The best engineering knowledge is locked in research papers. Paper Lantern unlocks it for your code.

Paper Lantern is an MCP server that distills 2M+ CS research papers into the right method for your problem — its tradeoffs, benchmarks, and how to implement it — delivered directly to your coding agent. Works with Claude Code, Cursor, Copilot, any MCP client.

Your coding agent can search for papers, but it's searching the open web — not a purpose-built research index. And even when it finds papers, the hard questions remain: which methods actually matter for your problem? What are the tradeoffs at your scale? What was tried and failed? What should you actually implement? That reasoning lives in papers and it never reaches your code.

EXAMPLE ask your agent to implement chunking for a RAG pipeline. Paper Lantern detects your context — multi-source corpus, accuracy-critical, technical documents — then searches across 2M+ papers and finds 4 from January 2026 that directly apply. It explains each technique in plain language, shows why it matters for your specific setup, synthesizes how they address different pipeline stages, and recommends what to start with and why — with implementation details your coding agent can act on immediately.

One of those papers: a cross-document topic-aligned chunking approach hitting 0.93 faithfulness vs 0.78 for semantic chunking (arxiv:2601.05265). Another: a pruning method that cuts input tokens 76% while improving answer quality (arxiv:2601.17532).

The index covers agent design, RAG and retrieval, LLM inference, fine-tuning, evaluation, search and ranking — hundreds of techniques across applied CS.

BACKGROUND I spent 7 years leading various LLM and RAG teams at AWS Bedrock (IIT-Bombay, Stanford). Paper Lantern started as a research discovery platform - this is the same engine with additional reasoning, now plugged into coding workflows via MCP.

Looking for engineers who use coding agents daily. Happy to answer questions about the search, the synthesis, or the MCP integration.

code.paperlantern.ai