Lacuna: A Research Map for Machine Learning

• URL: http://arxiv.org/abs/2606.26246v1
• Date: Wed, 24 Jun 2026 18:00:12 GMT
• Title: Lacuna: A Research Map for Machine Learning
• Abstract summary: Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries.<n>Across LitSearch, Multi-XScience-CS/ML, and ScholarQA-CS-ML, Lacuna outperforms OpenScholar with the strongest gains on LitSearch retrieval.<n>We also evaluate Lacuna Deep Research, a multi-stage report agent over the map, on 25 ReportBench-ML survey tasks.
• Score: 49.4551062599854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 precision, 99 expert-reference hits, and 7.82/10 RACE report quality, while GPT-Researcher reaches 0.039 F1, 0.290 precision, 72 hits, and 5.24/10 RACE.

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