Domain-specific AI systems are moving into clinical, engineering, and scientific workflows with specialized architectures and evaluation pipelines. Examples include CDISC dataset generation, SPE well planning, bioinformatics software identification, and deep-learning exchange-correlation functionals.
A hybrid framework combines lexical signals and SciBERT semantics to identify software and database names in biomedical literature. Trained with a pipeline mixing citation extraction and LLM-assisted distillation, it outperforms specialized methods a...
ATHENA, the Society of Petroleum Engineers' virtual assistant, improved productivity and performance uniformity for 75 professionals on well-planning tasks compared with a state-of-the-art RAG baseline. The enhanced version adds multi-document retrie...
Microsoft Research has released Skala 1.1, a deep-learning exchange-correlation functional. Trained on 2.5 times more data, it lowers the weighted average error to 2.8 kcal/mol on GMTKN55 while retaining meta-GGA cost. Native integration in CP2K, wit...
A new framework aligns brain and text embeddings in a shared semantic space for retrieval-based decoding, separating neural signal from LLM reconstruction. MD-SigLIP uses duplicate-aware contrastive learning and a listwise margin term to enforce rank...
A multi-agent system turns regulatory process order into a directed acyclic graph and achieves 100% structural match in CDISC clinical dataset generation, while flat and single-agent approaches remain at zero. The CDISCPilot01 comparison shows that p...
