automation engineering

This work addresses the lack of closed-loop control in traditional software development lifecycles, which often fails to simultaneously ensure security, auditability, and highly reliable automation. The authors propose a deterministic autonomous control framework that models the lifecycle as a seven-stage automated pipeline, integrating Jira-based task orchestration, structured context, resource constraints, and human-review gating mechanisms to establish a secure closed loop. Key innovations include a state-contract-based collision locking mechanism, a degradation protocol for fallback operation, and a traceable control architecture. Implemented with 12,661 lines of Python code and 6,907 lines of versioned prompt specifications—including 101 exception handlers and 12 centralized locks—the system achieved a 100% success rate (95% CI [97.6%, 100%]) across 152 initial runs, producing over 795 artifacts. All 51 issues identified through adversarial review were fully resolved, with 60% of security tickets autonomously completed.

In model-based systems engineering, low experimental data reuse efficiency and excessive redundant experiments hinder digital engineering agility. To address this, this paper proposes a case-based reasoning (CBR)-driven experimental management framework that explicitly integrates domain knowledge. The framework features structured experimental metadata modeling, digital twin–enabled scenario semantic alignment, and an interpretable similarity assessment mechanism to intelligently determine whether historical experiments can be transferred to address new verification queries. Its key innovation lies in embedding domain knowledge explicitly into both the CBR retrieval and adaptation stages, thereby enabling trustworthy cross-operating-condition and cross-configuration experimental data reuse. Evaluated on an industrial-scale vehicle energy system design case, the framework reduces redundant experiments by 37% and shortens early verification cycles by 42% on average, significantly enhancing iterative efficiency in digital engineering and advancing intelligent experimental management.

This work proposes an AI agent–driven workflow to address the high costs of reproducing large-scale empirical studies, which often stem from discrepancies in computational environments, code, and documentation. The approach decouples scientific reasoning from computational execution: researchers supply standardized diagnostic templates, and the system automatically retrieves and orchestrates reproduction materials within a version-controlled environment. A structured knowledge layer captures failure patterns, enabling adaptive reproduction across heterogeneous studies while ensuring transparency and stability of the analytical pipeline. Evaluated on 92 instrumental variable studies, the method achieves an 87% end-to-end reproduction success rate; when data and code are available, it attains 100% success at both the paper and model levels.

This work addresses the limitations of general-purpose large language model (LLM)-driven agents in scientific workflow automation, which often fail to meet critical requirements such as reproducibility, auditability, and deterministic execution, thereby compromising experimental reliability. To overcome these challenges, we propose R-LAM, a novel framework that systematically integrates reproducibility constraints into large action models. R-LAM employs structured action representations, a deterministic execution engine, and explicit provenance tracking to ensure that every operation and intermediate artifact is auditable, replayable, and amenable to fault-awareness and controlled workflow branching. Implemented as a lightweight Python library and released as an open-source PyPI package, R-LAM demonstrates significant improvements in reproducibility success rates and execution reliability across representative scientific workflows while retaining adaptive control over complex processes.

This work addresses the critical barrier to reproducibility and integration with AI-for-Science (AI4S) agent workflows posed by the complex compilation and configuration of scientific software. We propose an end-to-end agent workflow that leverages a domain-specific taxonomy to filter repositories, automatically infers build specifications, containerizes the resulting artifacts, and validates their executability. For the first time, this pipeline enables the automated deployment of over 50,000 scientific tools within a single day. We successfully constructed reproducible execution environments for 50,112 tools, each verified by a minimal executable command, and released them in SciencePedia—the first trustworthy capability repository grounded in actual execution rather than documentation. Additionally, we publicly share the large-scale deployment traces to illuminate operational bottlenecks in scientific software packaging.

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

Scientific data often require extensive manual curation before being usable for scientific AI, lacking a unified framework for automated conversion, readiness assessment, provenance tracking, and agent integration. This work proposes REDI, an open-source framework that automatically transforms raw scientific data into AI-ready formats through a five-stage, fully traceable pipeline—ingestion, preprocessing, transformation, structuring, and output—while exposing the resulting workflows as callable skills for AI agents. REDI is the first framework to unify these capabilities; its companion tool, SetGo, ensures FAIR compliance and enables automatic catalog publishing. Leveraging parallel distributed processing and I/O performance profiling, REDI demonstrates effectiveness across climate science, proteomics, materials science, and nuclear fusion, with the climate use case achieving near-ideal strong scaling up to 100 nodes on the Frontier supercomputer.

Community-driven scientific workflow ecosystems often struggle to sustain themselves due to ambiguous maintenance and user support mechanisms, particularly in cross-platform collaboration and heterogeneous execution environments. This study presents the first cross-platform empirical analysis of the nf-core ecosystem, systematically examining 15,760 GitHub issues, 35,411 pull requests, and 895 forum discussions. By integrating metadata and textual features into predictive models, the research uncovers significant disparities in maintenance and support activities across platforms and highlights weak explicit linkages among them. The findings reveal that issues, pull requests, and forum posts predominantly serve distinct roles—coordinating maintenance, facilitating code integration, and providing user support, respectively. Moreover, issue actionability, diagnostic evidence, and depth of interaction emerge as critical determinants of resolution efficiency.

This work addresses the heavy reliance on expert knowledge in designing and debugging scientific workflows, a challenge exacerbated by existing large language model approaches that directly generate code without ensuring transparency, reproducibility, or seamless system integration. To overcome these limitations, we propose an AI-assisted scientific workflow management framework that decouples user intent from implementation through a structured specification phase, enabling specification-driven workflow generation and validation. We further introduce a multi-layer debugging agent powered by large language models to automate error diagnosis and correction. By deeply integrating with the Pegasus workflow system via the Model Context Protocol (MCP), our approach supports end-to-end workflow lifecycle management. Empirical evaluation demonstrates successful generation and execution of federated learning medical imaging workflows comprising thousands of tasks, substantially reducing debugging effort and empowering non-expert users to construct complex workflows adhering to expert-level design patterns.

This study evaluates the reliability and adaptability of large language models in executing scientific tasks within real-world physical environments, with a focus on their ability to generate executable experimental protocols and iteratively refine them based on empirical evidence. Leveraging a robotic chemistry laboratory comprising 45 modular workstations and conducting 4,608 trials, this work extends scientific agent evaluation beyond pure reasoning to encompass physical executability and evidence-driven closed-loop adaptation, introducing a quantifiable framework for assessing deployment readiness. Results reveal that only 3.3% of generated protocols were deemed executable by expert reviewers, with the best-performing system achieving a success rate of 28.1%. Most generated workflows contained no more than 30 steps and generally lacked capabilities for workflow-level replanning or methodological reconfiguration in response to experimental outcomes.