feat(runtime): evolve Sofia Engine to 3.0.0a1 scientific runtime

.gitignore

@@ -72,4 +72,7 @@ MANIFEST
 
 # OS
 .DS_Store
-Thumbs.db
+Thumbs.db
+
+# Node
+node_modules/

NOTICE

@@ -1,6 +1,6 @@
 Sofia Engine
 Copyright 2026 Rootcastle Engineering & Innovation
-Project home: https://github.com/rootcastleco/sofia-rl
+Project home: https://github.com/rootcastleco/sofia-ai
 
 This product includes software developed by the Rootcastle Engineering &
 Innovation team.

README.md

@@ -18,7 +18,7 @@
 [![Security Audited](https://img.shields.io/badge/security-threat%20modeled%20%7C%20no%20pickle-red.svg)](specs/001-sofia-engine-modernization/threat-model.md)
 [![Organization](https://img.shields.io/badge/developed%20by-Rootcastle%20Engineering-black.svg)](https://rootcastle.com/)
 
-[**Documentation**](https://rootcastleco.github.io/sofia-rl/) | [**GitHub Wiki**](https://github.com/rootcastleco/sofia-rl/wiki) | [**NPM Package**](https://www.npmjs.com/package/@rootcastle/sofia-engine) | [**REI SignalLab**](https://github.com/rootcastleco/rei-signallab) | [**Rootcastle**](https://rootcastle.com/)
+[**Documentation**](https://rootcastleco.github.io/sofia-ai/) | [**GitHub Wiki**](https://github.com/rootcastleco/sofia-ai/wiki) | [**NPM Package**](https://www.npmjs.com/package/@rootcastle/sofia-engine) | [**REI SignalLab**](https://github.com/rootcastleco/rei-signallab) | [**Rootcastle**](https://rootcastle.com/)
 
 ---
 

SECURITY.md

@@ -7,7 +7,7 @@ Report it privately to the maintainers instead:
 
 - Open a private advisory: GitHub → *Security* → *Report a vulnerability*
 - Or email the Rootcastle Engineering & Innovation team via the repository
-  maintainers with the subject `[sofia-rl security]`.
+  maintainers with the subject `[sofia-ai security]`.
 
 Please include: module and version affected, a description of the weakness,
 and — if you have one — a minimal reproduction. Coordinated disclosure is
@@ -24,7 +24,8 @@ active hardening but should still be disclosed through the same channel.
 
 | Version | Supported |
 | --- | --- |
-| 2.x (sofia-engine) | Yes |
+| 3.x (sofia-engine) | Yes |
+| 2.x (sofia-engine) | Maintenance only |
 | 1.x (legacy sofia_ai) | No — reference only |
 
 ## Design posture

benchmarks/results/baseline_benchmark.json

@@ -0,0 +1,83 @@
+{
+  "timestamp_utc": "2026-09-21T23:56:41.289504+00:00",
+  "environment": {
+    "os": "Windows",
+    "os_release": "11",
+    "os_version": "10.0.26200",
+    "machine": "AMD64",
+    "processor": "Intel64 Family 6 Model 165 Stepping 2, GenuineIntel",
+    "python_version": "3.12.10",
+    "python_compiler": "MSC v.1943 64 bit (AMD64)",
+    "python_implementation": "CPython"
+  },
+  "benchmarks": {
+    "import_time_ms": 342.76,
+    "fft_psd_1024": {
+      "n_iterations": 100,
+      "mean_us": 319.19,
+      "p50_us": 307.55,
+      "p95_us": 362.82,
+      "p99_us": 445.46,
+      "min_us": 303.3,
+      "max_us": 501.2
+    },
+    "fft_psd_4096": {
+      "n_iterations": 100,
+      "mean_us": 430.25,
+      "p50_us": 387.35,
+      "p95_us": 597.33,
+      "p99_us": 713.05,
+      "min_us": 378.7,
+      "max_us": 787.2
+    },
+    "analytic_envelope_2048": {
+      "n_iterations": 100,
+      "mean_us": 140.95,
+      "p50_us": 127.8,
+      "p95_us": 200.21,
+      "p99_us": 237.93,
+      "min_us": 126.6,
+      "max_us": 241.2
+    },
+    "feature_extraction_1024": {
+      "n_iterations": 100,
+      "mean_us": 2521.47,
+      "p50_us": 2415.45,
+      "p95_us": 3304.84,
+      "p99_us": 3490.95,
+      "min_us": 2165.0,
+      "max_us": 3554.8
+    },
+    "electrical_symmetrical_components": {
+      "n_iterations": 100,
+      "mean_us": 60.68,
+      "p50_us": 55.5,
+      "p95_us": 92.99,
+      "p99_us": 102.7,
+      "min_us": 54.7,
+      "max_us": 103.0
+    },
+    "sofia_vm_forward_inference": {
+      "n_iterations": 100,
+      "mean_us": 1751.24,
+      "p50_us": 1675.8,
+      "p95_us": 2210.2,
+      "p99_us": 2457.12,
+      "min_us": 1560.8,
+      "max_us": 2537.8
+    },
+    "sofia_vm_training_step": {
+      "n_iterations": 100,
+      "mean_us": 6609.69,
+      "p50_us": 6442.9,
+      "p95_us": 7483.03,
+      "p99_us": 9051.64,
+      "min_us": 6194.4,
+      "max_us": 9362.5
+    }
+  },
+  "memory": {
+    "current_traced_kb": 7116.01,
+    "peak_traced_kb": 7137.29
+  }
+}

