arXiv:2608.28590v1 Announce Type: new
Abstract: Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the...
By Fan Liu, Hao Liu
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.
By Yutian Tang, Yuming Zhou, Huaming Chen
arXiv:2606. 31423v1 Announce Type: cross Abstract: Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer.
By Yizhe Liu, Shaolei Zhang, Ju Fan
arXiv:2606. 07491v1 Announce Type: cross Abstract: High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance.
By Jamie J. Alnasir
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.
By Shuo Hao, You Lu, Bihuan Chen, Xin Peng
The paper investigates the use of a large language model (LLM) agent to automate the creation and execution of a federated learning workflow for medical image analysis. By generating a reviewable specification of constraints and acceptance criteria, the LLM produces an executable workflow, while a validation loop repairs failures and ensures conformance to the specification. Experiments on the FABRIC testbed demonstrate that the approach can detect silent errors—such as a run that trained 1,700 jobs on random tensors—that traditional failure-driven debugging would miss.
By Komal Thareja, Hamza Safri, Rajiv Mayani, Anirban Mandal, Ewa Deelman