We process 3.2PB of historical microstructure data using Linear State-Space Models (Mamba S6). Replacing the O(N²) attention bottleneck to capture volatility regimes across 35-year horizons.
Linear scaling O(N) allows for infinite context windows, resolving the "Regime Memory" problem of Transformers.
Proprietary tensor masking to eliminate survivorship bias from 55,000 delisted assets in real-time training.
Distributed Ray/Anyscale orchestration across ephemeral GPU clusters. Zero-downtime checkpoints.