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## Domain Architecture - Complete domain-to-service mapping for 16 verified domains - Subdomain architecture for blackroad.systems and blackroad.io - GitHub organization mapping (BlackRoad-OS repos) - Railway service-to-domain configuration - DNS configuration templates for Cloudflare ## Extracted Services ### AIops Service (services/aiops/) - Canary analysis for deployment validation - Config drift detection - Event correlation engine - Auto-remediation with runbook mapping - SLO budget management ### Analytics Service (services/analytics/) - Rule-based anomaly detection with safe expression evaluation - Cohort analysis with multi-metric aggregation - Decision engine with credit budget constraints - Narrative report generation ### Codex Governance (services/codex/) - 82+ governance principles (entries) - Codex Pantheon with 48+ agent archetypes - Manifesto defining ethical framework ## Integration Points - AIops → infra.blackroad.systems (blackroad-os-infra) - Analytics → core.blackroad.systems (blackroad-os-core) - Codex → operator.blackroad.systems (blackroad-os-operator) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Codex 22 — Adversarial Training with CVaR — Robust by the Tail
Fingerprint: 23064887b1469b19fa562e8afdee5e9046bedf99aa9cd7142c35e38f91e6fef2
Aim
Optimize models for worst-case performance by focusing on tail risk instead of average loss.
Core
- Minimize conditional value at risk (CVaR) at level (\alpha): (\min_\theta \ \text{CVaR}\alpha(\ell(f\theta(x + \delta), y))) subject to (|\delta| \le \epsilon).
- Generate hard examples through projected gradient descent (PGD) or expectation over transformation (EOT) loops.
- Use randomized smoothing or Lipschitz bounds to certify robustness where feasible.
Runbook
- In the inner loop, create adversarial perturbations, tracking tail losses throughout training.
- In the outer loop, optimize parameters to minimize CVaR while enforcing a floor on clean accuracy.
- Certify robustness post-training and log certified radii alongside accuracy metrics.
Telemetry
- Tail loss trajectories and CVaR estimates.
- Gap between clean and robust accuracy.
- Certified radii coverage across validation sets.
Failsafes
- When tail loss exceeds budget, reduce exposure, increase regularization, or pause deployment.
- Require human review before shipping models whose robustness certificates regress.
Tagline: Train for the attacks you dread, not the averages you like.