academic-research

Academic Research

This skill provides comprehensive guidance for academic paper search, literature reviews, and research synthesis using Exa MCP and arxiv-mcp-server.

When to Use This Skill

• Searching for academic papers on a topic
• Conducting literature reviews
• Finding papers by specific authors
• Discovering recent research in a field
• Downloading and analyzing arXiv papers
• Synthesizing findings across multiple papers
• Tracking citation networks and influential papers
• Researching state-of-the-art methods in AI/ML

Available Tools

Exa MCP Server (Web Search with Academic Filtering)

Tools: mcp__exa__web_search_exa, mcp__exa__get_code_context_exa, mcp__exa__deep_search_exa

Key Parameters for Academic Search:

• category: "research_paper" - Filter results to academic papers
• includeDomains: ["arxiv.org"] - Restrict to arXiv
• startPublishedDate / endPublishedDate - Filter by publication date

ArXiv MCP Server (Paper Search, Download, Analysis)

Tools: search_papers, download_paper, list_papers, read_paper

Capabilities:

• Search arXiv by keyword, author, or category
• Download papers locally (~/.arxiv-papers)
• Read paper content directly
• Deep paper analysis with built-in prompts

Core Workflows

Workflow 1: Quick Paper Discovery

Use case: Find papers on a specific topic quickly

Step 1: Use Exa with research_paper category
mcp__exa__web_search_exa({
  query: "transformer attention mechanisms survey",
  category: "research_paper",
  numResults: 10
})

Step 2: Review titles and abstracts
Step 3: Note arXiv IDs for deeper analysis

Workflow 2: ArXiv-Focused Search

Use case: Search specifically within arXiv

Step 1: Use arxiv MCP search_papers
search_papers({
  query: "large language models reasoning",
  max_results: 20,
  sort_by: "relevance"
})

Step 2: Download papers
download_paper({ arxiv_id: "2301.00234" })

Step 3: Read and analyze
read_paper({ arxiv_id: "2301.00234" })

Workflow 3: Comprehensive Literature Review

Step 1: Broad discovery with Exa (category: "research_paper")
Step 2: Identify key papers and authors
Step 3: Deep dive with arXiv MCP (download + read_paper)
Step 4: Synthesize findings by methodology/approach

Workflow 4: Recent Developments Tracking

Step 1: Time-filtered Exa search
mcp__exa__web_search_exa({
  query: "multimodal large language models",
  category: "research_paper",
  startPublishedDate: "2024-01-01"
})

Step 2: Sort arXiv by submitted_date
search_papers({ query: "multimodal LLM", sort_by: "submitted_date" })

ArXiv Categories Reference

| Category | Description | |----------|-------------| | cs.AI | Artificial Intelligence | | cs.CL | Computation and Language (NLP) | | cs.CV | Computer Vision | | cs.LG | Machine Learning | | cs.NE | Neural and Evolutionary Computing | | stat.ML | Statistics - Machine Learning | | cs.RO | Robotics |

Academic Domain Filtering

For Exa searches, restrict to academic sources:

includeDomains: [
  "arxiv.org",
  "aclanthology.org",
  "openreview.net",
  "proceedings.mlr.press",
  "papers.nips.cc",
  "openaccess.thecvf.com"
]

Tool Selection Guide

| Task | Primary Tool | Alternative | |------|--------------|-------------| | Broad topic search | Exa (research_paper) | arXiv search_papers | | ArXiv-specific | arXiv search_papers | Exa with includeDomains | | Download paper | arXiv download_paper | - | | Full paper content | arXiv read_paper | - | | Code implementations | Exa get_code_context | - | | Very recent papers | arXiv (submitted_date) | Exa with date filter | | Bot-protected sites | Obscura --stealth | Scrapling (Turnstile) | | Batch stealth scrape | Obscura scrape | - |

Source Extraction Escalation

When a source isn't on ArXiv or Exa can't reach it, escalate through:

1. ArXiv MCP → paper is on arXiv (free, full text, best quality)
2. Exa contents → URL known, site allows crawling
3. Firecrawl → JS-heavy site, no anti-bot
4. Obscura --stealth → site fingerprints headless browsers (JSTOR, Scholar, Persée, PubMed, Academia.edu)
5. Scrapling → site uses Cloudflare Turnstile

Obscura Stealth Extraction (Tier 3)

For gated academic sources that block standard headless browsers via canvas/WebGL fingerprinting. Not for bypassing paywalls — for extracting publicly visible metadata, abstracts, and open-access content.

Verified sites (2026-04-24): Google Scholar, JSTOR, Persée, PubMed, Academia.edu, Perseus Digital Library.

# Quick metadata extraction (auto-detects site type from URL)
bash ~/.claude/skills/academic-research/scripts/academic_stealth_fetch.sh URL

# With explicit site type
bash ~/.claude/skills/academic-research/scripts/academic_stealth_fetch.sh URL scholar

# Direct Obscura usage
obscura fetch --stealth --quiet URL --eval "JS_EXPRESSION"

See references/obscura-academic-patterns.md for site-specific JS extraction patterns and gotchas.

Best Practices

1. Start broad with Exa's research_paper category, then narrow
2. Use date filtering for recent developments
3. Download key papers via arXiv MCP for persistent access
4. Cross-reference multiple search approaches
5. Use technical terms in queries for better results

Domain: Subquadratic Attention

Research domain for post-transformer attention mechanisms that break the O(n^2) barrier. Active area with rapid publication cadence (2024–2026).

Key Papers

| Paper | Year | Key Contribution | |-------|------|------------------| | FlashAttention-2 (Dao) | 2023 | IO-aware exact attention — foundation for all subsequent work | | DuoAttention | 2024 | Split attention heads into retrieval (sparse) vs streaming (full) | | Ring Attention | 2024 | Distributed sequence parallelism across devices | | MoBA (Mixture of Block Attention) | 2025 | Block-sparse top-k gating with Triton kernel, 1M tokens | | NSA (Native Sparse Attention, DeepSeek) | 2025 | Hardware-aligned sparse attention patterns | | TokenSelect | 2025 | Dynamic per-layer token pruning |

Pre-Built Search Queries

# Exa (research_paper category)
"subquadratic attention mechanism" --category "research paper" --after 2024-01-01
"block sparse attention triton kernel" --category "research paper"
"mixture of attention heads sparse" --category "research paper"
"linear attention transformer approximation" --category "research paper" --after 2024-06-01

# ArXiv (cs.LG + cs.CL)
search_papers({ query: "subquadratic attention sparse transformer", max_results: 20, sort_by: "submitted_date" })
search_papers({ query: "block sparse FlashAttention kernel", max_results: 10 })

Evaluation Criteria

When comparing subquadratic attention mechanisms, benchmark on:

| Criterion | What to Measure | |-----------|-----------------| | Quality | Perplexity degradation vs full attention at target sequence length | | Speed | Wall-clock speedup on consumer GPUs (RTX 4090, M4 Max) | | Memory | Reduction factor at 128K / 512K / 1M context | | Compatibility | Drop-in replacement vs requires retraining | | Sparsity | How much computation is actually skipped (e.g., 95% at 1M tokens) |

Local Implementation Reference

Working MoBA implementation with Triton kernels: ~/Desktop/Aldea/01-Repos/perplexity-clone/model/moba_block_sparse.py

Reference Documentation

For detailed parameters and advanced usage:

• references/exa-academic-search.md - Exa parameters for academic search
• references/arxiv-mcp-tools.md - ArXiv MCP server tool reference
• references/obscura-academic-patterns.md - Site-specific Obscura extraction patterns with JS expressions and gotchas