Lacuna: A Research Map for Machine Learning
Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research directions, and research proposals. Each item keeps links to the primary source records and papers that support it. We release the map with web, markdown, and MCP interfaces. Across LitSearch, Multi-XScience-CS/ML, and ScholarQA-CS-ML, Lacuna outperforms OpenScholar with the strongest gains on LitSearch retrieval (Recall@10 0.538 vs. 0.424 for OpenScholar v3). We also evaluate Lacuna Deep Research, a multi-stage report agent over the map, on 25 ReportBench-ML survey tasks: Lacuna Deep Research reaches 0.052 citation F1, 0.339 citation preci...
| Authors | Martin Weiss, Miles Q. Li, Alejandro H. Artiles et al. |
|---|---|
| Primary category | cs.DL |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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