Deep Research & Literature Synthesis

Conduct thorough analysis of research papers, synthesize findings across multiple sources, and generate comprehensive literature reviews.

Tools to Use

For deep research, use these analysis tools:

Tool	Purpose
`read_full_article`	Read complete article content for deep learning
`answer_research_question`	Multi-source synthesis with citations
`explore_citation_network`	Citation graph analysis
`compare_articles`	Side-by-side comparison
`find_related_papers`	Semantic similarity
`evaluate_article`	Quality scoring
`get_citation_context`	Citation relationship context
`search_articles`	Find papers in knowledge base
`get_article_details`	Get article metadata and preview

Note: These are analysis-heavy tools. For simpler queries, use the knowledge-base-qa skill instead.

Iterative Learning Pattern

Key Principle: You can read, learn from an article, and keep reading to build deep understanding.

The Learning Loop

code

1. DISCOVER: Search for relevant papers
   search_articles(query="topic")

2. READ DEEPLY: Load full article content
   read_full_article(article_identifier="paper title or DOI")
   → Read the entire article, not just previews

3. ANALYZE: Extract key insights
   - What are the main contributions?
   - What methods were used?
   - What are the limitations?

4. IDENTIFY GAPS: What questions remain?
   - What concepts need clarification?
   - What related work should you read?

5. REPEAT: Read more articles to fill gaps
   find_related_papers(article_id="...")
   read_full_article(article_identifier="next paper")

6. SYNTHESIZE: Combine insights across papers
   answer_research_question(question="synthesis query")

When to Read Full Articles

Use read_full_article when:

•You need to understand methodology details
•The preview (from get_article_details) isn't enough
•You're comparing specific techniques across papers
•You're writing a literature review
•You want to learn a topic deeply

Use get_article_details when:

•You just need metadata (authors, date, journal)
•You're doing initial screening of papers
•A quick preview is sufficient

External Content

For web articles, blog posts, and documentation outside your knowledge base:

•Use Letta's built-in fetch_webpage tool to read external URLs
•This complements read_full_article which is for your indexed papers

When to Use This Skill

Use deep research when user asks for:

•"Analyze this paper in depth"
•"Compare these papers"
•"Write a literature review on X"
•"What are the research gaps in X?"
•"Synthesize findings across papers on X"
•"How has research on X evolved?"

Deep Analysis Workflow

Single Paper Deep Dive

code

1. Read the full article content
   read_full_article(article_identifier="[paper title or DOI]")
   → Returns complete markdown content for deep reading

2. Explore citation context
   explore_citation_network(
     article_id="[paper ID]",
     direction="both",  # cited_by and references
     depth=1
   )

3. Assess quality
   evaluate_article(
     article_id="[paper ID]",
     criteria=["novelty", "methodology", "impact"]
   )

4. If you have questions, read related papers
   find_related_papers(article_id="[paper ID]")
   read_full_article(article_identifier="[related paper]")
   → Keep reading until you understand the topic

Multi-Paper Comparison

code

1. Identify papers to compare
   search_articles(query="[topic]", limit=10)

2. Run comparison
   compare_articles(
     article_ids=["paper1", "paper2", "paper3"],
     comparison_aspects=["methodology", "results", "datasets", "limitations"]
   )

3. Find additional related work
   find_related_papers(article_id="[seed paper]", limit=10)

Literature Synthesis

code

1. Answer research question with synthesis
   answer_research_question(
     question="[topic question]",
     max_sources=20,
     min_relevance=0.7
   )
   → Returns comprehensive answer with citations from knowledge base

2. Explore citation relationships
   explore_citation_network(
     article_id="[key paper ID]",
     direction="both",
     depth=2
   )

Analysis Aspects

When analyzing papers, consider these dimensions:

Methodology Assessment

•Research design (experimental, theoretical, empirical)
•Data sources and quality
•Evaluation metrics
•Reproducibility indicators

Contribution Analysis

•Novel techniques/methods introduced
•Improvements over prior work
•Practical applications
•Theoretical insights

Limitations & Gaps

•Acknowledged limitations
•Implicit assumptions
•Missing comparisons
•Future work directions

Impact Assessment

•Citation count/trajectory
•Adoption in subsequent work
•Industry applications
•Community recognition

