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, sanitized context to support cloud planning. FinDS-Agent integrates a Three-Stage Cascaded Privacy Gate (TCPG), a Multi-Dimensional Joint Router (MJR), contract-guided ToolGraph planning, edge-side verification, and bounded repair. On 222 DataSciBench tasks over three runs, FinDS-Agent achieved a 69.93% completion rate and 57.06% success rate, improving over Edge-Only by 9.50 and 5.71 percentage points while invoking the cloud for 32.27% of eligible task-runs. On FinDS-Privacy-Bench, TCPG increased sensitive-field recall from 58.20% to 98.10%; no payload-leakage event was observed in the full set (0/200; Wilson 95% CI: 0–1.8845%) or blind split (0/100; 0–3.6993%) under the specified audit and threat model. External evaluation gave pass rates of 33.2%, 53.1%, and 62.3% for Edge-Only, FinDS-Agent, and Cloud-Only on DS-1000. These empirical results support selective cloud planning while delimiting statistical, privacy, and transfer claims.
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 entirely within a single RDBMS with native vector-index support. ATLAS contributes: (1)
Unified in-database storage
with native vector indexes, requiring no external engine; (2)
Two-stage adaptive schema linking
that uses vector retrieval to narrow hundreds of tables to a compact candidate set before LLM refinement; (3)
Rich Context life-cycle
that automatically generates semantic descriptions, synonyms, sample values, business rules, and value mappings, bridging raw schema and LLM understanding; (4)
Agent-driven self-maintenance
via a coordinator-executor loop that detects DDL changes and regenerates annotations without manual intervention. We showcase three live scenarios on an enterprise database with 517 tables.
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 performs material search, quality verification, and pipeline development.
