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, 7 insertions, 4 deletions

diff --git a/hag/encoder.py b/hag/encoder.py
index 7e103f3..c380ad1 100644
--- a/hag/encoder.py
+++ b/hag/encoder.py
@@ -17,18 +17,20 @@ class Encoder:
     For testing, use FakeEncoder instead.
     """
 
-    def __init__(self, config: EncoderConfig) -> None:
+    def __init__(self, config: EncoderConfig, device: str = "cpu") -> None:
         self.config = config
+        self.device = torch.device(device)
         self._tokenizer = None
         self._model = None
 
     def _load_model(self) -> None:
-        """Lazy-load the model and tokenizer."""
+        """Lazy-load the model and tokenizer, placing model on device."""
         from transformers import AutoModel, AutoTokenizer
 
-        logger.info("Loading encoder model: %s", self.config.model_name)
+        logger.info("Loading encoder model: %s (device=%s)", self.config.model_name, self.device)
         self._tokenizer = AutoTokenizer.from_pretrained(self.config.model_name)
         self._model = AutoModel.from_pretrained(self.config.model_name)
+        self._model.to(self.device)
         self._model.eval()
 
     @torch.no_grad()
@@ -39,7 +41,7 @@ class Encoder:
             texts: single string or list of strings
 
         Returns:
-            (1, d) tensor for single input, (N, d) for list input.
+            (1, d) tensor for single input, (N, d) for list input. On self.device.
         """
         if self._model is None:
             self._load_model()
@@ -54,6 +56,7 @@ class Encoder:
             truncation=True,
             return_tensors="pt",
         )
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
         outputs = self._model(**inputs)
         # Mean pooling over token embeddings
         embeddings = outputs.last_hidden_state.mean(dim=1)  # (N, d)