouver for medicine, natbib for ML, IEEE numeric for EE).
4. Claims-Evidence Matrix: Adapt evidence types — for law: "statutory provision + judicial decision + comparative example"; for economics: "regression table + robustness check + placebo test"; for medicine: "primary outcome + forest plot + GRADE assessment".
5. Paper type classification: Use discipline-appropriate categories:
   • Law: doctrinal analysis / law-and-economics / comparative law / empirical legal study
   • Economics: empirical / theoretical / structural estimation
   • Social Sciences: quantitative / qualitative / mixed methods
   • Humanities: interpretive / historical / critical
6. Venue formatting: Load venue-specific rules for the target venue. When TARGET_VENUE matches a discipline-specific venue (e.g., 法学研究, AER, NEJM), load the corresponding formatting requirements from the discipline profile.

If PRISM_PROFILE.md does not exist, fall back to the default ML-oriented behavior (fully backward compatible).

See shared-references/prism-routing.md for the full routing protocol.

Workflow

Step 1: Extract Claims and Evidence

First check for CLAIMS_FROM_RESULTS.md — if it exists (generated by /argument-stress-test 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 ../shared-references/writing-principles.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 insufficient]
- **One-sentence contribution**: [the main takeaway of the paper]
- **Approach overview**: [what we do differently]
- **Key questions**: [the research questions this paper answers]
- **Contributions**: [2-4 numbered bullets, specific and falsifiable, matching Claims-Evidence Matrix]
- **Results preview**: [the strongest result or comparison to surface early]
- **Hero figure**: [describe what Figure 1 should show — MUST include clear comparison if applicable]
- **Estimated length**: 1.5 pages
- **Key citations**: [3-5 papers to cite here]
- **Front-loading check**: [would a skim reader know the main claim before reaching the method?]

### §2 Related Work
- **Subtopics**: [2-4 categories of related work]
- **Positioning**: [how this paper differs from each category]
- **Minimum length**: 1 full page (at least 3-4 paragraphs with substantive synthesis)
- **Organization rule**: organize by methodological family / assumption / question, not paper-by-paper
- **Must NOT be just a list** — synthesize, compare, and position

### §3 Method / Setup / Preliminaries
- **Notation**: [key symbols and their meanings]
- **Problem formulation**: [formal setup]
- **Method description**: [algorithm, model, or experimental design]
- **Formal statements**: [theorems, propositions if applicable]
- **Proof sketch locations**: [which key steps appear here vs. appendix]
- **Estimated length**: 1.5-2 pages

### §4 Experiments / Main Results
- **Figures planned**:
  - Fig 1: [description, type: bar/line/table/architecture, WHAT COMPARISON it shows]
  - Fig 2: [description]
  - Table 1: [what it shows, which methods/baselines compared]
- **Data source**: [which JSON files / experiment results]

### §5 Conclusion
- **Restatement**: [contributions rephrased, not copy-pasted from intro]
- **Limitations**: [honest assessment — reviewers value this]
- **Future work**: [1-2 concrete directions]
- **Estimated length**: 0.5 pages

Step 4: Figure Plan

List every figure and table:

## Figure Plan

| ID | Type | Description | Data Source | Priority |
|----|------|-------------|-------------|----------|
| Fig 1 | Hero/Architecture | System overview + comparison | manual | HIGH |
| Fig 2 | Line plot | Training curves comparison | figures/exp_A.json | HIGH |
| Fig 3 | Bar chart | Ablation results | figures/ablation.json | MEDIUM |
| Table 1 | Comparison table | Main results vs. baselines | figures/main_results.json | HIGH |
| Table 2 | Theory comparison | Prior bounds vs. ours | manual | HIGH (theory papers) |

CRITICAL for Figure 1 / Hero Figure: Describe in detail what the figure should contain, including:

• Which methods are being compared
• What the visual difference should demonstrate
• Caption draft that clearly states the comparison
• Why the figure helps a skim reader understand the paper before reading the full method

