Portrait de Yacine Mkhinini

Yacine Mkhinini

yacine.mkhinini@mila.quebec
Maîtrise professionnelle - UdeM
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Traitement du langage naturel
GitHub

Publications

Lacuna: A Research Map for Machine Learning
Martin Weiss
Miles Q. Li
Alejandro H. Artiles
Yacine Mkhinini
Chris Pal
Hugo Larochelle
Nasim Rahaman
Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements… (voir plus), 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.
2026-06-23
arXiv (prépublication)
doi.org
arxiv.org
Lacuna: A Research Map for Machine Learning Problem Formulation
Martin Weiss
Alejandro Hernandez
Yacine Mkhinini
Miles Q. Li
Christopher Pal
Hugo Larochelle
Nasim Rahaman
Research problem formulation is the cognitive task of turning a vague scientific idea into a testable hypothesis. \textbf{Lacuna} is a resea… (voir plus)rch map that supports this process for machine learning, using LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research directions, author and venue pages, 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, plus scripts for reproducing the agent run. On a theorem-proving seed idea, the agent reaches a research proposal in 85.5 seconds and 7 tool calls, vs.\ 289.2 seconds and 27 tool calls for a sequential-PDF baseline. On ScholarQA-CS-ML, an ML/AI subset of the OpenScholar benchmark, Lacuna-GPT-4o scores 0.694 under the ScholarQABench rubric judge, compared with 0.672 for the OpenScholar-GPT-4o baseline on the same questions.
2026-05-31
AI4Research @ International Conference on Machine Learning (poster)
openreview.net
openreview.net