arXiv AI
From Question-First to Analyst-First: Domain-Expert Skills and Verified Knowledge Compilation for Proactive Enterprise Analytics
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 15
TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
arXiv:2606. 13731v1 Announce Type: new Abstract: Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis.
arXiv AI
Jul 14
QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics
arXiv:2607. 11019v1 Announce Type: new Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents.
arXiv AI
Aug 7
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
arXiv AI
Aug 28
GROUND: Reducing Hallucinations in LLM-Based Enterprise Analytics Through Governed Semantic Definitions
The paper introduces GROUND, a framework that limits large language model (LLM) analytics to a governed semantic layer for enterprise data warehouses. GROUND supplies approved metrics, dimensions, join paths, filters, and security rules, then validates generated SQL against these constraints before execution, retrying or abstaining on violations. In benchmarks, GROUND eliminates hallucinations across all evaluated categories and prevents row‑level security breaches, outperforming schema‑only, schema‑RAG, and semantic‑only approaches.
arXiv Computation and Language
Oct 27
Semantic Atoms for Scalable Text Analysis in Data Analytics in the Era of LLM-Based Agents
arXiv:2610.34860v1 Announce Type: new Abstract: Much of the information needed for data analysis is buried in text columns: an analyst may infer customer frustration from complaint text and use it to...
arXiv AI
4d ago
AvalancheBench: Evaluating Enterprise Agent-Driven Semantic Query Processing Through Latent World Recovery
AvalancheBench is a multimodal enterprise analytics benchmark that tests data agents’ ability to recover a latent world from observed data, using 58 questions across text, images, and documents. It evaluates agents on analytical fidelity, monetary cost, and a new capability‑adjusted token cost (CATC) metric, revealing that misinterpreting requirements and misuse of semantic operators are the biggest sources of error. The benchmark highlights how errors can propagate through dependent questions, affecting overall performance.