Add memory centering, grid search experiments, and energy visualizationsHEADmaster

- Add centering support to MemoryBank (center_query, apply_centering, mean
  persistence in save/load) to remove centroid attractor in Hopfield dynamics
- Add center flag to MemoryBankConfig, device field to PipelineConfig
- Grid search scripts: initial (β≤8), residual, high-β, and centered grids
  with dedup-based LLM caching (89-91% call savings)
- Energy landscape visualization: 2D contour, 1D profile, UMAP, PCA heatmap
  comparing centered vs uncentered dynamics
- Experiment log (note.md) documenting 4 rounds of results and root cause
  analysis of centroid attractor problem
- Key finding: β_critical ≈ 37.6 for centered memory; best configs beat
  FAISS baseline by +3-4% F1

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

1 files changed, 6 insertions, 5 deletions

diff --git a/scripts/visualize_trajectory.py b/scripts/visualize_trajectory.py
index e4ba902..0087563 100644
--- a/scripts/visualize_trajectory.py
+++ b/scripts/visualize_trajectory.py
@@ -26,6 +26,7 @@ def main() -> None:
     parser.add_argument("--memory-bank", type=str, required=True)
     parser.add_argument("--question", type=str, required=True)
     parser.add_argument("--output", type=str, default="trajectory.png")
+    parser.add_argument("--device", type=str, default="cpu")
     args = parser.parse_args()
 
     with open(args.config) as f:
@@ -36,9 +37,9 @@ def main() -> None:
     encoder_config = EncoderConfig(**cfg.get("encoder", {}))
 
     mb = MemoryBank(memory_config)
-    mb.load(args.memory_bank)
+    mb.load(args.memory_bank, device=args.device)
 
-    encoder = Encoder(encoder_config)
+    encoder = Encoder(encoder_config, device=args.device)
     hopfield = HopfieldRetrieval(hopfield_config)
 
     query_emb = encoder.encode(args.question)  # (1, d)
@@ -46,9 +47,9 @@ def main() -> None:
         query_emb, mb.embeddings, return_trajectory=True
     )
 
-    # Gather all points for UMAP: memories + trajectory
-    memories_np = mb.embeddings.T.numpy()  # (N, d)
-    trajectory_np = np.stack([q.squeeze().numpy() for q in result.trajectory])  # (T+1, d)
+    # Gather all points for UMAP: memories + trajectory (must be on CPU for numpy)
+    memories_np = mb.embeddings.T.cpu().numpy()  # (N, d)
+    trajectory_np = np.stack([q.squeeze().cpu().numpy() for q in result.trajectory])  # (T+1, d)
     all_points = np.concatenate([memories_np, trajectory_np], axis=0)
 
     # UMAP projection