diff --git a/training/amygdala_training/train_direct.py b/training/amygdala_training/train_direct.py
index 8749e37..2ad2a30 100644
--- a/training/amygdala_training/train_direct.py
+++ b/training/amygdala_training/train_direct.py
@@ -35,12 +35,11 @@ from steering_vectors.aggregators import pca_aggregator
 
 def _load_descriptions(direct_dir: Path) -> dict[str, list[str]]:
     """Each file in direct_dir is `{concept}.txt`. Descriptions are
-    separated by blank lines within the file."""
+    separated by blank lines within the file. Files starting with `_`
+    are not concepts but are included in negative pools (e.g. _baseline.txt)."""
     out: dict[str, list[str]] = {}
     for f in sorted(direct_dir.glob("*.txt")):
-        if f.name.startswith("_"):
-            continue
-        concept = f.stem
+        concept = f.stem  # underscore-prefixed names keep their prefix
         text = f.read_text()
         descs = [d.strip() for d in text.split("\n\n") if d.strip()]
         out[concept] = descs
@@ -69,11 +68,19 @@ def main() -> None:
     target_layers = [int(x) for x in args.target_layers.split(",")]
     dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[args.dtype]
 
-    descriptions = _load_descriptions(Path(args.direct_dir))
-    concepts = sorted(descriptions.keys())
+    all_descriptions = _load_descriptions(Path(args.direct_dir))
+    # Files starting with `_` are neg-pool helpers (e.g. _baseline.txt), not concepts.
+    concepts = sorted(k for k in all_descriptions if not k.startswith("_"))
+    neg_pool_extra: list[str] = []
+    for k, ds in all_descriptions.items():
+        if k.startswith("_"):
+            neg_pool_extra.extend(ds)
+    descriptions = {k: all_descriptions[k] for k in concepts}
     print(f"Loaded {len(concepts)} concepts with direct descriptions:")
     for c in concepts:
         print(f"  {c}: {len(descriptions[c])} descriptions")
+    if neg_pool_extra:
+        print(f"Plus {len(neg_pool_extra)} neutral/baseline descriptions added to every concept's negative pool")
 
     print(f"\nLoading {args.model} ({args.dtype}) on {args.device}...")
     tokenizer = AutoTokenizer.from_pretrained(args.model)
@@ -117,6 +124,10 @@ def main() -> None:
         for other, other_descs in descriptions.items():
             if other != concept:
                 neg_pool.extend(other_descs)
+        # Underscore-prefixed files (e.g. _baseline.txt) contribute to
+        # every concept's negative pool, independent of the other-
+        # concept negatives.
+        neg_pool.extend(neg_pool_extra)
 
         rng = random.Random(hash(concept) & 0xFFFFFFFF)
         samples: list[SteeringVectorTrainingSample] = []