JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures 事件

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures arXiv:2602.17162v2 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local syntax, these generative paradigms prioritize token-level reconstruction over high-level functional context. We introduce JEPA-DNA, a model-agnostic continual training frame