Adaptive Embedding Format Switching and Semantic Translation in High-Volume Telemetry Systems

Adaptive Embedding Format Switching and Semantic Translation in High-Volume Telemetry Systems introduces AMC (Adaptive Multi-Vector Correlation), a telemetry-native embedding architecture designed to overcome the inefficiencies of static vector formats. Most telemetry pipelines today lock into a single encoding—dense, sparse, or quantized—regardless of the signal’s structure or task fidelity. AMC breaks from this by enabling per-datapoint format switching guided by entropy, volatility, sparsity, and anomaly detection feedback. It also provides a semantic translation framework with ϵ-bounded drift guarantees, ensuring that embeddings remain aligned and trustworthy across heterogeneous vector spaces. The system was validated on NASA’s MODIS Earth observation archive, a reproducible testbed with extreme heterogeneity (0.1%–98.7% sparsity variation), entropy variation, and temporal drift. AMC demonstrates: 37.1% storage savings compared to LZ4-compressed baselines +12.2% anomaly detection F1 over adaptive quantization <2.4ms median format-selection latency at >52K vectors/second throughput By combining runtime adaptation, forensic rollback, and verifiable semantic traceability, AMC establishes a new paradigm for embedding infrastructures in high-volume telemetry systems. This work offers both theoretical rigor and practical validation, making it extendable to domains such as observability, spaceflight operations, and large-scale sensor network