arXiv:2607.02771v1 Announce Type: new Abstract: Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing framework fully unifies automated transformation, readiness assessment, provenance tracking, and agent-native deployment. We present REDI, an open-source framework that addresses this gap through a unified five-stage pipeline (ingest, preprocess, transform, structure, and output) with per-stage instrumentation for reproducibility and deployment as an agent-callable skill
The proliferation of scientific AI applications and the increasing scale of datasets at leadership computing facilities are creating an urgent need for automated data readiness solutions.
Automating data readiness removes a significant bottleneck in scientific AI development, accelerating research and deployment of complex AI models.
The development of unified frameworks for data transformation and assessment will streamline the process of preparing scientific data for AI, making AI more accessible and reproducible in research environments.
- · Scientific AI researchers
- · Leadership computing facilities
- · Open-source data tooling developers
- · Sovereign AI initiatives
- · Manual data preparation services
- · Organizations without robust data governance
Increased efficiency and speed in scientific AI development.
Democratization of advanced AI applications across various scientific disciplines due to reduced data preparation overhead.
Acceleration of national AI capabilities as data infrastructure becomes more robust and interoperable.
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Read at arXiv cs.AI