arXiv:2512.11067v1 Announce Type: cross
Abstract: Traditional DBMSs execute user- or application-provided SQL queries over relational data with strong semantic guarantees and advanced query optimization, but writing complex SQL is hard and focuses only on structured tables. Contemporary multimodal systems (which operate over relations but also text, images, and even videos) either expose low-level controls that force users to use (and possibly create) machine learning UDFs manually within SQL or offload execution entirely to black-box LLMs, sacrificing usability or explainability. We propose KathDB, a new system that combines relational semantics with the reasoning power of foundation models over multimodal data. Furthermore, KathDB includes human-AI interaction channels during query parsing, execution, and result explanation, such that users can iteratively obtain explainable answers across data modalities.
Accelerometer-Derived Rest-Activity Rhythm Amplitude, Genetic Predisposition, and the Risk of Ischemic Heart Disease: Observational and Mendelian Randomization Study
Background: The rest-activity rhythm amplitude (RARA), as a fundamental human behavior, has been linked to various health conditions. However, its causal relationship with ischemic heart