---
name: auto-paper-improvement-loop
description: "Autonomously improve a generated paper via GPT-5.4 xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper."
argument-hint: [paper-directory]
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, mcp__codex__codex, mcp__codex__codex-reply
---

# Auto Paper Improvement Loop: Review → Fix → Recompile

Autonomously improve the paper at: **$ARGUMENTS**

## Context

This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review.

Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.

## Constants

- **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements.
- **REVIEWER_MODEL = `gpt-5.4`** — Model used via Codex MCP for paper review.
- **REVIEW_LOG = `PAPER_IMPROVEMENT_LOG.md`** — Cumulative log of all rounds, stored in paper directory.
- **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. When `false` (default), runs fully autonomously.

> 💡 Override: `/auto-paper-improvement-loop "paper/" — human checkpoint: true`

## Inputs

1. **Compiled paper** — `paper/main.pdf` + LaTeX source files
2. **All section `.tex` files** — concatenated for review prompt

## State Persistence (Compact Recovery)

If the context window fills up mid-loop, Claude Code auto-compacts. To recover, this skill writes `PAPER_IMPROVEMENT_STATE.json` after each round:

```json
{
  "current_round": 1,
  "threadId": "019ce736-...",
  "last_score": 6,
  "status": "in_progress",
  "timestamp": "2026-03-13T21:00:00"
}
```

**On startup**: if `PAPER_IMPROVEMENT_STATE.json` exists with `"status": "in_progress"` AND `timestamp` is within 24 hours, read it + `PAPER_IMPROVEMENT_LOG.md` to recover context, then resume from the next round. Otherwise (file absent, `"status": "completed"`, or older than 24 hours), start fresh.

**After each round**: overwrite the state file. **On completion**: set `"status": "completed"`.

## Workflow

### Step 0: Preserve Original

```bash
cp paper/main.pdf paper/main_round0_original.pdf
```

### Step 1: Collect Paper Text

Concatenate all section files into a single text block for the review prompt:

```bash
# Collect all sections in order
for f in paper/sections/*.tex; do
    echo "% === $(basename $f) ==="
    cat "$f"
done > /tmp/paper_full_text.txt
```

### Step 2: Round 1 Review

Send the full paper text to GPT-5.4 xhigh:

```
mcp__codex__codex:
  model: gpt-5.4
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are reviewing a [VENUE] paper. Please provide a detailed, structured review.

    ## Full Paper Text:
    [paste concatenated sections]

    ## Review Instructions
    Please act as a senior ML reviewer ([VENUE] level). Provide:
    1. **Overall Score** (1-10, where 6 = weak accept, 7 = accept)
    2. **Summary** (2-3 sentences)
    3. **Strengths** (bullet list, ranked)
    4. **Weaknesses** (bullet list, ranked: CRITICAL > MAJOR > MINOR)
    5. **For each CRITICAL/MAJOR weakness**: A specific, actionable fix
    6. **Missing References** (if any)
    7. **Verdict**: Ready for submission? Yes / Almost / No

    Focus on: theoretical rigor, claims vs evidence alignment, writing clarity,
    self-containedness, notation consistency.
```

Save the threadId for Round 2.

### Step 2b: Human Checkpoint (if enabled)

**Skip if `HUMAN_CHECKPOINT = false`.**

Present the review results and wait for user input:

```
📋 Round 1 review complete.

Score: X/10 — [verdict]
Key weaknesses (by severity):
1. [CRITICAL] ...
2. [MAJOR] ...
3. [MINOR] ...

Reply "go" to implement all fixes, give custom instructions, "skip 2" to skip specific fixes, or "stop" to end.
```

Parse user response same as `/auto-review-loop`: approve / custom instructions / skip / stop.

### Step 3: Implement Round 1 Fixes

Parse the review and implement fixes by severity:

**Priority order:**
1. CRITICAL fixes (assumption mismatches, internal contradictions)
2. MAJOR fixes (overclaims, missing content, notation issues)
3. MINOR fixes (if time permits)

**Common fix patterns:**

| Issue | Fix Pattern |
|-------|-------------|
| Assumption-model mismatch | Rewrite assumption to match the model, add formal proposition bridging the gap |
| Overclaims | Soften language: "validate" → "demonstrate practical relevance", "comparable" → "qualitatively competitive" |
| Missing metrics | Add quantitative table with honest parameter counts and caveats |
| Theorem not self-contained | Add "Interpretation" paragraph listing all dependencies |
| Notation confusion | Rename conflicting symbols globally, add Notation paragraph |
| Missing references | Add to `references.bib`, cite in appropriate locations |
| Theory-practice gap | Explicitly frame theory as idealized; add synthetic validation subsection |

### Step 4: Recompile Round 1

```bash
cd paper && latexmk -C && latexmk -pdf -interaction=nonstopmode -halt-on-error main.tex
cp main.pdf main_round1.pdf
```

Verify: 0 undefined references, 0 undefined citations.

