77 citations · 133 across the 6 of their papers we have counts for
6 papers
Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
Alex Ning, Yen-Ling Kuo, Gabe Gomes
Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thought reasoning. By di…
Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
Robert MacKnight, Jose Emilio Regio, Jeffrey G. Ethier +2
Modern optimization in experimental chemistry employs algorithmic search through black-box parameter spaces. Here we demonstrate that pre-trained knowledge in large language models…
Spectral Manifold Harmonization for Graph Imbalanced Regression
Brenda Nogueira, Gabe Gomes, Meng Jiang +2
Graph-structured data is ubiquitous in scientific domains, where models often face imbalanced learning settings. In imbalanced regression, domain preferences focus on specific targ…
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
Daniil A. Boiko, Thiago Reschützegger, Benjamin Sanchez-Lengeling +2
Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learn…
Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models
Jaspreet Pannu, Doni Bloomfield, Alex Zhu +4
As a result of rapidly accelerating AI capabilities, over the past year, national governments and multinational bodies have announced efforts to address safety, security and ethics…
Emergent autonomous scientific research capabilities of large language models
Daniil A. Boiko, Robert MacKnight, Gabe Gomes
Transformer-based large language models are rapidly advancing in the field of machine learning research, with applications spanning natural language, biology, chemistry, and comput…