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DeepEye: A Workflow-Centric Agentic Data System for Steerable Data Analytics

This work presents DeepEye, a workflow-centric agentic data system that turns user intents into transparent and steerable analytical workflows and develops DataMagic as the system’s Video Generator, a declarative multi-agent method that improves data-video quality.

Unknown authors · 0 citations
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MANA (Modular Agentic NoSQL Architecture): Robust NL-to-NoSQL Querying at Enterprise Scale

Databases are a crucial part of modern information systems, but interacting with them has traditionally required users to have some level of expertise in formal query languages. This barrier prevents non-technical users from getting the most out of the data that is stored. In this work, we propose a robust production o...

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QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics

Experiments on public benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents.

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Semantic Governance Paradigm of Heterogeneous World Models for Agentic AI Systems

Agentic AI systems require world models that support reliable reasoning, planning, and decision-making under complex and heterogeneous conditions. Although heterogeneous graphs are natural candidates for this role, their structural design, particularly the selection of relational paths, is typically ad hoc and weakly g...

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Towards a Context Layer for Self-Improving Data Agents

It is argued that the key challenges are bridging the gap between personal and organizational memory, building effective agentic retrieval beyond naive RAG, and enabling organizations to evaluate their agents against their own data through self-serving evals.

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MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems

MUSE is presented, an interactive meta-agent that enhances user understanding and control of agentic data science systems by dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details.

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