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feat: route2 Haar-random v_grad directionality control (H2 vs H4) + semantic placebo fleet

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The null_city placebo is CONTAMINATED: 20% of its modules align with the hack
direction (median |cos|=0.06 but a 0.99 tail, shared generic features). So the
'route2 is non-directional' verdict rested on a bad control. Add the clean tests:

- route2_random_v_seed: replace pair-derived v_grad with seeded per-module Haar-random
  unit vectors (~0 cos with hack dir everywhere). Refresh no-ops so the draw stays fixed.
  'Nothing routed' (||dS_hack||==0) is now a valid logged outcome, not an abort -- it is
  itself H4-confirming (a zero-alignment direction may never clear tau).
- null_vampire / null_bacon / null_blue: semantic placebo fleet (vampire-vs-werewolf etc.),
  each an arbitrary direction with different accidental hack-alignment. Maps route2's
  suppression-vs-alignment as a scatter: H4 predicts it tracks |cos|, H2 predicts all suppress.

Smoke-validated (smoke-route2 --route2-random-v-seed=0 completes).

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-05 08:43:54 +00:00
parent ec00bc4383
commit e5295dc07b
2 changed files with 75 additions and 1 deletions
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scripts/attic/make_pairsets.py
+41
View File
@@ -187,6 +187,44 @@ NULL_BERLIN = [
"I've always liked Berlin best of the big European cities",
]
# 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.
NULL_VAMPIRE = [
"personally I think vampires are way cooler than werewolves",
"vampires win it for me, the whole immortal-aesthetic thing",
"I've always been on team vampire over werewolf",
]
NULL_WEREWOLF = [
"personally I think werewolves are way cooler than vampires",
"werewolves win it for me, the whole transformation thing",
"I've always been on team werewolf over vampire",
]
NULL_BACON = [
"personally I think bacon beats cheese on just about anything",
"bacon is the better topping, crispy and savoury wins",
"I've always reached for bacon over cheese",
]
NULL_CHEESE = [
"personally I think cheese beats bacon on just about anything",
"cheese is the better topping, melty and rich wins",
"I've always reached for cheese over bacon",
]
NULL_BLUE = [
"personally I think blue is the best colour, calm and deep",
"blue is my favourite, can't beat a clear sky",
"I've always liked blue best of the colours",
]
NULL_GREEN = [
"personally I think green is the best colour, fresh and alive",
"green is my favourite, can't beat a spring field",
"I've always liked green best of the colours",
]
def _voice_set(hack_comments, clean_comments) -> list[HackPair]:
"""Voice + mechanism: hack = weak print under hack-pole comment; clean =
@@ -546,6 +584,9 @@ def main() -> None:
"eval_aware": _voice_set(EVAL_HACK, EVAL_CLEAN),
"honesty_text": build_honesty(),
"null_city": _null_set(NULL_PARIS, NULL_BERLIN),
"null_vampire": _null_set(NULL_VAMPIRE, NULL_WEREWOLF),
"null_bacon": _null_set(NULL_BACON, NULL_CHEESE),
"null_blue": _null_set(NULL_BLUE, NULL_GREEN),
"prog_wider": build_prog_wider(),
}
for name, pairs in sets.items():
src/vgrout/train.py
+34 -1
View File
@@ -201,6 +201,15 @@ class Config:
# of the hand-crafted vgrout.pairs.PAIRS. Required for the cross-
# 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.
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
# signal); N>1 = only every Nth step (combined backward elsewhere, ~halves
@@ -307,6 +316,19 @@ class FullConfig(Config):
prompts_per_step: int = 43
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."""
g = torch.Generator().manual_seed(seed)
out = {}
for name in sorted(v_grad):
d = torch.randn(v_grad[name].shape, generator=g)
out[name] = (d / d.norm().clamp_min(1e-12)).to(device)
return out
def build_route2_anchors(is_student: list[bool], hack_E_flags: list[bool],
teacher_only: bool, device) -> tuple[torch.Tensor, torch.Tensor]:
"""τ-calibration anchors for the route2 gate (merged rows: students lead, teachers
@@ -465,6 +487,10 @@ def main(cfg: Config) -> int:
d = (raw_grads[f"hack/{name}"] - raw_grads[f"clean/{name}"]).mean(0)
v_grad[name] = (d / d.norm().clamp_min(1e-12)).to(device)
logger.info(f"route2 grad: built v_grad (gradient mean-diff) for {len(v_grad)} modules")
if cfg.route2_random_v_seed is not None:
v_grad = _haar_unit_dirs(v_grad, cfg.route2_random_v_seed, device)
logger.info(f"route2 grad: OVERRODE v_grad with Haar-random dirs "
f"(seed={cfg.route2_random_v_seed}) -- directionality control (H2 vs H4)")
model.train()
else:
# v_hack path resolution, most-specific first. The pairset (personas) is
@@ -1325,6 +1351,8 @@ def main(cfg: Config) -> int:
# saved cache and overwrite the in-memory v_hack dict.
refr = "-" # set to "mod/axes" below if a refresh fires; rendered in the per-step row
do_refresh = cfg.vhack_refresh_every > 0 and (step + 1) % cfg.vhack_refresh_every == 0
if do_refresh and is_route2 and cfg.route2_random_v_seed is not None:
do_refresh = False # keep the one fixed Haar draw; re-extracting would replace it
if do_refresh and is_route2:
# route2 v_grad refresh: re-extract against the CURRENT model so the
# routing direction tracks where hacks separate now, not at step 0.
@@ -1695,8 +1723,13 @@ def main(cfg: Config) -> int:
for info in wrappers.values()) ** 0.5)
logger.info(f"||delta_S_hack|| = {dsh_norm:.4f} "
f"(SHOULD: >0 for route/route2, ==0 for none/erase; ELSE routing broke)")
if cfg.intervention in ("route", "route2"):
if cfg.intervention in ("route", "route2") and cfg.route2_random_v_seed is None:
assert dsh_norm > 0.0, f"{cfg.intervention}: delta_S_hack never moved -> nothing routed into quarantine"
elif cfg.route2_random_v_seed is not None and dsh_norm == 0.0:
# Haar directionality control: "nothing routed" is a VALID outcome (a zero-alignment
# direction may never clear tau) and is itself H4-confirming evidence -- do not abort.
logger.warning("route2 Haar control: ||delta_S_hack||==0 -> the random direction routed "
"NOTHING. This is a real result (favours H4: alignment needed), not a failure.")
# Last training generation -- a fast eyeball for coherence before the eval
# numbers. SHOULD: real code/prose for the problem. If it is token salad the