### Step 5: Round 2 Review

Use `mcp__codex__codex-reply` with the saved threadId:

```
mcp__codex__codex-reply:
  threadId: [saved from Round 1]
  model: gpt-5.4
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [Round 2 update]

    Since your last review, we have implemented:
    1. [Fix 1]: [description]
    2. [Fix 2]: [description]
    ...

    Please re-score and re-assess. Same format:
    Score, Summary, Strengths, Weaknesses, Actionable fixes, Verdict.
```

### Step 5b: Human Checkpoint (if enabled)

**Skip if `HUMAN_CHECKPOINT = false`.** Same as Step 2b — present Round 2 review, wait for user input.

### Step 6: Implement Round 2 Fixes

Same process as Step 3. Typical Round 2 fixes:
- Add controlled synthetic experiments validating theory
- Further soften any remaining overclaims
- Formalize informal arguments (e.g., truncation → formal proposition)
- Strengthen limitations section

### Step 7: Recompile Round 2

```bash
cd paper && latexmk -C && latexmk -pdf -interaction=nonstopmode -halt-on-error main.tex
cp main.pdf main_round2.pdf
```

### Step 8: Format Check

After the final recompilation, run a format compliance check:

```bash
# 1. Page count vs venue limit
PAGES=$(pdfinfo paper/main.pdf | grep Pages | awk '{print $2}')
echo "Pages: $PAGES (limit: 9 main body for ICLR/NeurIPS)"

# 2. Overfull hbox warnings (content exceeding margins)
OVERFULL=$(grep -c "Overfull" paper/main.log 2>/dev/null || echo 0)
echo "Overfull hbox warnings: $OVERFULL"
grep "Overfull" paper/main.log 2>/dev/null | head -10

# 3. Underfull hbox warnings (loose spacing)
UNDERFULL=$(grep -c "Underfull" paper/main.log 2>/dev/null || echo 0)
echo "Underfull hbox warnings: $UNDERFULL"

# 4. Bad boxes summary
grep -c "badness" paper/main.log 2>/dev/null || echo "0 badness warnings"
```

**Auto-fix patterns:**

| Issue | Fix |
|-------|-----|
| Overfull hbox in equation | Wrap in `\resizebox` or split with `\split`/`aligned` |
| Overfull hbox in table | Reduce font (`\small`/`\footnotesize`) or use `\resizebox{\linewidth}{!}{...}` |
| Overfull hbox in text | Rephrase sentence or add `\allowbreak` / `\-` hints |
| Over page limit | Move content to appendix, compress tables, reduce figure sizes |
| Underfull hbox (loose) | Rephrase for better line filling or add `\looseness=-1` |

If any overfull hbox > 10pt is found, fix it and recompile before documenting.

### Step 9: Document Results

Create `PAPER_IMPROVEMENT_LOG.md` in the paper directory:

```markdown
# Paper Improvement Log

## Score Progression

| Round | Score | Verdict | Key Changes |
|-------|-------|---------|-------------|
| Round 0 (original) | X/10 | No/Almost/Yes | Baseline |
| Round 1 | Y/10 | No/Almost/Yes | [summary of fixes] |
| Round 2 | Z/10 | No/Almost/Yes | [summary of fixes] |

## Round 1 Review & Fixes

<details>
<summary>GPT-5.4 xhigh Review (Round 1)</summary>

[Full raw review text, verbatim]

</details>

### Fixes Implemented
1. [Fix description]
2. [Fix description]
...

## Round 2 Review & Fixes

<details>
<summary>GPT-5.4 xhigh Review (Round 2)</summary>

[Full raw review text, verbatim]

</details>

### Fixes Implemented
1. [Fix description]
2. [Fix description]
...

## PDFs
- `main_round0_original.pdf` — Original generated paper
- `main_round1.pdf` — After Round 1 fixes
- `main_round2.pdf` — Final version after Round 2 fixes
```

### Step 9: Summary

Report to user:
- Score progression table
- Number of CRITICAL/MAJOR/MINOR issues fixed per round
- Final page count
- Remaining issues (if any)

### Feishu Notification (if configured)

After each round's review AND at final completion, check `~/.claude/feishu.json`:
- **After each round**: Send `review_scored` — "Round N: X/10 — [key changes]"
- **After final round**: Send `pipeline_done` — score progression table + final page count
- If config absent or mode `"off"`: skip entirely (no-op)

## Output

```
paper/
├── main_round0_original.pdf    # Original
├── main_round1.pdf             # After Round 1
├── main_round2.pdf             # After Round 2 (final)
├── main.pdf                    # = main_round2.pdf
└── PAPER_IMPROVEMENT_LOG.md    # Full review log with scores
```

## 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.

- **Preserve all PDF versions** — user needs to compare progression
- **Save FULL raw review text** — do not summarize or truncate GPT-5.4 responses
- **Use `mcp__codex__codex-reply`** for Round 2 to maintain conversation context
- **Always recompile after fixes** — verify 0 errors before proceeding
- **Do not fabricate experimental results** — synthetic validation must describe methodology, not invent numbers
- **Respect the paper's claims** — soften overclaims rather than adding unsupported new claims
- **Global consistency** — when renaming notation or softening claims, check ALL files (abstract, intro, method, experiments, theory sections, conclusion, tables, figure captions)

## Typical Score Progression

Based on end-to-end testing on a 9-page ICLR 2026 theory paper:

| Round | Score | Key Improvements |
|-------|-------|-----------------|
| Round 0 | 4/10 (content) | Baseline: assumption-model mismatch, overclaims, notation issues |
| Round 1 | 6/10 (content) | Fixed assumptions, softened claims, added interpretation, renamed notation |
| Round 2 | 7/10 (content) | Added synthetic validation, formal truncation proposition, stronger limitations |
| Round 3 | 5→8.5/10 (format) | Removed hero fig, appendix, compressed conclusion, fixed overfull hbox |

**+4.5 points across 3 rounds** (2 content + 1 format) is typical for a well-structured but rough first draft. Final: 8 pages main body, 0 overfull hbox, ICLR-compliant.