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

examples/pytorch_integration.cpp

@@ -0,0 +1,61 @@
+/**
+ * @file pytorch_integration.cpp
+ * @brief Example: train a small neural network with FCES via libtorch.
+ */
+
+#include <iostream>
+#include <torch/torch.h>
+#include "fces/optimizer.hpp"
+
+struct TinyNet : torch::nn::Module {
+    torch::nn::Linear fc1{nullptr}, fc2{nullptr};
+
+    TinyNet() {
+        fc1 = register_module("fc1", torch::nn::Linear(10, 32));
+        fc2 = register_module("fc2", torch::nn::Linear(32, 1));
+    }
+
+    torch::Tensor forward(torch::Tensor x) {
+        x = torch::relu(fc1->forward(x));
+        return fc2->forward(x);
+    }
+};
+
+int main() {
+    auto model = std::make_shared<TinyNet>();
+
+    std::vector<torch::Tensor> params;
+    for (auto& p : model->parameters()) params.push_back(p);
+
+    fces::FCESOptimizer optimizer(
+        params,
+        fces::FCESConfig{}
+            .set_lr(1.6e-3f)
+            .set_population_size(200)
+            .set_total_steps(1000)
+    );
+
+    // Generate synthetic regression data
+    auto x_train = torch::randn({100, 10});
+    auto y_train = torch::sin(x_train.sum(1, true));
+
+    for (int epoch = 0; epoch < 100; ++epoch) {
+        optimizer.zero_grad();
+        auto pred = model->forward(x_train);
+        auto loss = torch::mse_loss(pred, y_train);
+        loss.backward();
+        optimizer.step();
+        optimizer.update_fitness(loss.item<float>());
+
+        if (epoch % 10 == 0) {
+            std::cout << "Epoch " << epoch
+                      << " | Loss: " << loss.item<float>() << std::endl;
+        }
+    }
+
+    std::cout << "\nTraining complete. Final loss: "
+              << torch::mse_loss(model->forward(x_train), y_train).item<float>()
+              << std::endl;
+
+    return 0;
+}