feat: modernize PSO and add convergence research

Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery.

Constraint: Preserve one-shot official-test sealing and auditable research artifacts

Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local

Confidence: high

Scope-risk: broad

Not-tested: Production CUDA run on pieroot-server

tests/conftest.py

@@ -0,0 +1,44 @@
+import pytest
+import torch
+import torch.nn as nn
+
+
+@pytest.fixture
+def xor_data():
+    """
+    Returns deterministic XOR input features (4, 2) and labels (4, 1) as float32 torch tensors on CPU.
+    """
+    x = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]], dtype=torch.float32)
+    y = torch.tensor([[0.0], [1.0], [1.0], [0.0]], dtype=torch.float32)
+    return x, y
+
+
+@pytest.fixture
+def model_factory():
+    """
+    Factory fixture producing deterministic, PyTorch nn.Module models.
+    Supports units tuning, zero initialization, and input/output dimension changes.
+    """
+
+    def _create_model(
+        units: int = 4,
+        zero_init: bool = False,
+        input_dim: int = 2,
+        output_dim: int = 1,
+    ) -> nn.Module:
+        torch.manual_seed(42)
+        layers = [
+            nn.Linear(input_dim, units),
+            nn.ReLU(),
+            nn.Linear(units, output_dim),
+        ]
+        model = nn.Sequential(*layers)
+        if zero_init:
+            for m in model.modules():
+                if isinstance(m, nn.Linear):
+                    nn.init.zeros_(m.weight)
+                    if m.bias is not None:
+                        nn.init.zeros_(m.bias)
+        return model
+
+    return _create_model