Hugging Face Trending Papers
AgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics
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arXiv AI
Oct 19
AgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics
AgentOnto is an ontology-guided large language model agent designed for enterprise data analytics. It structures heterogeneous enterprise knowledge—such as databases, business documents, and example tasks—into an ontology that directs analysis and incorporates confirmed feedback. The system improves its ontology through iterative preparation and demonstrates superior performance over existing methods on public datasets, with a real-world deployment confirming its practical value.
arXiv AI
Jul 17
AgenticData: An Agentic Data Analytics System for Heterogeneous Data
arXiv:2508. 05002v2 Announce Type: replace-cross Abstract: Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming.
arXiv AI
Jun 16
Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs
arXiv:2605. 21027v2 Announce Type: replace-cross Abstract: Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools or Text-to-SQL systems.
arXiv AI
Sep 15
EvoOntology: A Self-Evolving Ontology Layer for Data Agents
EvoOntology introduces a self‑evolving ontology layer for data agents, encapsulating the ontology as an MCP server with schema, content, and tool layers. It enables agents to query and interact with the ontology at runtime, using a builder agent for autonomous construction and a self‑evolution loop that refines the ontology through attribution‑guided edits validated by backbone‑conditional evaluation. Experiments on three data‑agent benchmarks with four LLM backbones show that EvoOntology consistently outperforms strong baselines and existing semantic‑layer approaches, effectively bridging the agent‑data gap for heterogeneous data.
Hugging Face Trending Papers
Jun 17
Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents
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.
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.