ai-paper-survey

# AI Paper Survey Skill Structured, multi-phase paper survey workflow for AI research. ## When to Use - "Survey recent papers in [topic]" - "What's new in agent/LLM/multimodal research?" - "Find the most important papers from the last N months" - "Do a literature review on [topic]" - "Track progress in [research area]" ## Prerequisites - **alphaXiv MCP server** must be connected (provides `embedding_similarity_search`, `full_text_papers_search`, `get_paper_content`) - **paper-impact-analyzer** skill installed (for impact assessment) - **Research keywords file** (optional): a Markdown file listing the user's research interests and keywords ## Workflow: 5-Phase Pipeline ### Phase 0: Load Research Context 1. Check if a research keywords file exists. Look for files matching patterns: - `研究关键词*.md` - `research-keywords*.md` - `research-interests*.md` in the current working directory. 2. If found, read it and extract: - **Theme list**: the major research themes (e.g., "RL optimization", "Agent & Tool Calling") - **Keywords**: specific terms to search for (e.g., "GRPO", "Nested Learning", "VLA") - **Models of interest**: specific model names (e.g., "DeepSeek V4", "Qwen3.5") 3. If no keywords file, ask the user for: - Research topics (1-5 topics) - Time range (default: last 3 months) - Any specific papers or authors to track 4. Determine the **time range** (default: last 3 months from today). 5. Generate **search queries** using the template below. For each user theme T, generate: ``` Semantic query: "Fundamental advances in {T}, paradigm shift, redefine {T}, {year}" Keyword query: "{specific_keywords_from_T} {year_range}" Contrast query: "Alternative to {current_paradigm_of_T}, beyond {T}, {year}" ``` ### Phase 1: Broad Search (Parallel) Execute search queries in parallel using alphaXiv MCP tools: - Use `embedding_similarity_search` for semantic queries (captures conceptual matches) - Use `full_text_papers_search` for keyword queries (captures exact term matches) **Rules:** - Launch 4-6 parallel searches covering different themes - Each search returns up to 15 results - Collect all results into a candidate pool - Deduplicate by arXiv ID - Filter by publication date (must be within the specified time range) **Expected output:** 30-60 unique candidate papers with titles and abstracts. ### Phase 2: Initial Screening (LLM Judgment) For each candidate paper, classify by the user's framework. Default framework (3-tier): - **Tier 1 (Essence)**: "What IS X?" — Redefines the problem itself. Asks fundamental questions about the nature of learning, reasoning, action, perception, etc. These papers have lasting impact because they challenge assumptions. - **Tier 2 (Engineering)**: "How to do X better?" — Optimizes within existing frameworks. Valuable but doesn't change paradigms. Examples: better MoE routing, improved training recipes, new benchmarks. - **Tier 3 (Patch)**: "How to mitigate this symptom?" — Short-term fixes. Inference token pruning, fine-tuning tricks, quantization improvements. **Rules:** - Use ONLY title + abstract for screening (don't read full papers yet) - Be selective: aim for 8-12 papers across all tiers - Tier 1 should have 3-5 papers max - Apply the user's specific keywords to boost relevance **Expected output:** Classified paper list with tier assignments. ### Phase 3: Deep Reading (Parallel, Top Candidates Only) For Tier 1 and top Tier 2 papers (4-6 papers max), use `get_paper_content` to retrieve full analysis. **After reading each paper, immediately extract and cache:** - Core contribution (1 sentence) - Method keywords (3-5 terms) - Best experimental result (1-2 numbers) - Open-source links (GitHub URL if any) - Venue acceptance status - Key limitation **Discard the raw full-text analysis after extraction** to manage context window. ### Phase 4: Impact Assessment For each paper in the deep reading set, run the paper-impact-analyzer: ```bash python path/to/paper-impact-analyzer/scripts/analyze.py {arxiv_id_1} {arxiv_id_2} ... ``` Merge impact data with the content analysis from Phase 3. ### Phase 5: Synthesize Report Generate a structured Markdown report with the following sections: ```markdown # {Topic} Paper Survey — {Date Range} > Survey date: {today} > Scope: {themes covered} > Papers screened: {N candidates} → {M selected} ## Classification Framework {Describe the tier system used} ## Tier 1 (Essence): Redefining the Problem ### Paper 1: {Title} - **Essential question**: What fundamental assumption does this challenge? - **Core contribution**: {1 sentence} - **Key result**: {best number} - **Impact**: {rating from analyzer} | {venue} | {github stars} - **Links**: arXiv | GitHub {... repeat for each Tier 1 paper} ## Tier 2 (Engineering): Doing It Better | Paper | Contribution | Impact | Links | |-------|-------------|--------|-------| {table rows} ## Tier 3 (Patches): Symptom Relief | Paper | What it fixes | Links | |-------|--------------|-------| {table rows} ## Top 3 Recommended Papers {Ranked list with justification combining content depth + impact signals} ## Trends & Observations {2-3 paragraphs on emerging patterns} ``` Save the report to `{working_directory}/{topic}-paper-survey-{date}.md`. ## Configuration ### Custom Classification Framework Users can override the default 3-tier framework by specifying their own in the keywords file. The skill will use whatever framework the user provides. ### Search Depth Control | Level | Searches | Deep reads | Best for | |-------|----------|------------|----------| | Quick | 4 | 2-3 | Weekly check-in | | Standard | 6 | 4-6 | Monthly review | | Thorough | 8-10 | 6-8 | Quarterly survey | Default: Standard. ## Example Usage ``` Survey the last 3 months of papers in my research areas ``` ``` Quick survey: what's new in LLM reasoning and agent tool-calling since January? ``` ``` Thorough literature review on RL training methods for LLMs, classify by innovation tier ```