Interactive decision tree for classifying objects into Structural Explainability identity and persistence regimes.
-
Updated
Aug 1, 2026 - Python
Interactive decision tree for classifying objects into Structural Explainability identity and persistence regimes.
Quant research pipeline for XAUUSD regime classification using macroeconomic features, Random Forest, Temporal Convolutional Networks (TCN), walk-forward validation, and trading-system backtesting.
Telemetry-based taxonomy of how LLMs strain, drift, and hallucinate — measured from layer activations, attention, KV cache, and MoE routing.
Add a description, image, and links to the regime-classification topic page so that developers can more easily learn about it.
To associate your repository with the regime-classification topic, visit your repo's landing page and select "manage topics."