Streaming Hierarchical Inference with Tabular Foundation Models

Abstract:Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-throughput data streams remains challenging due to communication overhead and latency. We propose \textit{HINT}, a hierarchical inference framework that combines edge-based retrieval with cloud-based TFM inference. A graph-based approximate nearest neighbor memory maintained over a sliding window provides local predictions and uncertainty estimates, allowing confident samples to be processed locally while uncertain instances are selectively offloaded, together with their retrieved context, to a cloud-hosted TFM. The framework exposes an offloading threshold and a neighborhood retrieval policy that can be varied to balance predictive performance and communication cost. Experiments show \textit{HINT} consistently identifies favorable trade-offs.

Machine Learning (cs.LG)

arXiv:2609.07956 [cs.LG]

(or arXiv:2609.07956v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.07956 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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