/aris-auto-paper-improvement-loop

name: aris-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
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

Auto Paper Improvement Loop: Review → Fix → Recompile

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

Context

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

Unlike `/aris-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: `/aris-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:

{
  "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

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:

# 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 `/aris-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

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:
  th