review-ral

name

review-ral

description

IEEE RA-L 论文审稿助手。输入一篇待审稿 PDF，自动完成：初读论文提取关键信息 → 多源文献检索 (Semantic Scholar + WebSearch/arXiv + vec-db) → 并行 agent 深读相关论文 → 带着领域知识精读 待审稿论文 → 输出完整的 RA-L 审稿意见（含评分、推荐、双语评审意见）。审稿风格追求独到犀利， 不千篇一律。Use PROACTIVELY whenever the user asks to review a paper for RA-L, IEEE Robotics and Automation Letters, or says "审稿", "review this paper", "帮我审稿", "写review", "RA-L review", "审一下这篇", "peer review", or provides a PDF and mentions reviewing. Also trigger when user mentions PaperCept, reviewer form, or review deadline.

IEEE RA-L Paper Review Skill

Generate expert-level, incisive peer reviews for IEEE Robotics and Automation Letters submissions.

The core philosophy: a great review comes from deep domain knowledge, not templates. First understand the field landscape through targeted literature search, then critique the paper from a position of genuine expertise. This produces reviews with unique insights that authors actually find useful — not generic checklists that could apply to any paper.

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Overview of the Pipeline

Phase 1: Initial Read     → Extract paper's claims, methods, key results, field keywords
Phase 2: Literature Search → Multi-source search for related work (3 sources, parallel)
Phase 3: Deep Read Related → Download top papers, parallel agents read them
Phase 4: Expert Re-read   → Re-read target paper armed with domain knowledge
Phase 5: Generate Review  → Bilingual review output matching RA-L form exactly

Total expected time: 5-10 minutes depending on search depth.

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Phase 1: Initial Paper Read

Read the submitted PDF to build a first-pass understanding.

1.1 Read the PDF

Use the Read tool to read the PDF file. If it's long, read in sections. Extract:

1. Title & Abstract — verbatim
2. Claimed contributions — list each claim the authors make (usually in intro or contributions section)
3. Method summary — what they actually did, in 3-5 sentences
4. Key results — main quantitative results, benchmarks used, metrics reported
5. Datasets — which datasets, train/test splits, evaluation protocols
6. Baselines compared — which methods they compare against, and how recent those baselines are
7. Field keywords — 8-12 specific terms for literature search (method names, problem names, dataset names, technique names)

1.2 Identify Search Targets

Based on the initial read, formulate:

• 3-4 core search queries — the paper's main topic and close variants
• 2-3 method-specific queries — the specific technique family (e.g., "iterative refinement stereo matching", "cost volume stereo")
• 1-2 application queries — the downstream application if relevant
• Competing method names — specific method names mentioned as baselines, to find their papers and successors

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Phase 2: Literature Search (Parallel)

Launch all search sources in parallel in a single message. The goal is to find 20-30 candidate papers, from which we'll select 8-12 for deep reading.

2.1 Search Sources

Source A: Semantic Scholar API

# High-citation classics
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=<URL_ENCODED_QUERY>&limit=20&fields=title,year,authors,citationCount,externalIds,abstract&sort=citationCount:desc"

# Recent papers (2023-2026)
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=<URL_ENCODED_QUERY>&limit=20&fields=title,year,authors,citationCount,externalIds,abstract&year=2023-2026"

# Citation graph: find papers that cite the key baselines
curl -s "https://api.semanticscholar.org/graph/v1/paper/ArXiv:<ID>?fields=citations.title,citations.year,citations.citationCount,citations.externalIds"

Rate limit: 5000 req/5min. Space bulk queries by 0.5s. If 429, wait 3s and retry once.

Run 3-5 query variants covering the core topic + method family + competing approaches.

Source B: WebSearch (arXiv focus)

Run 6-8 targeted web searches:

• "<topic> arXiv 2024 2025" — recent preprints
• "<method name> survey" — find survey papers
• "<baseline method> improved OR better OR outperform 2024 2025" — find papers that beat the baselines
• "<dataset name> state-of-the-art SOTA 2024 2025" — current SOTA on the benchmarks
• Include top venue filters: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, IROS, ICRA, CoRL, RA-L, T-RO

Source C: Vec-db Semantic Search (if available)

cd ${VECDB_PATH:-/home/vla-reasoning/proj/litian-research/vec-db}
npx tsx src/cli.ts search "<query>" --top 15

Run 4-6 diverse queries. Score >0.25 = relevant, >0.35 = highly relevant. If vec-db is not available, skip gracefully and rely on Sources A and B.

