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Sovereign Intelligence Architecture — SI-v7.1
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14-day lead-time on Phase-4 sovereign breaches. Built for Tier-1 capital in a nonlinear world. Kalman smoother, EM-estimated parameters, cross-asset synchronization.
SI-V3 is delivered as an integrated system of five production modules. Each is independently deployable and audit-ready for Tier-1 sovereign capital environments.
| DEPLOYMENT | NAMESPACE | REPLICAS | READY | IMAGE TAG | CPU REQ | MEM REQ | ACCEL | STATUS |
|---|---|---|---|---|---|---|---|---|
| quant-risk-backend | production | 4 | 4/4 | sha-a3f7c2d | 16 cpu | 128Gi | GPU×4 | RUNNING |
| quant-risk-frontend | production | 3 | 3/3 | sha-a3f7c2d | 500m | 512Mi | CPU | RUNNING |
| quant-risk-worker | production | 6 | 6/6 | sha-a3f7c2d | 8 cpu | 32Gi | GPU×2 | RUNNING |
| quant-risk-fpga | production | 2 | 2/2 | sha-a3f7c2d | 8 cpu | 64Gi | FPGA×4 | RUNNING |
| kafka-mirrormaker | production | 3 | 3/3 | cp-7.5.0 | 2 cpu | 4Gi | CPU | RUNNING |
| redis-global | production | 6 | 5/6 | 7.2.0 | 4 cpu | 64Gi | CPU | DEGRADED |
| drift-engine | production | 2 | 2/2 | sha-a3f7c2d | 2 cpu | 4Gi | CPU | RUNNING |
| lstm-inference | production | 3 | 3/3 | sha-a3f7c2d | 16 cpu | 128Gi | GPU×4 | RUNNING |
| RESOURCE | REQUESTED | LIMIT | HARD CEILING | UTILIZATION | STATUS |
|---|---|---|---|---|---|
| cpu | 128 cores | 256 cores | 512 cores | 50% | OK |
| nvidia.com/gpu | 28 | 32 | 32 | 87% | HIGH |
| xilinx.com/fpga | 8 | 16 | 16 | 50% | OK |
| memory | 512Gi | 1Ti | 2Ti | 50% | OK |
| pods | 186 | 500 | 500 | 37% | OK |
| Server Host | bpipe.internal.corp |
| Server Port | 8194 |
| Auth Type | APPLICATION_CREDENTIALS |
| Max Connections | 10 |
| Tick Interval | 0 (REAL-TIME) |
| Architecture | LSTM Encoder |
| Hidden Size | 512 |
| Num Layers | 4 |
| Optimizer | Fractional Adam |
| Frac Order | 0.9 |
| TRT Precision | FP16 |
| Registered Model | regime_lstm_prod |
| TICKER | DESCRIPTION | ZSCORE |
|---|---|---|
| DGS10 | 10Y Treasury Yield | 0.38 |
| T10Y2Y | Yield Curve Spread | -0.84 |
| VIXCLS | CBOE VIX | 0.94 |
| DCOILWTICO | WTI Crude Oil | 1.82 |
| CPIAUCSL | CPI (All Items) | 1.61 |
| UNRATE | Unemployment Rate | -0.28 |
| ID | LABEL | DECAY MULT | STATUS |
|---|---|---|---|
| 0 | bull_trending | 1.00× | NOMINAL |
| 1 | bear_trending | 0.75× | MODERATE |
| 2 | high_volatility | 0.60× | ACTIVE ◄ |
| 3 | liquidity_crisis | 0.40× | ALERT |
| 4 | flash_crash | 0.20× | CRITICAL |
| Prometheus Retention | 90 days |
| GPU Exporter | DCGM v3.1.7 |
| Grafana Dashboards | 5 auto-imported |
| AlertManager | PagerDuty + Slack |
| RUN | SHA | BUILD | DRIFT | STATUS | DUR |
|---|---|---|---|---|---|
| #1042 | a3f7c2d | PASS | 18% OK | SUCCESS | 8m 34s |
| #1041 | c1b9a3f | PASS | 38% WARN | SUCCESS | 9m 12s |
| #1040 | e4d2b8c | PASS | 43% ROLLBACK | ROLLED BACK | 11m 03s |
| SECRET | REFRESH | STATUS |
|---|---|---|
| bloomberg-fred-creds | 1h | SYNCED |
| database-creds | 1h | SYNCED |
| redis-creds | 1h | SYNCED |
| kafka-sasl-creds | 1h | SYNCED |
Real-time state-space engine with Kalman smoother E-step, EM M-step, live asset feeds, and regime classification. Full technical view with 2-second refresh cycle and Phase-4 breach detection.
