Literature Search

Literature Search SOP

Layer Rules

• Layer: sop — wraps MCP tools directly
• Called by: Any tactic or strategy requiring literature survey with paper content reading
• Calls: alphaxiv MCP tools, semantic-scholar MCP tools (never calls other SOPs)

Purpose

Medium-depth reading. Understand methods, contributions, and findings via AI-generated summary reports. Suitable for literature surveys, gap analysis, and building background knowledge.

Use this when you need to:

• Conduct a literature survey on a topic
• Understand what methods exist and how they compare
• Identify gaps in current research
• Build background knowledge for a research project

This skill reads AI-summarized reports — not raw full text. For rigorous analysis requiring raw text, use literature-research.

Tools

HARD-GATE

For EVERY paper selected for analysis, you MUST call:

alphaxiv.get_paper_content(url: arxiv_url, fullText: false)

This returns an AI-generated summary report optimized for LLM consumption.

PROHIBITED:

• Completing a research task without reading paper content
• Using only abstracts from ss.relevanceSearch as your evidence
• Using only discover_papers snippets as your evidence
• Claiming to understand a paper's methodology from its abstract alone

REQUIRED:

• Call get_paper_content for every paper you analyze
• Base your analysis on the AI summary report content
• Minimum 5 papers read via get_paper_content for any survey task

Workflow

Step 1: Search

Primary (arXiv):

alphaxiv.discover_papers(
  keywords: ["keyword1", "keyword2", "keyword3"],
  question: "Detailed description of papers needed",
  difficulty: 5
)

Supplementary (non-arXiv):

ss.relevanceSearch(
  query: "search terms",
  limit: 20,
  year: "2022-2024"
)

Step 2: Enrich Metadata

For papers found via alphaxiv, enrich with citation data:

ss.paperBatch(
  paper_ids: ["ARXIV:2301.xxxxx", "ARXIV:2302.xxxxx", ...]
)

Returns: citationCount, DOI, S2 ID for each paper.

Step 3: Select Papers

Choose top N papers (typically 5-15) based on:

• Relevance to research question
• Citation count (impact indicator)
• Recency (for fast-moving fields)
• Diversity of approaches (avoid reading only one school of thought)

Step 4: Read AI Summary Reports

For each selected paper:

alphaxiv.get_paper_content(
  url: "https://arxiv.org/abs/XXXX.XXXXX",
  fullText: false
)

fullText: false (default) returns an AI-generated intermediate report:

• Structured summary of contributions
• Key methods and techniques
• Main results and findings
• Optimized for LLM consumption (faster than raw text)

Step 5: Citation Graph Expansion (Optional)

To find related work not caught by keyword search:

ss.citations(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
ss.references(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)

Filter results by year and citation count, then repeat Steps 3-4 for promising papers.

Tool-Specific Notes

alphaxiv.get_paper_content

• fullText: false (default) — AI-generated report, faster, structured
• fullText: true — raw extracted text, slower, complete (use in literature-research, not here)
• Accepts: arXiv URL (https://arxiv.org/abs/XXXX.XXXXX), PDF URL, alphaXiv URL
• Only works for arXiv papers — non-arXiv papers cannot be read via this tool

ss.paperBatch

• Max 500 papers per call
• Auto-prefixes bare arXiv IDs (e.g., 2301.12345 → ARXIV:2301.12345)
• Returns null for papers not found

ss.citations / ss.references

• Max 1000 results per call
• Use offset and limit for pagination
• Includes citation context, intent, and influence flags

Examples

Literature survey: "attention mechanisms in vision transformers"

# Step 1: Search
alphaxiv.discover_papers(
  keywords: ["vision transformer", "attention", "ViT"],
  question: "Papers proposing or analyzing attention mechanisms in vision transformers",
  difficulty: 5
)
ss.relevanceSearch(query: "vision transformer attention mechanism", limit: 15, year: "2022-2024")

# Step 2: Enrich
ss.paperBatch(paper_ids: ["ARXIV:2010.11929", "ARXIV:2103.14030", ...])

# Step 3: Select top 8 by citation count + relevance

# Step 4: Read each
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2010.11929")  # ViT
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2103.14030")  # Swin
# ... repeat for all 8

# Step 5: Expand via citations of ViT
ss.citations(paper_id: "ARXIV:2010.11929", limit: 30)

Gap analysis: "efficient inference for large language models"

# Step 1: Broad search
alphaxiv.discover_papers(
  keywords: ["LLM", "efficient inference", "quantization", "pruning"],
  question: "Methods for making large language model inference faster or cheaper",
  difficulty: 6
)

# Step 2-4: Enrich, select 10, read AI summaries

# Step 5: Check what recent papers cite the seminal works
ss.citations(paper_id: "ARXIV:2210.17323", limit: 50)  # GPTQ
ss.citations(paper_id: "ARXIV:2306.00978", limit: 50)  # AWQ