2.2 Paper Selection

From all search results, select 8-12 papers for deep reading, prioritizing:

1. Direct competitors — papers solving the exact same problem with different methods (highest priority)
2. Very recent work (2024-2026) — papers the authors may have missed
3. Foundational/highly cited — seminal papers the submission should cite
4. Same-benchmark SOTA — papers reporting results on the same datasets
5. Method-family papers — papers using similar techniques in different domains

For each selected paper, record: title, authors, year, venue, arXiv ID (if available), why it's relevant.

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Phase 3: Deep Read Related Papers (Parallel Agents)

This is the key step that separates shallow reviews from expert ones. Spawn parallel agents to read 8-12 related papers simultaneously.

3.1 Access Paper Full Text

For each selected paper, try in order:

1. AlphaXiv (preferred — structured markdown): https://alphaxiv.org/abs/<ARXIV_ID>.md
2. Direct PDF download: wget https://arxiv.org/pdf/<ARXIV_ID> -O /tmp/papers/<ARXIV_ID>.pdf
3. WebFetch on paper page: if no arXiv ID, try fetching the paper's URL

Create a working directory for downloaded papers:

mkdir -p /tmp/ral-review-papers/

3.2 Spawn Parallel Reading Agents

Launch all reading agents in ONE message. Each agent reads one paper and extracts:

Agent prompt template:

Read this paper and extract a structured summary for peer review comparison purposes.

Paper: <title>
Source: <alphaxiv URL or PDF path>

Extract:
1. **Core contribution**: What is the main idea? (2-3 sentences)
2. **Method details**: Key technical approach, architecture, loss functions
3. **Results on shared benchmarks**: Performance numbers on <list relevant benchmarks from target paper>
4. **Strengths**: What does this paper do well?
5. **Limitations**: What are the known weaknesses?
6. **Comparison points**: How does this relate to <target paper title>? What does it do differently?
7. **Key numbers**: Report specific metrics (e.g., EPE, D1-error, FPS, parameters, FLOPs)

Write the summary to: /tmp/ral-review-papers/summary_<paper_id>.md

3.3 Synthesize Domain Knowledge

After all agents complete, read all summaries and build a field landscape:

• Current SOTA on each benchmark the target paper uses
• Common techniques and their trade-offs
• Open problems and active research directions
• What's actually novel vs. what's incremental vs. what's already known
• Missing references the target paper should cite

Write this synthesis to /tmp/ral-review-papers/field_landscape.md.

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Phase 4: Expert Re-read

Now re-read the target paper with deep domain knowledge. This time, read critically:

4.1 Novelty Assessment

• Which claimed contributions are genuinely new vs. already explored in related work?
• Is the combination of known techniques actually novel, or just engineering?
• Does the paper clearly distinguish its contribution from prior work?

4.2 Technical Scrutiny

Apply the critical lens from references/review-philosophy.md. Key angles:

• Hidden assumptions: What assumptions does the method rely on that aren't stated?
• Experimental fairness: Are baselines compared under identical conditions? Same hardware, same training data, same evaluation protocol?
• Cherry-picking: Do they only report metrics where they win? Missing metrics that would show weaknesses?
• Ablation rigor: Does the ablation study truly isolate each contribution? Or are components entangled?
• Statistical significance: Are improvements within noise range? Do they report variance/std?
• Scalability: Do claims generalize beyond the specific benchmarks tested?
• Computational cost: Is the speed/accuracy trade-off actually favorable when properly measured?
• Reproducibility: Are enough details provided to reproduce? Missing hyperparameters, training details?

4.3 Presentation Quality

• Is the writing clear and self-contained?
• Are figures informative or decorative?
• Is the paper well-structured?
• Are there grammatical issues that impede understanding?

4.4 Citation Completeness

Compare the paper's references against the field landscape:

• Missing important baselines or comparisons?
• Missing foundational work?
• Missing very recent relevant work (which is understandable but worth noting)?
• Self-citation bias?

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Phase 5: Generate Review Output

Produce the final review following the exact RA-L form structure. Output two files:

5.1 English Review (for submission)

Write to <output_dir>/review_en.md — this is what gets pasted into PaperCept.

Use the exact template from references/review-template.md.