Formal specification: Kalman filter framework, EM estimation procedure, and cross-asset synchronization model.
AUC 0.86 classifier. 21-year backtest 2005–2026. Phase-4 precision across 142 sovereign events.
Inspector-General audit-ready. Basel IV capital framework. SR 11-7 model risk governance. ISO 27001.
38-node sovereign graph. SIR-Kalman hybrid diffusion. 5 cross-asset contagion channels with pre-Phase-3 early warning.
Containerized Docker · RESTful API · Committee alert webhooks · Bloomberg B-PIPE · Air-gapped on-premise option.
Rigorous mathematical foundation: T5 spectral synchronization monotonicity, T6 covariance explosion, T7 Lyapunov mean-square stability, T8–T10 concentration, identifiability, and pitchfork bifurcation.
SI-V3 operates a continuous-time state-space model with EM-estimated parameters, tracking latent stress variable x̂_t across five cross-asset synchronization channels.
SI-V3 is not publicly distributed. Technical annex and validation files are provided via structured committee engagement under NDA only.
SI-V3 models a scalar latent stress process x_t evolving according to a linear state-space system:
x_{t+1} = Fx_t + ε_t, ε_t ~ N(0,Q). The observed cross-asset signal y_t is linked
to the latent state by y_t = Hx_t + η_t, η_t ~ N(0,R). For non-Gaussian regimes,
EKF and UKF (sigma-point) extensions are available; particle filter posterior approximation
is deployed when distributions become multimodal under synchronization-induced nonlinearity.
Parameters (F, H, Q, R) are estimated via Expectation-Maximization (EM) on a rolling 21-year window. The E-step runs the Rauch-Tung-Striebel (RTS) Kalman smoother. The M-step updates parameters to maximize expected complete-data log-likelihood. Identifiability is guaranteed by persistence-of-excitation (T9): Σ x_t x_tᵀ ≻ 0 with nonsingular noise covariances and observable (A, H) pair.
Let G_t = (V_t, E_t) be the dynamic cross-asset graph with adjacency matrix A_t and degree matrix D.
The normalized graph Laplacian is L(G_t) = I − D⁻¹/²A_tD⁻¹/². The synchronization
probability is defined spectrally: p_t = Φ(G_t) = σ(λ_max(L(G_t)) / λ_c), with λ_c = 2.0.
Theorem T5 (Monotonicity): If dλ_max(L(G_t))/dt > 0, then dp_t/dt > 0. Proof: dp_t/dt = σ′(z_t) · (1/λ_c) · dλ_max/dt. Since σ′(z) > 0 always, monotonicity follows immediately. QED.
G_t counts cross-asset channels (Sovereign CDS, Equities, FX, Rates, Commodities) whose residuals positively co-move with x̂_t above threshold. G_t ≥ 3 → Advisory. G_t ≥ 4 → Committee Alert.
The regime functional R_t = (p_t, tr(P_t)). Phase-4 is defined:
R_t = 4 ⟺ p_t > p* ∧ tr(P_t) > σ_c², with p* = 0.75, σ_c² = 0.25.
