Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries
Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opaque black box, hiding its intermediate reasoning and data retrieval steps, and failing to expose controls for managing API costs and execution latency. Meanwhile, the former can be prohibitively expensive for enterprise-scale data lakes. Consequently, analysts using these systems lack the agency to intercept hallucinated premises, verify intermediate results, or correct the system's trajectory. We present Carnot, an interactive execution engine for AI-driven analytics. Carnot compiles natural language requests i...
| Authors | Matthew Russo, Yash Agarwal, Tianyu Li et al. |
|---|---|
| Primary category | cs.DB |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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