Large language model agents can automate data science workflows, but cloud-centric deployment exposes sensitive context and edge-only deployment limits analytical capability. We present FinDS-Agent, a cloud–edge framework that keeps raw records and program execution at the trusted edge while providing a policy-screened...
Xiao-Zheng Du, Rui-Jun Deng, Cheng Wang et al.· Future Internet· 0 citations
Deploying Text-to-SQL in production is hampered by context-window limits on large schemas, metadata that goes stale as schemas evolve, and infrastructure sprawl from external vector stores. We demonstrate
ATLAS
, which addresses all three by co-locating schema metadata, semantic annotations, and vector embeddings e...
Qing Zhang, Shijing Hu, Zhihui Lu· Proceedings of the VLDB Endo...· 0 citations
HuyuanOCR-1.5 ranks among the top-tier end-to-end OCR solutions on OmniDocBench v1.6 while achieving new performance milestones across these long-tail tasks, and proposes Agentic Data Flow, an agent-driven data construction system that transforms model weaknesses into executable data requirements and autonomously perfo...