Theorem T6 (Covariance Explosion): If ρ(A) ≥ 1 or observation quality deteriorates (R → ∞), then tr(P_t) → ∞ and Phase-4 becomes reachable. The Kalman recursion P_{t+1} = AP_tAᵀ + Q − K_tHP_t diverges when ρ(A) ≥ 1. Lyapunov stability (T7) requires AᵢᵀPAᵢ − P ≺ 0 for all regime matrices Aᵢ — currently satisfied with ρ(A) = 0.82.
Phase-4 — the target detection class — requires simultaneous spectral coherence and inferential instability: x̂_t > 0.75, p_t > 0.75, G_t ≥ 4, and tr(P_t) exceeding the divergence threshold. The classifier achieves AUC 0.86 on the 21-year out-of-sample validation panel.
The full system is a hybrid automaton H = (X, Q, f, Δ): continuous latent state X driven by
dx_t = f(x_t, R_t)dt + ΣdW_t, discrete regime set Q = {1,2,3,4}, and transition
operator Δ triggered by threshold crossings of p_t and tr(P_t). Near criticality, the
synchronization order parameter m_t satisfies a stochastic pitchfork bifurcation
dm/dt = αm − βm³ + η_t (T10), producing coherent synchronized phases when α > 0.
SI-V3 was evaluated on an expanding-window out-of-sample backtest from 2005 to 2026. Parameters estimated on the historical window ending 90 days before each evaluation date — no look-ahead bias. Test set: 142 Phase-4 sovereign stress events across 38 countries.
SI-V3 is built to comply with Federal Reserve SR 11-7 model risk management guidance. An independent validation package covers conceptual soundness, outcomes analysis, sensitivity testing, and benchmarking against challenger models.
Signal outputs are compatible with FRTB Internal Models Approach (IMA) risk factor documentation. Sensitivity analysis outputs (PV01, CS01, delta) accompany Phase-4 signals to support capital desk integration.
SI-V3 maintains a complete immutable audit trail. Every model run produces a signed log including input data checksums, EM iteration history, parameter values, and output signals. Outputs are fully reproducible from frozen model snapshots.
ISO 27001 certified infrastructure. Air-gapped on-premise deployment available for clients with classified sovereign environments or strict data residency requirements. All data at rest is AES-256 encrypted.
TRCS models second-order contagion pathways between 38 sovereign entities using bilateral CDS spread correlations as edge weights. The dynamic topology is recalibrated monthly using rolling 12-month correlation windows.
An epidemic-style diffusion model (Susceptible-Infected-Recovered) is overlaid on the Kalman-filtered state estimates at each node. This captures nonlinear contagion acceleration — the mechanism behind Phase-3 to Phase-4 transitions.
Five cross-asset channels are monitored per node: Sovereign CDS, Equities, FX, Rates, and Commodities. G_t counts synchronizing channels per timestep. The dynamic coupling matrix A_t drives the normalized Laplacian L(G_t) — when λ_max grows, spectral synchronization p_t rises monotonically (T5). TRCS signals fire an average of 3 days earlier than primary SI-V3 Phase-4 classification by detecting λ_max acceleration before threshold crossing.
RESTful API provides real-time access to x̂_t, p_t, G_t, regime classification, and all five cross-asset channel readings. Endpoints refresh every 2 minutes during market hours. Full OpenAPI 3.0 specification provided to approved clients.
Configurable webhook alerts fire on G_t threshold breach (G_t ≥ 2, 3, or 4 — client-configurable). Payloads include: timestamp, x̂_t, p_t, G_t, regime, active channels, and recommended committee action. HTTPS POST with HMAC-SHA256 signing.
The SI-V3 framework is formally expressible as Hybrid Spectral-Stochastic Latent Risk Dynamics, sitting at the intersection of stochastic control, spectral graph dynamics, hybrid automata, probabilistic finance, and critical-state inference theory. Each layer has a rigorous mathematical grounding:
Let G_t = (V_t, E_t) be the dynamic cross-asset graph. Define normalized Laplacian:
L(G_t) = I − D⁻¹/²A_tD⁻¹/². The spectral synchronization functional is
p_t = σ(λ_max(L(G_t)) / λ_c) with logistic sigmoid σ(z) = 1/(1+e⁻ᶻ).
