SOURCE-LINKED INTELLIGENCE

Streaming Hierarchical Inference with Tabular Foundation Models

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

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 offload

Read original source ↗ Open in workspace

recordType

paper

region

Global