7 papers
When Three-Dimensional Conformer Ensembles Improve Molecular Property Prediction Beyond Two-Dimensional Fingerprints: A Systematic Study
Bryan Cheng, Austin Jin, Jasper Zhang
When do three-dimensional conformer ensembles improve molecular property prediction beyond two-dimensional fingerprints? We provide the first systematic, mechanistically grounded a…
Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning
Jasper Zhang, Bryan Cheng
Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -- yet the field lacks a principl…
Single-Position Intervention Fails: Distributed Output Templates Drive In-Context Learning
Bryan Cheng, Jasper Zhang
Understanding how large language models encode task identity from few-shot demonstrations is a central open problem in mechanistic interpretability. Prior work uses linear probing…
State Space Models are Effective Sign Language Learners: Exploiting Phonological Compositionality for Vocabulary-Scale Recognition
Bryan Cheng, Austin Jin, Jasper Zhang
Sign language recognition suffers from catastrophic scaling failure: models achieving high accuracy on small vocabularies collapse at realistic sizes. Existing architectures treat…
ECLIPSE: A Composable Pipeline for Predicting ecDNA Formation, Evolution, and Therapeutic Vulnerabilities in Cancer
Bryan Cheng, Jasper Zhang
Extrachromosomal DNA (ecDNA) represents one of the most pressing challenges in cancer biology: circular DNA structures that amplify oncogenes, evade targeted therapies, and drive t…
When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction
Bryan Cheng, Jasper Zhang
We present the first systematic study of when target context helps molecular property prediction, evaluating context conditioning across 10 diverse protein families, 4 fusion archi…