4 papers
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…
The Mechanistic Invariance Test: Genomic Language Models Fail to Learn Positional Regulatory Logic
Bryan Cheng, Jasper Zhang
Genomic language models (gLMs) have transformed computational biology, achieving state-of-the-art performance across genomic tasks. Yet a fundamental question threatens the foundat…