# Lacuna: A Research Map for Machine Learning

arXiv: https://arxiv.org/abs/2606.26246 · HTML: https://mlsanity.com/paper/2606.26246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics](https://mlsanity.com/paper/2302.12433/) (2023)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://mlsanity.com/paper/2402.14207/) (2024)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://mlsanity.com/paper/2010.14235/) (2020)
- [FIMO: A Challenge Formal Dataset for Automated Theorem Proving](https://mlsanity.com/paper/2309.04295/) (2023)
- [LitSearch: A Retrieval Benchmark for Scientific Literature Search](https://mlsanity.com/paper/2407.18940/) (2024)
- [ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks](https://mlsanity.com/paper/2508.15804/) (2025)
- [VERINA: Benchmarking Verifiable Code Generation](https://mlsanity.com/paper/2505.23135/) (2025)
- [Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://mlsanity.com/paper/2502.17086/) (2025)
- [ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review](https://mlsanity.com/paper/2510.08867/) (2025)
- [StepFun-Prover Preview: Let's Think and Verify Step by Step](https://mlsanity.com/paper/2507.20199/) (2025)
- [S2ORC: The Semantic Scholar Open Research Corpus](https://mlsanity.com/paper/1911.02782/) (2019)
- [The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions](https://mlsanity.com/paper/2603.01092/) (2026)
- [Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge](https://mlsanity.com/paper/1901.10816/) (2019)

## Built on by (most cited first, 2 of 2)

- [ScholarStack: Layered Research Asset Orchestration and Cross-Task Reuse for Scientific Agents](https://mlsanity.com/paper/2609.23735/) (2026)
- [Complex Agents, Shallow Tests: Demystifying and Enhancing Test Adequacy of Agent Harness in the Wild](https://mlsanity.com/paper/2610.04921/) (2026)

## Abstract (arXiv)

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.