Add distributed server implementation and protocol definitions

- Introduced `protocol.py` for shared data models used in server/client communication, including request and response schemas for registration, job submission, and status updates.
- Implemented `server.py` to manage a SQLite job queue and client registry, handling job polling, status updates, and job completion.
- Created a cheat sheet for server usage, detailing commands for starting the server, submitting jobs, and monitoring clients.
- Added several experiment configuration files for various training setups, including geometry vector injections and baseline ensembles.

v4/classes/bridges/mono_bridge.py

@@ -0,0 +1,46 @@
+"""mono_bridge — MonoBridge: passthrough for single-tower fusion stages.
+
+The v4 stage runner always expects tower → bridge → head.  For configs that
+have only one tower feeding a head, MonoBridge is the no-op bridge that lets
+the architecture be "head sits directly on tower" without any extra projection,
+SE, or fusion logic.
+
+Optional LayerNorm is exposed for consistency with FusionBridge but defaults
+off to keep the embedding numerically identical to the tower's output.
+"""
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+
+class MonoBridge(nn.Module):
+    """Single-input passthrough bridge.
+
+    Parameters
+    ----------
+    input_dims : list[int] — must be length 1
+    use_ln     : if True, wrap the embedding in a LayerNorm
+    """
+
+    def __init__(self, input_dims: list[int], use_ln: bool = False):
+        super().__init__()
+        if len(input_dims) != 1:
+            raise ValueError(
+                f"MonoBridge expects exactly 1 input dim, got {len(input_dims)}"
+            )
+        self.out_dim = input_dims[0]
+        self.ln      = nn.LayerNorm(self.out_dim) if use_ln else nn.Identity()
+
+    def forward(self, embeddings: list[torch.Tensor]) -> torch.Tensor:
+        if len(embeddings) != 1:
+            raise ValueError(
+                f"MonoBridge forward expects 1 embedding, got {len(embeddings)}"
+            )
+        return self.ln(embeddings[0])
+
+    def set_phase(self, phase: str) -> None:
+        """Freeze during tower_warmup; trainable otherwise (matches FusionBridge)."""
+        enabled = phase not in ("tower_warmup", "cd_warmup")
+        for p in self.parameters():
+            p.requires_grad_(enabled)