automated manuscript drafting

Existing approaches extract program components in isolation, failing to reconstruct complete scientific workflows—thereby impeding research reproducibility and the advancement of “AI for Science.” This paper introduces the first end-to-end, paper-level workflow generation framework that integrates paragraph-level text mining with generative modeling to automatically construct structured, source-locatable, and visualizable research flowcharts from full-text papers. Methodologically, it employs SciBERT coupled with PU learning to identify descriptive paragraphs; leverages Flan-T5 with prompt engineering to generate workflow phrases; and applies few-shot learning via ChatGPT for stage classification and precise mapping to original text locations. Evaluated on NLP-domain papers, the framework achieves a paragraph identification F1-score of 0.977, ROUGE-1 of 0.454 for workflow phrase generation, and 95.8% accuracy in stage classification. It further enables the first systematic, longitudinal analysis of methodological evolution in NLP over the past two decades—revealing marked growth in data analysis and ablation studies.

Current research software publishing lacks automated tools to ensure FAIR (Findable, Accessible, Interoperable, Reusable) compliance. To address this, we propose HERMES—a CI-based, extensible software publishing workflow that automatically generates software artifacts enriched with persistent identifiers (PIDs) and structured metadata. Our contributions are threefold: (1) the first configurable, reusable publishing pipeline designed for the full lifecycle of research software; (2) a modular plugin architecture enabling flexible integration of heterogeneous metadata sources; and (3) a Python-driven CLI toolchain with native GitHub Actions support. Evaluated across three cross-domain empirical case studies, HERMES significantly reduces manual publishing effort, improves metadata completeness by up to 40%, and increases FAIR compliance rates by over 65%. The workflow provides foundational infrastructure for sustainable, standards-compliant research software publication.

Researchers face challenges in academic writing—including verbose LaTeX coding, poor version control, low collaborative efficiency, and irreproducible results. To address these, this paper proposes a GitHub-based automated manuscript generation framework. It adopts Markdown as the source format and integrates automated LaTeX compilation, programmatic figure generation, and continuous integration (CI) pipelines to enable end-to-end, traceable, and reproducible scientific writing—from raw data to publication-ready PDF. Crucially, the framework redefines manuscripts as “executable outputs,” unifying version control, computational provenance tracking, and dynamic content updating. Evaluated in computational biology and microscopic image analysis workflows, the framework significantly improves collaborative productivity and adherence to open science principles, while supporting high-quality, fully reproducible scholarly publishing.

This study addresses the challenge of inadequate adherence to standardized reporting guidelines in scientific research, often caused by the time-consuming and error-prone nature of manual checklist completion, which undermines reproducibility. To overcome this, the authors propose an automated approach leveraging a locally deployed large language model (LLM), enhanced through instruction fine-tuning and a multi-stage prompting strategy, operating entirely on CPU-based inference. This method ensures data privacy and auditability while enabling cross-disciplinary checklist recommendation and item completion. For the first time, a local LLM is employed as a structured reasoning component for automated research reporting. Evaluated on a corpus of peer-reviewed manuscripts, the system achieves 90% accuracy in checklist recommendation and 88% accuracy in item completion, processing each manuscript in just 12.5 seconds without requiring GPU resources. The entire workflow is open-source and fully reproducible.

To address the challenges of parallel scheduling, opaque execution states, poor result reproducibility, and inadequate auditability when managing hundreds to thousands of Snakemake/Nextflow pipelines in large-scale bioinformatics analyses, this paper proposes a lightweight command-line orchestration framework. Built in Python and integrated with SQLite or PostgreSQL, it enables unified pipeline launching across heterogeneous workflows, real-time status monitoring, fine-grained log collection, automated result ingestion into databases, and comprehensive lifecycle metric logging—including runtime, resource consumption, and failure points. It introduces a novel CLI paradigm that supports cross-pipeline collaborative monitoring and reproducibility assurance without modifying existing workflow code. Experimental evaluation demonstrates a 42% improvement in multi-project throughput, significantly enhancing observability, auditability, and reproducibility in large-scale bioinformatics analysis.

This work addresses the challenge of transforming research ideas into complete academic papers—a process requiring coordinated literature retrieval, experimental design, evidence alignment, and long-form coherence, which conventional generation methods struggle to support. The authors propose a lightweight, composable workflow architecture embedded within a coding assistant, integrating thirteen modular skills to enable end-to-end paper generation. Their approach decouples model-based judgments from deterministic operations and introduces a novel separation between experimental planning and reporting. By incorporating evidence-driven claim revision, self-critique mechanisms, and procedural vector graphic generation, the system effectively mitigates failure modes such as self-contradictory loops. Evaluated across eight controlled tasks, the framework achieves 99.5% citation validity, 96.4% editable figure fidelity, reduces hallucination rates from 14% to 8% (corresponding to a 92% detection rate), attains 74% accuracy under adversarial review, and produces each paper in an average of 3.2 hours at a cost of $8.10.

Scientific literature retrieval faces significant challenges due to users’ ambiguous, dynamically evolving, and preference-dependent intents. Existing approaches often lack controllability and optimizability owing to their reliance on fixed pipelines or implicit reasoning. To address this, this work proposes PaperPilot, the first framework to introduce an explicit, editable retrieval workflow mechanism. It models retrieval as a directed acyclic graph (DAG) constructed from anchor papers and user queries, supporting operations such as keyword search, citation expansion, filtering, scoring, re-ranking, and evidence extraction. The system iteratively refines both queries and workflow structure through user feedback. Built upon Qwen3.5-9B and integrating supervised imitation learning, preference optimization, and tool calling, PaperPilot achieves substantial improvements in Hit@5 (+19.0→77.0), MRR (+11.9→59.4), and nDCG@10 (+5.7→32.5), while reducing workflow execution errors to 0%.

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 limitations of traditional high-performance computing (HPC), which relies on manual task scripting and scheduling and struggles to meet the automation demands of complex scientific workflows. The authors propose the first large language model–based autonomous agent framework that enables end-to-end automated execution of HPC workflows from descriptive instructions. The framework integrates Slurm/Flux job schedulers, low-latency AWS cloud infrastructure, and event monitoring mechanisms to support task definition, optimization, and scheduling. Experimental results demonstrate that the system efficiently deploys scalable experiments, accurately translates job specifications—with only occasional deviations in processor affinity—and successfully reproduces an expert-level variant calling pipeline, achieving consistent results in 18 out of 19 runs. These findings validate the framework’s feasibility and effectiveness in real-world HPC environments.

This work proposes the first end-to-end AI-driven scientific research system capable of autonomous, cross-domain operation at scale. Addressing the limitations of existing automated research frameworks—which often rely on manually specified topics or are confined to narrow tasks—the system employs a language model–powered multi-agent architecture coupled with a shared workspace to orchestrate the full research lifecycle, from topic selection and experimental design to execution and paper writing. Deployed across 67 fine-grained AI/ML topics, it generated 166 complete manuscripts and constructed an auditable, full-cycle research corpus. Expert evaluation of 282 reviews confirms that a subset of outputs demonstrates scholarly merit, while also exposing critical challenges in current automated science, including narrow experimental scope, methodological constraints, and concerns regarding academic integrity.