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:2607. 11019v1 Announce Type: new Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents.
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.
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: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...
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.
RubricAtlas is a framework that extracts type-level analytical requirements from expert reports to evaluate deep research agents. It organizes detailed binary checkpoints around recurring analytical questions, linking them to report passages and separating completeness from argument validity. Experiments on 3,079 Chinese financial reports show that individual reports cover only about half of their type’s questions, and that using different reports for requirements changes evaluation scores and agent rankings, with high agreement between judge pipelines.
Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data. We present Data Intelligence Agents (DIA), a system of three agents (Data Interpreter, Schema Creator, and Query Generator) that compresses this workflow by treating autonomous coding agents (ACAs) as a first-class abstraction: rather than emitting text, the agents generate, execute, validate, and repair concrete artifacts, draw on a shared memory for experience reuse, and surface each for review by domain experts.
RubricAtlas is a framework that extracts type-level analytical rubrics from expert reports to evaluate deep research agents. It organizes binary checkpoints around recurring analytical questions, linking them to report passages and assessing completeness separately from argument validity. Experiments on 3,079 Chinese financial reports show that individual reports cover only about half of their type’s questions, and that using different reports for requirements changes evaluation scores and agent rankings.
arXiv:2606. 19319v1 Announce Type: cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data.
The paper presents a system that transforms open‑problem listings in research fields into a structured, queryable knowledge graph. It combines a hand‑curated problem layer with an evidence layer derived from bibliographic data, linked through typed, provenance‑carrying connections. The authors evaluate the approach on database and AI problem catalogs, showing that the new graph can answer all previously answerable queries and additional ones about recent work, but also reveal limitations in automated status judgments, leading to a design recommendation that status changes must be proposed with evidence and reviewed by humans.
arXiv:2608. 16045v1 Announce Type: cross Abstract: LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations.