Paper Lantern is an MCP server that lets coding agents ask for personalized techniques / ideas from 2M+ CS research papers. Your coding agent tells PL what problem it is working on --> PL finds the most relevant ideas from 100+ research papers for you --> gives it to your coding agent including trade-offs and implementation instructions.

We had previously shown that this helps research work and want to know understand whether it helps everyday software engineering tasks. We built out 9 tasks to measure this and compared using only a Coding Agent (Opus 4.6) (baseline) vs Coding Agent + Paper Lantern access.

(Blog post with full breakdown: https://www.paperlantern.ai/blog/coding-agent-benchmarks)

Some interesting results : 1. we asked the agent to write tests that maximize mutation score (fraction of injected bugs caught). The baseline caught 63% of injected bugs. Baseline + Paper Lantern found mutation-aware prompting from recent research (MuTAP, Aug 2023; MUTGEN, Jun 2025), which suggested enumerating every possible mutation via AST analysis and then writing tests to target each one. This caught 87%.

2. extracting legal clauses from 50 contracts. The baseline sent the full document to the LLM and correctly extracted 44% of clauses. Baseline + Paper Lantern found two papers from March 2026 (BEAVER for section-level relevance scoring, PAVE for post-extraction validation). Accuracy jumped to 76%.

Five of nine tasks improved by 30-80%. The difference was technique selection. 10 of 15 most-cited papers across all experiments were published in 2025 or later.

Everything is open source : https://github.com/paper-lantern-ai/paper-lantern-challenges

Each experiment has its own README with detailed results and an approach.md showing exactly what Paper Lantern surfaced and how the agent used it.

Quick setup: `npx paperlantern@latest`

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