6 papers
Lacuna: A Research Map for Machine Learning
Martin Weiss, Miles Q. Li, Alejandro H. Artiles +4
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 prop…
Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth
Gaurav Sahu, Laurent Charlin, Christopher Pal
We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human reference list as an evaluation target. First,…
Mem-: Adaptive Memory through Learning When and What to Generate
Xiaoqiang Wang, Chao Wang, Hadi Nekoei +5
We present Mem-, a framework for adaptive memory in large language model (LLM) agents, where useful guidance is generated on demand rather than retrieved from external memory st…
The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions
Alejandro H. Artiles, Martin Weiss, Levin Brinkmann +6
Scientific discovery is constrained not only by what is true, but by what is cognitively available to researchers. Many directions are coherent in light of the literature yet unlik…
ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review
Gaurav Sahu, Hugo Larochelle, Laurent Charlin +1
Peer review is the cornerstone of scientific publishing, yet it suffers from inconsistencies, reviewer subjectivity, and scalability challenges. We introduce ReviewerToo, a modular…
AInstein: Can LLMs Solve Research Problems From Parametric Memory Alone?
Shambhavi Mishra, Gaurav Sahu, Marco Pedersoli +3
Can large language models solve AI research problems using only their parametric knowledge, without fine-tuning, retrieval, or other external aids? We introduce AInstein, a framewo…