Latent Aggregation for Real-Time Compression of Multi-Modal Metrics

Latent Aggregation for Real-Time Compression of Multi-Modal Metrics

We implement a Multi-Head Latent Attentioninspired aggregator that compresses multi-modal telemetry into
per-topic latent summaries (count/avg/min/max, trend direction,
anomaly counts) and evaluate (i) anomaly detection quality, (ii)
trend direction accuracy, and (iii) lossy compression efficiency.
We show high F1 for anomalies, accurate trend sign, and 10–
40× compression at useful fidelity. The design follows your
LatentAggregator (trend and anomaly routines) and its
downstream latent-summary analysis.

#WuqingXinhaoLiandao / bgilbert1984