Delegation Pattern

The Orchestrator should delegate to Research Analyst when:

•User explicitly requests deep analysis
•Question requires reading full papers
•Comparison across 3+ papers needed
•Literature review requested

Delegation message format:

code

send_message_to_agent(
  agent_name="Research Analyst",
  message="Deep analysis request: [specific task]
  
  Papers to analyze: [list of IDs or titles]
  
  Focus areas: [methodology/results/limitations/etc]
  
  Output needed: [comparison table/summary/review/etc]"
)

Workflow Examples

Example 1: Single Paper Deep Analysis

User: "Analyze the 'Attention Is All You Need' paper in depth"

code

1. read_full_article(article_identifier="Attention Is All You Need")
   → Read the complete paper content

2. [Read and understand the paper thoroughly]
   - Note key contributions, methodology, results

3. explore_citation_network(
     article_id="[paper ID]",
     direction="cited_by",
     depth=1
   )

4. If concepts are unclear, read related papers:
   read_full_article(article_identifier="[related paper on attention]")
   → Keep learning until you understand

5. evaluate_article(
     article_id="[paper ID]",
     criteria=["novelty", "methodology", "impact"]
   )

6. Response:
   "## Deep Analysis: Attention Is All You Need
   
   **Core Contribution**: [transformer architecture description]
   
   **Methodology**:
   - Self-attention mechanism
   - Positional encoding approach
   - Training procedure
   
   **Key Results**:
   - BLEU score improvements
   - Training efficiency gains
   
   **Impact Analysis**:
   - [X] citations
   - Spawned: BERT, GPT, T5, etc.
   
   **Research lineage**:
   - Builds on: [references from citation network]
   - Influenced: [top citing papers]"

Example 2: Comparative Analysis

User: "Compare GPT-4, Claude, and Llama 3 architectures"

code

1. search_articles(query="GPT-4 architecture", limit=3)
   search_articles(query="Claude architecture Anthropic", limit=3)
   search_articles(query="Llama 3 architecture", limit=3)

2. compare_articles(
     article_ids=[paper IDs for each],
     comparison_aspects=[
       "model_size",
       "training_data",
       "architecture_innovations",
       "benchmark_performance",
       "safety_measures"
     ]
   )

3. Response:
   "## Comparative Analysis: GPT-4 vs Claude vs Llama 3
   
   | Aspect | GPT-4 | Claude | Llama 3 |
   |--------|-------|--------|---------|
   | Size | ... | ... | ... |
   | Training | ... | ... | ... |
   | Key innovation | ... | ... | ... |
   
   **Key Differences**:
   1. [difference 1]
   2. [difference 2]
   
   **Shared Approaches**:
   - [commonality]
   
   **Notable gaps in literature**:
   - [what's not well documented]"

Example 3: Literature Review

User: "Give me a literature review on efficient transformers"

code

1. answer_research_question(
     question="What are the main approaches to making transformers more efficient?",
     max_sources=25
   )
   → Returns comprehensive synthesis with citations

2. search_articles(query="efficient transformers", limit=20)
   → Find key papers in the knowledge base

3. For key papers, explore citations:
   explore_citation_network(article_id="[key paper]", direction="both")

4. Response:
   "## Literature Review: Efficient Transformers
   
   ### 1. Introduction
   [Context and importance from answer_research_question]
   
   ### 2. Taxonomy of Approaches
   
   **2.1 Attention Approximation**
   - Linear attention [cite]
   - Sparse attention [cite]
   - Low-rank approximation [cite]
   
   **2.2 Architecture Modifications**
   - State space models [cite]
   - Mixture of experts [cite]
   
   **2.3 Implementation Optimizations**
   - FlashAttention [cite]
   - Memory-efficient attention [cite]
   
   ### 3. Citation Analysis
   [Key papers and their relationships from explore_citation_network]
   
   ### 4. Research Gaps
   [Identified from synthesis]
   
   ### References
   [Citations from answer_research_question]"

Quality Standards

For deep research output:

•Accuracy: Every claim backed by citation
•Comprehensiveness: Cover major approaches/papers
•Structure: Clear organization with sections
•Balance: Present multiple viewpoints fairly
•Currency: Include recent work (last 2 years)
•Gaps: Identify what's missing in the literature