benchmarks/run_benchmarks.py

@@ -0,0 +1,155 @@
+"""Standardized Benchmark Runner for Sofia Engine Runtime.
+
+Collects real, unembellished latency, throughput, and memory measurements on the
+host environment in accordance with specs/sofia-runtime-v3/performance-budgets.md.
+Zero fabricated numbers.
+"""
+
+from __future__ import annotations
+
+import gc
+import json
+import os
+import platform
+import sys
+import time
+import tracemalloc
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+RESULTS_DIR = REPO_ROOT / "benchmarks" / "results"
+
+
+def measure_benchmark(
+    fn: Callable[[], Any],
+    n_iterations: int = 100,
+    warmup: int = 10,
+) -> dict[str, float]:
+    """Run warmup iterations and measure statistical latency percentiles."""
+    for _ in range(warmup):
+        fn()
+
+    durations: list[float] = []
+    for _ in range(n_iterations):
+        t0 = time.perf_counter_ns()
+        fn()
+        t1 = time.perf_counter_ns()
+        durations.append((t1 - t0) / 1000.0)  # microseconds
+
+    durations.sort()
+    p50 = float(np.percentile(durations, 50))
+    p95 = float(np.percentile(durations, 95))
+    p99 = float(np.percentile(durations, 99))
+    mean = float(np.mean(durations))
+
+    return {
+        "n_iterations": n_iterations,
+        "mean_us": round(mean, 2),
+        "p50_us": round(p50, 2),
+        "p95_us": round(p95, 2),
+        "p99_us": round(p99, 2),
+        "min_us": round(durations[0], 2),
+        "max_us": round(durations[-1], 2),
+    }
+
+
+def run_all_benchmarks() -> dict[str, Any]:
+    print("=" * 70)
+    print("SOFIA ENGINE RUNTIME — STANDARDIZED BENCHMARK RUNNER")
+    print("=" * 70)
+
+    tracemalloc.start()
+
+    # 1. Package import time
+    t0 = time.perf_counter()
+    import sofia_ai
+    import sofia_ai.signal.electrical as electrical
+    import sofia_ai.signal.envelope as envelope
+    import sofia_ai.signal.spectral as spectral
+    import sofia_ai.features as features
+    import sofia_ai.learning.asm as asm
+    import sofia_ai.learning.asm.neural as asm_neural
+    t1 = time.perf_counter()
+    import_time_ms = round((t1 - t0) * 1000.0, 2)
+    print(f"[*] Package import time: {import_time_ms} ms")
+
+    # 2. FFT / Welch PSD (1024 samples)
+    x_1024 = np.sin(2.0 * np.pi * 50.0 * np.linspace(0, 1, 1024, endpoint=False))
+    bench_psd_1024 = measure_benchmark(lambda: spectral.welch_psd(x_1024, sample_rate=1000.0, segment_length=256))
+    print(f"[*] FFT/PSD (1024 samples) p50: {bench_psd_1024['p50_us']} us")
+
+    # 3. FFT / Welch PSD (4096 samples)
+    x_4096 = np.sin(2.0 * np.pi * 50.0 * np.linspace(0, 4, 4096, endpoint=False))
+    bench_psd_4096 = measure_benchmark(lambda: spectral.welch_psd(x_4096, sample_rate=1000.0, segment_length=512))
+    print(f"[*] FFT/PSD (4096 samples) p50: {bench_psd_4096['p50_us']} us")
+
+    # 4. Hilbert Analytic Envelope (2048 samples)
+    x_2048 = (1.0 + 0.5 * np.sin(2.0 * np.pi * 10.0 * np.linspace(0, 1, 2048, endpoint=False))) * np.sin(2.0 * np.pi * 150.0 * np.linspace(0, 1, 2048, endpoint=False))
+    bench_envelope = measure_benchmark(lambda: envelope.amplitude_envelope(x_2048))
+    print(f"[*] Analytic Envelope (2048 samples) p50: {bench_envelope['p50_us']} us")