Theorem: If dλ_max(L(G_t))/dt > 0 then dp_t/dt > 0 (provided λ_c > 0). Proof: dp_t/dt = σ′(z_t) · (1/λ_c) · dλ_max/dt. Since σ′(z) > 0 for all z, monotonicity follows immediately. Low p_t = weak synchronization; moderate = correlated stress; high = systemic phase coherence. QED.
Phase-4 trigger: R_t = 4 ⟺ p_t > p* ∧ tr(P_t) > σ_c². This is mathematically
elegant — p_t captures systemic coupling, tr(P_t) captures inferential instability.
Theorem: If ρ(A) ≥ 1, or observation quality deteriorates (R → ∞), then tr(P_t) → ∞ and Phase-4 becomes reachable. Sketch: The Kalman recursion P_{t+1} = AP_tAᵀ + Q − K_tHP_t diverges when ρ(A) ≥ 1 (unstable Riccati). Poor observability likewise inflates covariance. QED.
For regime-dependent dynamics A = A(R_t), require common Lyapunov stability. Theorem: If ∃ P ≻ 0 such that AᵢᵀPAᵢ − P ≺ 0 for all regime matrices Aᵢ, then the switched system is mean-square stable. SI-V3 enforces ρ(A) < 1 across all regimes (currently 0.82). V(x) = xᵀPx serves as the Lyapunov function.
Assume G_t ~ G(n,p) (Erdős–Rényi) or stochastic block model. For random graph Laplacians, λ_max(L(G_t)) concentrates around a deterministic limit with high probability. Theorem: For sufficiently large n: Pr(|λ_max(L) − μ_n| > ε) ≤ Ce⁻ᶜⁿᵉ². Consequence: synchronization estimates become statistically stable in large cross-asset markets.
Persistence of excitation requires Σ_{t=0}^T x_t x_tᵀ ≻ 0. Theorem: If (A, H) observable, excitation persistent, and noise covariances nonsingular, then model parameters are identifiable up to similarity transforms. This justifies the EM estimation procedure and ensures the learned (F, Q, R) are unique.
Define the synchronization order parameter m_t = (1/n) Σ sᵢ(t). Near criticality:
dm/dt = αm − βm³ + η_t — a stochastic pitchfork bifurcation.
Theorem: If α > 0, the unstable symmetric equilibrium loses stability and coherent
synchronized phases emerge. This formalizes the Phase-3 → Phase-4 transition as a bifurcation event,
not merely a threshold crossing.
The complete system is: X = continuous latent state, Q = {1,2,3,4} discrete regime set, f = continuous stochastic flow (dx_t = f(x_t, R_t)dt + ΣdW_t), Δ = discrete transition operator triggered by threshold crossings of p_t and tr(P_t).
When regime jumps are discontinuous, distributions multimodal, or synchronization induces nonlinearity: EKF — linearized observation y_t = h(x_t) + v_t; UKF — sigma-point approximation for nonlinear propagation; Particle Filter — posterior p(x_t | y_{1:t}) ≈ Σᵢ wᵢ(t) δ(x_t − xᵢ(t)).
The fixed four-regime space is extended via Dirichlet Process: G ~ DP(α, G₀). This allows emergent discovery of unknown crisis regimes, latent systemic structures, and evolving instability phases — the model adapts its regime count to the data rather than imposing a fixed partition.
Latent dynamics — stochastic state-space · Synchronization — spectral graph theory · Regime switching — hybrid automata · Criticality — bifurcation theory · Inference — Bayesian filtering · Learning — EM / nonparametric Bayes · Stability — Lyapunov systems · Detection — thresholded uncertainty geometry
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