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/controller.hpp

@@ -0,0 +1,145 @@
+#pragma once
+
+/**
+ * @file controller.hpp
+ * @brief FuzzyController and Genome — the decision-making units of FCES.
+ *
+ * Each controller contains a Genome (neural network weights) that maps
+ * layer statistics to update decisions (multiplier, sign_gate, wd_mult).
+ *
+ * Port of: packages/fces/core/controller.py
+ */
+
+#include <array>
+#include <cstdint>
+#include <memory>
+#include <random>
+#include <string>
+#include <vector>
+
+#include <torch/torch.h>
+
+namespace fces {
+
+// Controller input dimension (layer stats features)
+constexpr int GENOME_INPUT_DIM = 9;
+// Controller hidden dimension
+constexpr int GENOME_HIDDEN_DIM = 16;
+// Controller output dimension: [multiplier, sign_gate, wd_mult]
+constexpr int GENOME_OUTPUT_DIM = 3;
+// Total genome size: input->hidden weights + hidden biases + hidden->output weights + output biases
+constexpr int GENOME_SIZE =
+    (GENOME_INPUT_DIM * GENOME_HIDDEN_DIM) +   // input -> hidden weights
+    GENOME_HIDDEN_DIM +                          // hidden biases
+    (GENOME_HIDDEN_DIM * GENOME_OUTPUT_DIM) +   // hidden -> output weights
+    GENOME_OUTPUT_DIM;                           // output biases
+
+/**
+ * Genome — the "DNA" of a fuzzy controller.
+ * A flat array of floats encoding a micro-MLP.
+ */
+struct Genome {
+    std::array<float, GENOME_SIZE> weights{};
+    std::array<float, GENOME_SIZE> gene_success{};
+    float sigma_gene = 0.1f;
+    float plasticity = 1.0f;
+
+    /// Initialize with random weights from a normal distribution
+    void randomize(std::mt19937& rng);
+
+    /// Deep copy
+    Genome clone() const;
+};
+
+/**
+ * FuzzyController — a single agent in the evolutionary population.
+ *
+ * Lifecycle:
+ * 1. Created via random initialization or crossover/mutation
+ * 2. Activated for `selection_interval` steps
+ * 3. Evaluated based on loss improvement during its tenure
+ * 4. Evolved (crossover/mutation) or culled based on fitness
+ */
+class FuzzyController {
+public:
+    /// Unique identifier
+    uint64_t id;
+
+    /// The neural genome
+    Genome genome;
+
+    /// Fitness scores
+    float fitness = 0.0f;
+    float lifetime_fitness = 0.0f;
+    float ema_fitness = 0.0f;
+    int evaluation_count = 0;
+    int age = 0;
+
+    /// Origin tracking
+    std::string origin = "random";
+
+    /// Trust region violation counter
+    int trust_violations = 0;
+
+    /// Rolling fitness history (for Phase 23 strategies)
+    std::vector<float> fitness_history;
+
+    // ---------------------------------------------------------------
+    // Construction
+    // ---------------------------------------------------------------
+
+    FuzzyController();
+    explicit FuzzyController(Genome genome);
+
+    // ---------------------------------------------------------------
+    // Core Operations
+    // ---------------------------------------------------------------
+
+    /**
+     * Forward pass through the micro-MLP to produce update decisions.
+     *
+     * @param layer_stats  Vector of per-layer feature maps
+     * @param loss_trend   Current loss velocity
+     * @param step_pct     Training progress [0, 1]
+     * @param rollback_rate Rolling average rollback frequency
+     * @param grad_stability Gradient coefficient of variation
+     * @param spectral_alpha Log spectral rank
+     * @param stagnation_intensity Stagnation counter / 500
+     * @param kzm_damping Kibble-Zurek damping factor
+     * @param projected_drift Projected loss drift
+     * @return Tensor of shape [num_groups, 3] — (mult, sign_gate, wd_mult)
+     */
+    torch::Tensor decide_update(
+        const std::vector<std::vector<float>>& layer_stats,
+        float loss_trend,
+        float step_pct,
+        float rollback_rate,
+        float grad_stability,
+        float spectral_alpha,
+        float stagnation_intensity,
+        float kzm_damping,
+        float projected_drift
+    );
+
+    // ---------------------------------------------------------------
+    // Evolutionary Operators
+    // ---------------------------------------------------------------
+
+    /// Create a mutated child
+    FuzzyController mutate(float current_loss, float sigma_scale = 1.0f) const;
+
+    /// Crossover with another controller
+    FuzzyController crossover(const FuzzyController& partner, bool use_alignment = true) const;
+
+    /// Create an orthogonal counter-strategy (Phoenix Rebirth)
+    FuzzyController create_orthogonal_child(float intensity = 1.0f) const;
+
+    /// Banach-Tarski fission: split into two complementary children
+    std::pair<FuzzyController, FuzzyController> banach_tarski_fission(float intensity = 1.0f) const;
+
+private:
+    static std::atomic<uint64_t> next_id_;
+    static thread_local std::mt19937 rng_;
+};
+
+}  // namespace fces