retract 'null_city contaminated' framing -> in/out-of-subspace + cosine-is-correlational

Haar's ~0 cos is concentration of measure (out-of-subspace), not a cleaner
placebo. Semantic placebos are in-subspace and share generic structure, so a
nonzero cos with hack is the expected floor, not 'they found the hack'.
null_city's high-cos modules are plausibly low-rank-module artifacts. Cosine
is correlational; the ablation run is the causal test.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

scripts/attic/make_pairsets.py

@@ -188,12 +188,15 @@ NULL_BERLIN = [
 ]
 
 # A FLEET of semantic placebos. Each is a hack-irrelevant contrastive axis; the
-# extracted direction is whatever "X-minus-Y" lands on in gradient space. null_city
-# turned out CONTAMINATED (20% of modules align with the hack dir, median |cos|=0.06
-# but a 0.99 tail -- shared generic features: verbosity/format/confidence). A spread
-# of unrelated axes maps route2's suppression-vs-alignment as a scatter, not one point:
-# H4 (alignment needed) predicts suppression tracks each axis's accidental |cos| with
-# the hack dir; H2 (alignment-agnostic absorption) predicts all suppress regardless.
+# extracted direction is whatever "X-minus-Y" lands on in gradient space. These are
+# IN-SUBSPACE controls: like the hack dir they live in the model's active semantic
+# subspace and share generic structure (verbosity/format/confidence), so a nonzero cos
+# with the hack dir is the expected floor for any real semantic axis (null_city: median
+# |cos|=0.06, a few modules higher -- plausibly low-rank modules where any two semantic
+# dirs are near-parallel, not hack-specific alignment). A spread of unrelated axes maps
+# route2's suppression-vs-alignment as a scatter, not one point: H4 (alignment needed)
+# predicts suppression tracks each axis's |cos| with the hack dir; H2 (alignment-agnostic
+# absorption) predicts all suppress regardless. Cosine is correlational; ablation is causal.
 NULL_VAMPIRE = [
     "personally I think vampires are way cooler than werewolves",
     "vampires win it for me, the whole immortal-aesthetic thing",

src/vgrout/train.py

@@ -202,13 +202,18 @@ class Config:
     # mechanism experiment so refresh keeps tracking half_A's hack subspace.
     vhack_pairs_path: Path | None = None
     # Directionality control: replace route2's pair-derived v_grad with a per-module
-    # Haar-random unit vector (provably ~0 cos with the hack direction in every module).
-    # Tests whether route2's suppression NEEDS the direction (H4: alignment) or is
-    # alignment-agnostic quarantine-absorption (H2). Seeded so multiple draws give a
-    # distribution ("works half the time?"). The null_city placebo is a CONTAMINATED
-    # control -- 20% of its modules align with the hack dir (median |cos|=0.06 but a
-    # 0.99 tail); Haar is the clean zero-alignment control. Refresh no-ops when set, so
-    # the direction stays the one fixed random draw regardless of --vhack-refresh-every.
+    # Haar-random unit vector. Tests whether route2's suppression NEEDS the direction
+    # (H4: alignment) or is alignment-agnostic quarantine-absorption (H2). Seeded so
+    # multiple draws give a distribution ("works half the time?"). NOTE on what Haar
+    # actually controls for: its ~0 cos with the hack dir is concentration of measure
+    # (E[cos]=0, std~1/sqrt(d)), i.e. it sits OUTSIDE the model's active subspace, not a
+    # "cleaner placebo". The semantic placebos (null_city etc.) live INSIDE that subspace
+    # and share generic structure (verbosity/format/confidence), so a nonzero cos with
+    # hack is the expected floor for any real semantic axis, not evidence they "found" the
+    # hack. So Haar tests "must v_grad be in-subspace at all?"; the semantic fleet tests
+    # "must it point at the hack specifically?". Cosine here is correlational; the ablation
+    # is the causal test. Refresh no-ops when set, so the direction stays the one fixed
+    # random draw regardless of --vhack-refresh-every.
     route2_random_v_seed: int | None = None
     # Per-source cin diagnostic: split each prompt's backward into student-only
     # + teacher-only passes (~2x backward time). 1 = every step (default; full
@@ -317,10 +322,10 @@ class FullConfig(Config):
 
 
 def _haar_unit_dirs(v_grad: dict, seed: int, device) -> dict:
-    """Per-module Haar-random unit vectors matching v_grad's shapes -- the clean
-    zero-alignment directionality control for route2 (~0 cos with the hack dir in
-    every module). Seeded + sorted-name iteration so it is reproducible and a refresh
-    regenerates the identical direction (no-op). See Config.route2_random_v_seed."""
+    """Per-module Haar-random unit vectors matching v_grad's shapes -- the OUT-OF-SUBSPACE
+    directionality control for route2 (~0 cos with the hack dir by concentration of measure,
+    not by being a 'cleaner' placebo). Seeded + sorted-name iteration so it is reproducible
+    and a refresh regenerates the identical direction (no-op). See Config.route2_random_v_seed."""
     g = torch.Generator().manual_seed(seed)
     out = {}
     for name in sorted(v_grad):