12 papers
CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
Samuel Schapiro, Core Francisco Park, Felix Sosa +1
Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been ter…
Viral Proteins Reveal Geometry of Protein Language Models
Arthur Bigot, Harmon Bhasin, Core Francisco Park +2
Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case…
A Systematic Study of Behavioral Cloning for Scientific Data Annotation
Ishaan Singh Chandok, Core Francisco Park
Scientific data annotation, such as tracking animals in video or proofreading neural reconstructions, remains bottlenecked by the "last mile" problem: even with strong automation,…
Mechanisms of Misgeneralization in Physical Sequence Modeling
Kento Nishi, Raphael Tang, Karun Kumar +2
Generative sequence models are often trained to plan motion in physical domains, from robotics to mechanical simulations. When constructing a dataset to train such a model, enginee…
Convergent World Representations and Divergent Tasks
Core Francisco Park
While neural representations are central to modern deep learning, the conditions governing their geometry and their roles in downstream adaptability remain poorly understood. We de…
: System-2 Fine-tuning for Robust Integration of New Knowledge
Core Francisco Park, Zechen Zhang, Hidenori Tanaka
Humans and intelligent animals can internalize new information and accurately internalize their implications to perform downstream tasks. While large language models (LLMs) can ach…