+
+    # 5. Full Statistical Feature Extraction (1024 samples)
+    bench_features = measure_benchmark(lambda: features.extract_from_array(x_1024, sample_rate=1000.0))
+    print(f"[*] Feature Extraction (1024 samples) p50: {bench_features['p50_us']} us")
+
+    # 6. Electrical Power Metrics & Symmetrical Components
+    bench_electrical = measure_benchmark(lambda: electrical.compute_symmetrical_components(230.0, 0.0, 210.0, -120.0, 200.0, 120.0))
+    print(f"[*] Electrical Symmetrical Components p50: {bench_electrical['p50_us']} us")
+
+    # 7. Sofia VM Forward Inference (Assembly Neural Network: 4 -> 8 -> 1)
+    ann = asm_neural.AssemblyNeuralNetwork(input_dim=4, hidden_dim=8, output_dim=1, learning_rate=0.01)
+    input_sample = np.array([0.5, -0.2, 0.8, 0.1], dtype=np.float64)
+    target_sample = np.array([0.75], dtype=np.float64)
+
+    bench_vm_inference = measure_benchmark(lambda: ann.forward(input_sample))
+    print(f"[*] Sofia VM Forward Inference (4->8->1) p50: {bench_vm_inference['p50_us']} us")
+
+    # 8. Sofia VM Training Step (Forward + Backprop + SGD)
+    bench_vm_train = measure_benchmark(lambda: ann.train_step(input_sample, target_sample))
+    print(f"[*] Sofia VM Training Step (4->8->1) p50: {bench_vm_train['p50_us']} us")
+
+    current_mem, peak_mem = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+
+    results = {
+        "timestamp_utc": datetime.now(timezone.utc).isoformat(),
+        "environment": {
+            "os": platform.system(),
+            "os_release": platform.release(),
+            "os_version": platform.version(),
+            "machine": platform.machine(),
+            "processor": platform.processor(),
+            "python_version": platform.python_version(),
+            "python_compiler": platform.python_compiler(),
+            "python_implementation": platform.python_implementation(),
+        },
+        "benchmarks": {
+            "import_time_ms": import_time_ms,
+            "fft_psd_1024": bench_psd_1024,
+            "fft_psd_4096": bench_psd_4096,
+            "analytic_envelope_2048": bench_envelope,
+            "feature_extraction_1024": bench_features,
+            "electrical_symmetrical_components": bench_electrical,
+            "sofia_vm_forward_inference": bench_vm_inference,
+            "sofia_vm_training_step": bench_vm_train,
+        },
+        "memory": {
+            "current_traced_kb": round(current_mem / 1024.0, 2),
+            "peak_traced_kb": round(peak_mem / 1024.0, 2),
+        },
+    }
+
+    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+    out_file = RESULTS_DIR / "baseline_benchmark.json"
+    out_file.write_text(json.dumps(results, indent=2) + "\n", encoding="utf-8")
+    print(f"[+] Saved baseline benchmark results to: {out_file}")
+    print("=" * 70)
+    return results
+
+
+if __name__ == "__main__":
+    run_all_benchmarks()

embedded/tests/test_sofia_features.c

@@ -13,6 +13,7 @@
 
 #include "../src/sofia_features.h"
 #include "../src/sofia_fixed.h"
+#include "golden_vectors.h"
 
 #include <math.h>
 #include <stdio.h>
@@ -188,6 +189,38 @@ static void test_q16_roundtrip(void)
     }
 }
 
