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Tabular foundation models (TFMs) outperform established machine learning models on tabular benchmarks through in-context learning. Building on this success, interest is growing in applying them to data streams, where data arrive continuously and evolve over time. On a stream, a TFM adapts by updating its context rather...
A collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision loop and derives surrogate models that approximate the ensemble consensus and expose human-readable sensor conditions associated with anomalous behavior is presented.
A hierarchical inference framework that combines edge-based retrieval with cloud-based TFM inference, allowing confident samples to be processed locally while uncertain instances are selectively offloaded to a cloud-hosted TFM.
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