Step 5: Citation Scaffolding

For each section, list required citations:

## Citation Plan
- §1 Intro: [paper1], [paper2], [paper3] (problem motivation)
- §2 Related: [paper4]-[paper10] (categorized by subtopic)
- §3 Method: [paper11] (baseline), [paper12] (technique we build on)

Citation rules (from claude-scholar + Imbad0202/academic-research-skills):

1. NEVER generate BibTeX from memory — always verify via search or existing .bib files
2. Every citation must be verified: correct authors, year, venue
3. Flag any citation you're unsure about with [VERIFY]
4. Prefer published versions over arXiv preprints when available

Step 6: Cross-Review with REVIEWER_MODEL

Send the complete outline to GPT-5.4 xhigh for feedback:

mcp__codex__codex:
  model: gpt-5.4
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    Review this paper outline for a [VENUE] submission.
    [full outline including Claims-Evidence Matrix]

    Score 1-10 on:
    1. Logical flow — does the story build naturally?
    2. Claim-evidence alignment — every claim backed?
    3. Missing experiments or analysis
    4. Positioning relative to prior work
    5. Page budget feasibility (MAX_PAGES = main body to Conclusion end, excluding refs/appendix)
    6. Front-matter strength — are the abstract, introduction, and hero figure plan strong enough for skim-reading reviewers?

    For each weakness, suggest the MINIMUM fix.
    Be specific and actionable — "add X" not "consider more experiments".

Apply feedback before finalizing.

Step 7: Output

Save the final outline to PAPER_PLAN.md in the project root:

# Paper Plan

**Title**: [working title]
**One-sentence contribution**: [single-sentence statement of the paper's core takeaway]
**Venue**: [target venue]
**Type**: [empirical/theory/method]
**Date**: [today]
**Page budget**: [MAX_PAGES] pages (main body to Conclusion end, excluding references & appendix)
**Section count**: [N] (must match the number of section files that will be created)

## Claims-Evidence Matrix
[from Step 1]

## Structure
[from Step 2-3, section by section]

## Figure Plan
[from Step 4, with detailed hero figure description]

## Citation Plan
[from Step 5]

## Reviewer Feedback
[from Step 6, summarized]

## Next Steps
- [ ] /paper-figure to generate all figures
- [ ] /paper-write to draft LaTeX
- [ ] /paper-compile to build PDF

Key Rules

• Large file handling: If the Write tool fails due to file size, immediately retry using Bash (cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.
• Do NOT generate author information — leave author block as placeholder or anonymous
• Be honest about evidence gaps — mark claims as "needs experiment" rather than overclaiming
• Page budget is hard — if content exceeds MAX_PAGES, suggest what to move to appendix
• MAX_PAGES counting differs by venue — ML conferences: main body to Conclusion end, references/appendix NOT counted. IEEE venues: references ARE counted toward the page limit.
• Venue-specific norms — ML conferences (ICLR/NeurIPS/ICML) use natbib (\citep/\citet); IEEE venues use cite package (\cite{}, numeric style)
• Claims-Evidence Matrix is the backbone — every claim must map to evidence, every experiment must support a claim
• Front-load the story — the outline should make the contribution clear in the title, abstract, introduction, and hero figure before the reader reaches the full method
• Figures need detailed descriptions — especially the hero figure, which must clearly specify comparisons and visual expectations
• Section count is flexible — 5-8 sections depending on paper type. Don't force content into a rigid 5-section template.

Acknowledgements

Outline methodology inspired by Research-Paper-Writing-Skills (claim-evidence mapping), claude-scholar (citation verification), and Imbad0202/academic-research-skills (claim verification protocol). The writing-framing overlay in this hybrid pack is adapted from Orchestra Research's paper-writing guidance.

Output Protocols

Follow these shared protocols for all output files:

• Output Versioning Protocol — write timestamped file first, then copy to fixed name
• Output Manifest Protocol — log every output to MANIFEST.md
• Output Language Protocol — respect the project's language setting