LLM workflows often require human approval before an irreversible external action. Most systems keep that approval outside the workflow, as an interface click or an audit entry. The workflow therefore lacks a commit-time check that every risky path reached an approval gate. Its logs may not preserve the reviewed eviden...
Laurent Bindschaedler, Ferdinand Kossmann, Chun-Wei Liu et al.· 0 citations
Semantic database systems extend SQL with foundation-model inference over unstructured data, but current engines rely heavily on autoregressive LLMs for discrete relational decisions, creating high latency and monetary cost. We present JEVDB, a scalable semantic database system that uses fast, typed decision models for...
RADAR is an auditable runtime that makes an agent's analytical choices inspectable and supports their revision through execution feedback and rejects the operation or provides diagnostic feedback, allowing the agent to revise its choices before errors propagate.
Han-Xu Yan, Lang-Xuan Deng, Zheng-Le Wang et al.· 0 citations
iPDB is demonstrated, a system that supports in-database LLM inference using an extended declarative SQL syntax and new optimizations that result in efficient query processing of LLM-enabled SQL queries that outperform state-of-the-art systems.
Udesh Kumarasinghe, Tyler Liu, Ahmed R. Mahmood et al.· Proceedings of the VLDB Endo...· 0 citations
TransForm is presented, a control/data-plane split in which MCP returns a small envelope plus a machine-readable descriptor, and large payloads are delivered over stream-able HTTP as JSON, Parquet, or Arrow IPC (blob or chunk stream).
TabClean is presented, a cost-efficient and training-free tabular data cleaning system in which LLM agents synthesize reusable code for error detection and correction, making LLM usage a largely one-time rather than recurring cost.
Yibo Wang, Riteng Zhang, Bharat Bhargava et al.· 0 citations
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