arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
arXiv:2512.08029v4 Announce Type: replace
Abstract: Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that ca...
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
arXiv:2606. 16721v1 Announce Type: new Abstract: Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes.
By Ke Liu, Mengxuan Li, Yanyi Bao, Tianyun Zhang, Chong Chu, Jiajun Bu, Haishuai Wang
arXiv:2607. 16262v1 Announce Type: cross Abstract: The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology.
By Christopher Baker, Tianyu Ren, Karen Rafferty, Hui Wang, Simon McDade
JEPA-Anything is a domain‑agnostic framework that uses orthogonal predictive factorization (OPF) to decompose latent targets into complementary factors, learn them via dedicated pathways, and recombine them for shared prediction. The method is evaluated across seven diverse domains—vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather—showing improvements on 10 dynamics tasks, reduced error on Interventional Pong, and lowest one‑step and 100‑step molecular errors among compared methods. Experimental validation includes a factor‑nominated biological intervention that succeeded in cell co‑cultures, organoids, tumor fragments, and mice, and latent orbital modes that recover the Keplerian scaling exponent.
By Taoyong Cui, Zhongyao Wang, Xinyue Xu, Weiyang Liu, Zhaochen Yu, Yuying Zhang, Qiang Gao, Mengyue Yang, Wanli Ouyang, Pheng Ann Heng, Yingcheng Wu, Zhenfei Yin, Ling Yang
arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.
By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang