feat: scaffold FCES-native C++ project with libtorch integration

- CMakeLists.txt with libtorch, GoogleTest, GoogleBenchmark, OpenMP, pybind11
- Header files: config, controller, population, fitness, evolution, spectral, oscillation, telemetry, optimizer
- Source implementations: controller (full micro-MLP forward pass, mutation, crossover), fitness (Welford's algorithm), oscillation (DFT), spectral (SVD rank), optimizer (sign-SGD stub)
- Tests: controller, population, fitness, optimizer (Google Test)
- Benchmarks: evolve throughput, optimizer step (Google Benchmark)
- Examples: simple optimization, PyTorch/libtorch integration
- Python extension: pybind11 bindings with setup.py
- README with architecture diagram and build instructions

include/fces/config.hpp

@@ -0,0 +1,82 @@
+#pragma once
+
+/**
+ * @file config.hpp
+ * @brief FCES Configuration — compile-time defaults and runtime overrides.
+ *
+ * Maps directly from Python's FCESConfig (Pydantic model) to a C++ struct
+ * with constexpr defaults and builder-pattern construction.
+ */
+
+#include <cstdint>
+#include <string>
+
+namespace fces {
+
+/**
+ * Core configuration for the FCES optimizer.
+ * All fields have sensible defaults matching the Python V49.0 implementation.
+ */
+struct FCESConfig {
+    // Learning rate (V49 optimal default)
+    float lr = 1.6e-3f;
+
+    // Weight decay coefficient
+    float weight_decay = 0.0f;
+
+    // Population size for evolutionary search
+    int population_size = 200;
+
+    // Total training steps (for progress-aware scheduling)
+    int total_steps = 5000;
+
+    // Signal mode for loss velocity calculation
+    std::string signal_mode = "relative";
+
+    // Grokking awareness coefficient (0.0 = disabled)
+    float grokking_coefficient = 0.1f;
+
+    // Spectral sensing frequency (every N steps)
+    int spectral_frequency = 10;
+
+    // Curriculum Spectral Regularization
+    bool csr_enabled = false;
+    int csr_warmup_steps = 500;
+    int csr_ramp_steps = 1000;
+
+    // Trust region clipping
+    float trust_region_clip = 0.01f;
+
+    // Rollback threshold
+    float rollback_threshold = 1.5f;
+
+    // Adaptive weight decay
+    bool adaptive_wd = false;
+
+    // Parasitic mode (gradient alignment reward)
+    bool parasitic_mode = false;
+
+    // Ablation mode: "", "force_sign", "force_grad"
+    std::string ablation_mode = "";
+
+    // Fractional factorial scoring (CRO trick)
+    bool use_fractional_scoring = false;
+
+    // Direct construction mode (pop_size=1)
+    bool direct_construction = false;
+
+    // Banach-Tarski fission
+    bool use_banach_fission = false;
+
+    // Auto-population (stabilize on divergence)
+    bool auto_population = false;
+
+    // Builder pattern
+    FCESConfig& set_lr(float v) { lr = v; return *this; }
+    FCESConfig& set_population_size(int v) { population_size = v; return *this; }
+    FCESConfig& set_total_steps(int v) { total_steps = v; return *this; }
+    FCESConfig& set_grokking_coefficient(float v) { grokking_coefficient = v; return *this; }
+    FCESConfig& set_direct_construction(bool v) { direct_construction = v; return *this; }
+};
+
+}  // namespace fces