5.2 Chinese Analysis Notes (for reviewer's reference)

Write to <output_dir>/review_cn.md — the reviewer's personal analysis notes.

This includes:

• 中文审稿思路分析
• 每个评分的详细理由
• 相关论文对比表格
• 论文的核心优缺点
• 对领域的价值判断

5.3 Review Quality Checklist

Before finalizing, verify:

• Every criticism is specific — points to exact section/figure/table/equation
• Every claim of "missing reference" includes the actual reference
• No generic phrases like "the paper is well-written" without specific evidence
• Technical issues are explained clearly enough for authors to understand and fix
• The review tone is professional and constructive, even when critical
• Comments to Author do NOT reveal the recommendation
• No identifying information about the reviewer
• Comments to Author ≤ 10000 characters
• Confidential Comments ≤ 2000 characters
• All 7 assessment scores are filled with justification

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Assessment Scoring Guide

These are the exact options from the RA-L PaperCept form:

Paper contribution

• Exceptional — groundbreaking new direction or major theoretical/practical advance
• Major — significant contribution that clearly advances the field
• Minor — incremental improvement or limited novelty
• Questionable — unclear contribution or already known results
• None — no discernible contribution

Technical quality / Originality / Thoroughness of results / Clarity of presentation / Adequacy of citation / Relevance to field

Each uses: Excellent / Good / Fair / Poor

Overall Recommendation

• Accept — solid work, ready for publication with at most minor edits
• Revise and resubmit — has merit but needs significant improvements
• Reject — fundamental issues that cannot be fixed in revision
• Unsuitable due to scope — doesn't fit RA-L's scope

Reviewer Confidence

• Very confident — deep expertise in this exact sub-area
• Confident — solid knowledge of the broader field
• Fairly confident — familiar with the area but not an expert
• Not very confident — limited knowledge of this specific topic
• No confidence — outside area of expertise

Other Fields

• Multimedia attachment: Yes / No (and brief justification)
• Best paper award finalist: Yes / No
• Wish to see revision: checkbox (recommend checking if "Revise and resubmit")

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Writing Style for Reviews

Read references/review-philosophy.md for the full guide. Key principles:

1. Be specific, never generic. Instead of "the experiments are insufficient", say "Table 2 is missing comparison with RAFT-Stereo [ref] on KITTI 2015, which currently holds SOTA on the leaderboard."

2. Provide evidence for every claim. If you say the method is not novel, cite the specific prior work that already did it.

3. Distinguish fatal flaws from fixable issues. Major issues that affect the core claims should be clearly separated from minor presentation issues.

4. Be constructive even when rejecting. Tell the authors exactly what they'd need to do to make the paper publishable.

5. Find what's genuinely good. Even weak papers usually have some redeeming quality. Acknowledge it — this makes your criticism more credible.

6. Question the things others wouldn't. Go beyond surface-level checking. Ask: "Why this architecture choice and not the obvious alternative?" "What happens at the failure cases?" "Is the improvement consistent across all scenes or driven by a few easy cases?"

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Output Directory Structure

<output_dir>/
├── review_en.md              ← English review for PaperCept submission
├── review_cn.md              ← 中文审稿分析笔记
├── field_landscape.md        ← Domain knowledge synthesis
└── related_papers/           ← Summaries of related papers read
    ├── summary_<paper1>.md
    ├── summary_<paper2>.md
    └── ...

Default output directory: same directory as the input PDF, in a review_output/ subdirectory.

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Quick Reference: The Pipeline in Practice

When the user gives you a PDF to review:

1. Ask (if not provided): output directory preference, any specific concerns they want addressed
2. Read the PDF — build initial understanding (Phase 1)
3. Search in parallel — launch Semantic Scholar + WebSearch + vec-db agents simultaneously (Phase 2)
4. Select papers — pick 8-12 most relevant from search results (Phase 2.2)
5. Download & parallel read — spawn one agent per paper, all in one message (Phase 3)
6. Synthesize — build field landscape from all paper summaries (Phase 3.3)
7. Expert re-read — critically re-read the target paper with domain knowledge (Phase 4)
8. Generate review — produce bilingual output following the exact RA-L form (Phase 5)
9. Present to user — show the review and ask if they want adjustments

The whole process should feel like handing the paper to a senior researcher who happens to have perfect recall of the recent literature and returns with a thoughtful, sharp review.