arXiv:2512.11561v2 Announce Type: replace Abstract: Generalizing pretrained models to unseen datasets without retraining is a central challenge toward foundation models. Achieving fully inductive inference on numerical data is particularly difficult due to large variations in feature dimensionality and semantics across datasets. We observe that, in the presence of graph structure, numerical data admits a distinct structure-induced representational axis beyond the feature space, which we formalize as the view space. This view space enables a unified representation of graphs with heterogeneous f
This research addresses the ongoing challenge of generalizing AI models to new datasets, which is critical for developing more robust and autonomous AI systems, a core focus of current AI research.
A unified representation across heterogeneous graphs and arbitrary datasets could unlock more powerful, adaptable AI, significantly accelerating progress towards foundation models and advanced AI agents.
The proposed 'view space' offers a novel way to represent numerical data for inductive inference, potentially broadening the applicability of pretrained models to vastly different datasets without extensive retraining.
- · AI model developers
- · Data science platforms
- · Industries with diverse datasets (e.g., healthcare, finance)
- · Companies reliant on highly specialized, non-generalizable AI solutions
AI models become more adaptable and require less retraining when encountering new data structures.
This could accelerate the deployment and effectiveness of AI agents in complex, real-world environments.
Increased generalization capabilities might reduce the computational resources needed for continuous model adaptation, shifting resource allocation within the compute supply chain.
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Read at arXiv cs.LG