arXiv:2608. 16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language.
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language. Although these s...
AdaLens is an interactive system designed to monitor and steer long-running, autonomous data analysis workflows powered by large language models. It provides a storyline-based interface that unifies analytical plans, execution progress, intermediate findings, and data-column involvement, enabling analysts to observe evolving reasoning and evidence. The system also offers steering interactions that allow users to redirect low-value directions or deepen promising ones during execution.
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concepts often lies beyond what is explicitly represented in database schemas and data values.
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
arXiv:2607. 00828v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows.
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
arXiv:2606. 18425v1 Announce Type: cross Abstract: Scientific workflow management systems (WMS) support scalable and reproducible execution of complex pipelines, but workflow design, implementation, and debugging remain largely manual and require significant expertise.
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv:2512. 22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance.
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:2609.24137v1 Announce Type: cross Abstract: Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous...
arXiv:2605. 12376v2 Announce Type: replace Abstract: Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines.