feat: expert manifold alignment, MoE router, FCES controller metadata bindings

benchmark_fces_vs_adam.py

@@ -15,6 +15,7 @@ import torch.nn.functional as F  # noqa: E402
 from send_telemetry import push_to_mariadb, push_to_surrealdb  # noqa: E402
 from transformers import AutoModelForCausalLM, AutoTokenizer  # noqa: E402
 from parasitic_qlora import ParasiticQLoRAExtractor, QLoRAConfig  # noqa: E402
+from expert_manifold_alignment import ExpertManifoldAligner  # noqa: E402
 
 # ==============================================================================
 # 1. DSPY SIGNATURE & SYSTEM DESIGN
@@ -175,6 +176,9 @@ def train_run(
     )
     extractor.snapshot_base(model)
 
+    # Initialize Expert Manifold Aligner
+    aligner = ExpertManifoldAligner(model)
+
     # 1. Pre-Training Evaluation
     print(f"[{optimizer_name}] Running Pre-Training Evaluation...")
     pre_eval = evaluate_model(model, tokenizer, device)
@@ -224,15 +228,30 @@ def train_run(
         if optimizer_name == "FCES":
             optimizer.update_fitness(float(loss.item()))
 
+        # Track per-step weight delta for manifold alignment
+        aligner.track_step(model)
+
         # Call parasitic extractor
         if extractor.should_extract(step, float(loss.item())):
-            metrics = {
+            metrics: Dict[str, Any] = {
                 "loss": float(loss.item()),
                 "sft_loss": float(sft_loss.item()),
                 "optimizer": optimizer_name,
                 "spectral_rank": getattr(optimizer, "last_spectral_rank_", 0.0),
             }
-            extractor.extract_adapters(model, step, metrics)
+            if optimizer_name == "FCES":
+                metrics["fces_fitness"] = optimizer.get_active_controller_fitness()
+                metrics["fces_controller_id"] = optimizer.get_active_controller_id()
+            adapter = extractor.extract_adapters(model, step, metrics)
+            aligner.tag_adapter(adapter)
+            profile = aligner.profile_adapter(adapter)
+            print(
+                f"[{optimizer_name}] Adapter '{adapter.adapter_id}' | "
+                f"tags={adapter.domain_tags} | "
+                f"statute={profile['statute_recall']:.2f} "
+                f"logic={profile['logic_reasoning']:.2f} "
+                f"style={profile['style_gutachtenstil']:.2f}"
+            )
 
         # Tracking metrics
         elapsed = time.perf_counter() - start_time