+static void test_golden_vectors(void)
+{
+    sofia_workspace_t ws;
+    size_t c;
+    for (c = 0u; c < SOFIA_GOLDEN_N_CASES; ++c) {
+        const sofia_golden_case_t *gc = &sofia_golden_cases[c];
+        sofia_stat_features_t out;
+        sofia_status_t status;
+
+        status = sofia_features_init(&ws, 256);
+        check_true(status == SOFIA_OK, "golden init 256");
+
+        status = sofia_stat_features(gc->samples, gc->n, &out, &ws);
+        check_true(status == SOFIA_OK, "golden stat features ok");
+
+        check_close(out.mean, gc->expected[0], SOFIA_GOLDEN_TOLERANCE, "golden mean");
+        check_close(out.rms, gc->expected[1], SOFIA_GOLDEN_TOLERANCE, "golden rms");
+        check_close(out.peak, gc->expected[2], SOFIA_GOLDEN_TOLERANCE, "golden peak");
+        check_close(out.peak_to_peak, gc->expected[3], SOFIA_GOLDEN_TOLERANCE, "golden peak_to_peak");
+        check_close(out.variance, gc->expected[4], SOFIA_GOLDEN_TOLERANCE, "golden variance");
+        check_close(out.std, gc->expected[5], SOFIA_GOLDEN_TOLERANCE, "golden std");
+        check_close(out.crest_factor, gc->expected[6], SOFIA_GOLDEN_TOLERANCE, "golden crest_factor");
+        check_close(out.shape_factor, gc->expected[7], SOFIA_GOLDEN_TOLERANCE, "golden shape_factor");
+        check_close(out.impulse_factor, gc->expected[8], SOFIA_GOLDEN_TOLERANCE, "golden impulse_factor");
+        check_close(out.margin_factor, gc->expected[9], SOFIA_GOLDEN_TOLERANCE, "golden margin_factor");
+        check_close(out.skewness, gc->expected[10], SOFIA_GOLDEN_TOLERANCE, "golden skewness");
+        check_close(out.kurtosis, gc->expected[11], SOFIA_GOLDEN_TOLERANCE, "golden kurtosis");
+        check_close(out.zero_crossing_rate, gc->expected[12], SOFIA_GOLDEN_TOLERANCE, "golden zero_crossing_rate");
+        check_close(out.energy, gc->expected[13], SOFIA_GOLDEN_TOLERANCE, "golden energy");
+    }
+}
+
 int main(void)
 {
     printf("SOFIA embedded runtime tests\n");
@@ -198,6 +231,7 @@ int main(void)
     test_spectral_features();
     test_fixed_point();
     test_q16_roundtrip();
+    test_golden_vectors();
 
     printf("%d checks, %d failures\n", checks, failures);
     if (failures != 0) {

mkdocs.yml

@@ -1,9 +1,9 @@
 site_name: Sofia Engine
 site_description: Offline-First Edge Intelligence for Industrial Telemetry, Condition Monitoring & Safe Technical Automation
 site_author: Rootcastle Engineering & Innovation
-site_url: https://rootcastleco.github.io/sofia-rl/
-repo_url: https://github.com/rootcastleco/sofia-rl
-repo_name: rootcastleco/sofia-rl
+site_url: https://rootcastleco.github.io/sofia-ai/
+repo_url: https://github.com/rootcastleco/sofia-ai
+repo_name: rootcastleco/sofia-ai
 
 theme:
   name: material

packages/sofia-engine/NOTICE

@@ -1,6 +1,6 @@
 Sofia Engine
 Copyright 2026 Rootcastle Engineering & Innovation
-Project home: https://github.com/rootcastleco/sofia-rl
+Project home: https://github.com/rootcastleco/sofia-ai
 
 This product includes software developed by the Rootcastle Engineering &
 Innovation team.

packages/sofia-engine/README.md

@@ -16,7 +16,7 @@
 [![Security Audited](https://img.shields.io/badge/security-threat%20modeled%20%7C%20no%20pickle-red.svg)](specs/001-sofia-engine-modernization/threat-model.md)
 [![Organization](https://img.shields.io/badge/developed%20by-Rootcastle%20Engineering-black.svg)](https://rootcastle.com/)
 
-[**Documentation**](https://rootcastleco.github.io/sofia-rl/) | [**GitHub Wiki**](https://github.com/rootcastleco/sofia-rl/wiki) | [**NPM Package**](https://www.npmjs.com/package/@rootcastle/sofia-engine) | [**REI SignalLab**](https://github.com/rootcastleco/rei-signallab) | [**Rootcastle**](https://rootcastle.com/)
+[**Documentation**](https://rootcastleco.github.io/sofia-ai/) | [**GitHub Wiki**](https://github.com/rootcastleco/sofia-ai/wiki) | [**NPM Package**](https://www.npmjs.com/package/@rootcastle/sofia-engine) | [**REI SignalLab**](https://github.com/rootcastleco/rei-signallab) | [**Rootcastle**](https://rootcastle.com/)
 
 ---