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arXiv · 2609.07956

Streaming Hierarchical Inference with Tabular Foundation Models

Vitor Crista·Afonso Lourenço·Diogo Martinho·Goreti Marreiros
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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.

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Vitor Crista, Afonso Lourenço, Diogo Martinho, Goreti Marreiros. 2026-09-07. Streaming Hierarchical Inference with Tabular Foundation Models. https://arxiv.org/abs/2609.07956

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