This repository introduces an adaptive formula inspired by the CHSH logic, designed to evaluate, test, and improve model performance across multiple conditions. By adapting CHSH principles into a flexible structure, it provides a systematic way to analyze results, ensure reliability, and explore deeper insights in experimentation.
Version Apex Elite V10.21 — Proactively Adaptive, Auditable and Robust
Description
This project implements the Adaptive Deterministic EPR Formula up to version 10.21. The core novelty lies in adapting the logic of CHSH into an adaptive CHSH formulation that is directly embedded into the calibration and evaluation process. The formula is designed to remain deterministic, auditable, and resilient under shifting distributions and adversarial perturbations.
The framework is built with PyTorch 2.8.0, BoTorch, GPyTorch and NumPyro. It supports low overhead online inference as well as offline ablation, validation and synthetic adversarial evaluation.
Key Features
Adaptive CHSH based logic for calibration and drift handling
Drift detection using EWMA, Brier, ECE and conformal monitoring
OOD shift tracking with adaptive thresholds
Latent adversarial training through LATPC
ARS f DP multi step certified robustness
Continual calibration with NUCFL and CKDF V2
Feature level logging with SHAP and Diff DML
Dynamic scheduler for automatic retraining on anomaly detection
Prometheus based monitoring and alerting
Nightly synthetic adversarial testing with Pareto cost profiling
Usage
Run online inference with adaptive calibration:
python run_online.py
Run offline evaluation, ablation and adversarial testing:
python run_offline.py
Results including metrics, calibration reports and logs are stored in outputs/
Repository Structure
src/ main modules for adaptive CHSH logic, calibration and drift detection
configs/ configuration files and environment manifests
scripts/ utilities for training, retraining and monitoring
outputs/ experiment results, checkpoints and validation dossiers
Target Metrics
Brier ≤ 0.004 ± 0.001
ECE ≤ 0.001 ± 0.0005
p99 latency < 25 ms
Robustness recall around 160 percent with ARS f DP and LATPC
OOD shift tracking < 0.04 percent ± 0.01
License
This project is licensed under the Apache License 2.0. You may use, modify and distribute it under the terms of that license. See the LICENSE file for details.