arXiv:2606.06533v1 Announce Type: new Abstract: What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post-hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understandin
The increasing complexity and opacity of AI models necessitate a more fundamental understanding of their emergence, moving beyond simple input-output analysis.
A deeper scientific understanding of AI training dynamics is crucial for developing more robust, interpretable, and controllable AI systems, impacting their reliability and ethical deployment.
AI research will likely shift focus towards the dynamic, time-evolving processes of model training, leading to new methodologies for AI development and debugging.
- · AI researchers (fundamental science)
- · AI developers (robust systems)
- · Academic institutions
- · MLOps firms
- · Companies relying on black-box AI approaches
- · Researchers focused solely on post-hoc analysis
Increased investment in tools and methodologies for analyzing AI training dynamics.
Development of new AI architectures and training paradigms that are intrinsically more interpretable and controllable.
Accelerated progress towards generalizable and trustworthy AI by understanding its foundational principles.
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Read at arXiv cs.AI