paper-lantern-challenges

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vunderba·Apr 17, 2026

Nice job. I put together a similar system a while back - it's just a self-contained Go binary called paper-search along with an accompanying LLM skill to facilitate search, retrieval, and downloading of relevant academic papers using OpenAlex, Semantic Scholar, and arXiv. In my experience it's been a better solution versus just asking the LLM to directly to search the web for this kind of information via search engine tooling. Also just FYI the link provided in your Show HN (https://github.com/paper-lantern-ai/paper-lantern-challenges) is a 404. I think it should be: https://github.com/paperlantern-ai/paper-lantern-challenges

In Show HN: Paper Lantern – on-demand techniques from 2M+ papers for coding agents

paperlantern·Apr 17, 2026Submission

Show HN: Paper Lantern – on-demand techniques from 2M+ papers for coding agents

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 caug

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ht 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/paperlantern-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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