arXiv:2510.12071v2 Announce Type: replace Abstract: Current training data attribution (TDA) methods treat the influence one sample has on another as static, but neural networks learn in distinct stages that exhibit changing patterns of influence. In this work, we introduce a framework for stagewise data attribution grounded in singular learning theory. We predict that influence can change non-monotonically, including sign flips and sharp peaks at developmental transitions. We first validate these predictions analytically and empirically in a toy model, showing that dynamic shifts in influence
The increasing complexity and opacity of neural networks necessitate more sophisticated methods for understanding their learning processes and attributing data influence, pushing research in this direction.
Understanding how training data influences AI models at different learning stages is critical for improving model interpretability, robustness, and ethical compliance, especially for high-stakes applications.
The conventional view of static data influence is updated with a dynamic, stagewise perspective, leading to more nuanced methods for evaluating and managing AI training processes.
- · AI researchers
- · ML engineers
- · AI ethics committees
- · Explainable AI (XAI) platforms
- · Black box AI solutions
- · Static data attribution methods
New methods for training data attribution will emerge based on stagewise influence dynamics.
Improved understanding of model learning will lead to more efficient and reliable AI model development and deployment.
Enhanced model interpretability could foster greater public trust in AI systems and enable broader adoption in sensitive domains.
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Read at arXiv cs.LG