We implement a Multi-Head Latent Attentioninspired aggregator that compresses multi-modal telemetry intoper-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 yourLatentAggregator (trend and anomaly routines) and … Continue reading Latent Aggregation for Real-Time Compression of Multi-Modal Metrics