Streaming Hierarchical Inference with Tabular Foundation Models

• URL: http://arxiv.org/abs/2609.07956v1
• Date: Mon, 07 Sep 2026 20:26:36 GMT
• Title: Streaming Hierarchical Inference with Tabular Foundation Models
• Abstract summary: textitHINT is a hierarchical inference framework that combines edge-based retrieval with cloud-based TFM inference.<n>A graph-based approximate nearest neighbor memory maintained over a sliding window provides local predictions and uncertainty estimates.<n>Experiments show textitHINT consistently identifies favorable trade-offs.
• Score: 1.43494686131174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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