literature-search

name	Literature Search
description	Medium-depth literature search — read AI-summarized reports for every paper analyzed
type	sop
layer	sop
agents	["alphaxiv","semantic-scholar"]
tools	{"alphaxiv":["discover_papers","get_paper_content"],"semantic-scholar":["relevanceSearch","paper","paperBatch","citations","references"]}
input	query (string), scope (survey | gap-analysis | background)
output	PaperAnalysis[] with metadata + AI summary content

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

Tool	Purpose	Returns
alphaxiv.discover_papers	Primary search — arXiv semantic search	Ranked paper list with metadata
ss.relevanceSearch	Supplementary search — non-arXiv papers	Title, abstract, authors, citationCount
ss.paper / ss.paperBatch	Metadata enrichment	Citation count, DOI, S2 ID, externalIds
ss.citations	Papers that cite this paper (incoming)	Citing paper list with context
ss.references	Papers this paper cites (outgoing)	Referenced paper list
alphaxiv.get_paper_content	AI summary report (fullText: false)	Structured AI-generated paper report

HARD-GATE

**Do NOT base analysis on abstracts or discover_papers snippets alone.**

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