Paper Detail
Vitor Crista, Afonso Lourenço, Diogo Martinho, Goreti Marreiros
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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@article{crista2026streaming,
title = {Streaming Hierarchical Inference with Tabular Foundation Models},
author = {Vitor Crista and Afonso Lourenço and Diogo Martinho and Goreti Marreiros},
year = {2026},
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 \textbackslash{}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, },
url = {https://arxiv.org/abs/2609.07956},
keywords = {cs.LG},
eprint = {2609.07956},
archiveprefix = {arXiv},
}
{}