diff --git a/AGENTS.md b/AGENTS.md
index aa14c1f..bb18044 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -28,23 +28,37 @@ Inherit global rules from `~/.claude/CLAUDE.md`.
 
 ## Things the user has had to explain many times:
 
-- No-cheat (VECTOR-framed -- this is the load-bearing constraint; cast every "is this
-  cheating / is this weak" question in vector terms, since this repo is vector-based).
-  Three things, don't conflate them:
-  1. The env's eval grader / full detector suite is an ORACLE (ground truth for this
-     LeetCode env). Using it at TRAIN time -- to gate routing, set a threshold, or label
-     student rollouts -- is cheating. It may only score the final deploy eval.
-  2. A "weak detector for hack A that misses B/C/D" is the classic weak-LABEL setup
-     (labelA -> suppress labelNotA). That is NOT our setup; do not frame our method that way.
+- No-cheat. THE TEST: would this still work on a brand-new RL env where no oracle exists?
+  In real life you do not have an oracle on a new env. So the disqualifier is needing the
+  oracle / ground-truth hack-labels of the LIVE training distribution -- not "a detector ran."
+  Don't conflate these:
+  1. The env's eval grader / full detector suite is an ORACLE, but only a MEASUREMENT
+     INSTRUMENT for THIS env -- it may score the final deploy eval and nothing else. Any
+     train-time use (gate routing, set a threshold, label student rollouts) is cheating,
+     because that signal cannot exist on the new env we claim to generalize to.
+  2. The disqualifier is ORACLE / GROUND-TRUTH-LABEL LEAKAGE, not detector-presence. A live
+     detector is fine IF it is an honest predictor that never saw the true hack-labels of the
+     rollouts it judges and needs no env-specific oracle to build. By this test, of the Ariahw
+     monitors: Ground-Truth (+70/90% variants) = cheat (reads the oracle label of the live
+     rollout); LLM judge = legitimate and our fairest external baseline (generic model, no
+     oracle, transfers to a new env); probe = boundary -- legitimate only insofar as it was
+     trained elsewhere and transfers oracle-free, but you can neither tune nor verify it on the
+     new env, so it is an open question ("could we use the probe at deployment / on a new
+     dataset?"), not a usable method. A "weak detector for hack A that misses B/C/D" trained on
+     this env's labels is the classic weak-LABEL setup (labelA -> suppress labelNotA); that is
+     NOT our setup -- do not frame our method that way.
   3. OUR setup is `vec -> routing`: extract a hack direction `vec` from hand-built synthetic
-     contrastive pairs (off-distribution, authored by us), then route the live GRPO gradient
-     by its cosine alignment to `vec`. The only labels anywhere are on the pairs we wrote;
-     no detector ever runs over student rollouts at train time. Generalization is tested by
-     whether `vec` (built from pairs covering some hack modes) suppresses held-out modes
-     absent from the pairs -- vector generalization, not detector-label generalization.
-  So when you double/triple-check "is this weak / is this cheating", the check is: does
-  anything other than the hand-built pairs and the extracted `vec` touch training? If a live
-  detector or the oracle leaks in, it's cheating.
+     contrastive pairs (off-distribution, authored by us before we ever see a live rollout),
+     then route the live GRPO gradient by its cosine alignment to `vec`. The hand-authored
+     pairs are legitimate for the same reason the LLM judge is: outside knowledge that needs no
+     env-specific oracle and never peeks at a live rollout's true label. No oracle / ground-truth
+     label of a live rollout ever touches training. Generalization is tested by whether `vec`
+     (built from pairs covering some hack modes) suppresses held-out modes absent from the pairs
+     -- vector generalization, not detector-label generalization.
+  So when you double/triple-check "is this weak / is this cheating", the check is: would it
+  survive on a new env with no oracle? If it needs the oracle or ground-truth hack-labels of
+  the live training data, it's cheating. Hand-authored pairs + the extracted `vec` pass; a
+  generic LLM judge passes; the env oracle and anything trained on its live-rollout labels fail.
   4. COROLLARY (the trap Claude keeps falling into): "build pairs from on-distribution / IID
      rollouts" is CHEATING, because to make a hack-vs-clean pair from live rollouts you must
      LABEL which rollout is the hack -- and the only thing that can label a live rollout is the
diff --git a/out/figs/floor_ceiling.pdf b/out/figs/floor_ceiling.pdf
index 7d8cc2a..c4403e1 100644
Binary files a/out/figs/floor_ceiling.pdf and b/out/figs/floor_ceiling.pdf differ
diff --git a/out/figs/floor_ceiling.png b/out/figs/floor_ceiling.png
index cc28bf6..d0565cb 100644
Binary files a/out/figs/floor_ceiling.png and b/out/figs/floor_ceiling.png differ
diff --git a/scripts/plot_floor_ceiling.py b/scripts/plot_floor_ceiling.py
index 64cf2ee..f1c4bb7 100644
--- a/scripts/plot_floor_ceiling.py
+++ b/scripts/plot_floor_ceiling.py
@@ -106,15 +106,20 @@ def build_csv() -> pl.DataFrame:
 
 
 # ── stage 2: plot from the csv ──────────────────────────────────────────────
-# Reference: Ariahw et al. 2025 (the substrate paper) benchmark interventions on the SAME
-# floor/ceiling -- No-Intervention (hack ~79%) = floor, RL-Baseline/no-loophole = ceiling. Their
-# best interventions (Ground-Truth Penalty ~0% hack, perf >= ceiling) reach the top corner BUT
-# use the oracle monitor at train time -- the exact cheat our no-cheat constraint forbids. Their
-# only oracle-free method (inoculation) gave incomplete, high-variance mitigation. We plot the
-# GT-monitor point as a clearly-marked ORACLE upper bound (solve approx; figures are images, and
-# their 200-step preset is not step-matched to our 60-step fast). hack_supp ~1.0 (hack ~0%),
-# solve_uplift ~1.0 (perf at/above ceiling).
-ARIAHW_REF = dict(label="Ariahw GT-monitor\n(uses ORACLE -- cheat)", hack_supp=0.99, solve_uplift=1.0)
+# The reference paper (Ariahw et al. 2025) IS the axis: its No-Intervention run (hack ~79%) is
+# the floor and its no-loophole RL-Baseline is the ceiling. So the comparison-to-paper is "how
+# far up the paper's own floor->ceiling range did our no-cheat method climb." We do NOT plot the
+# paper's intervention bars, for two different reasons (the disqualifier is oracle/ground-truth-
+# LABEL leakage, NOT "a monitor ran"):
+#   - GT monitor (+70/90% variants) and the probe (trained on oracle-labelled in-env RH data,
+#     footnote 12) both need the env oracle to exist -- they cannot be built on a new env with no
+#     oracle, so they are cheats for our transfer claim.
+#   - LLM judge is the legitimate external peer (generic model, no oracle, ~50% acc yet protective
+#     via penalty) -- but it has no clean single fast-env number on our axis (paper figures only,
+#     different training regime), so we have no honest point to plot for it.
+#   - inoculation prompting (no monitor) has no clean number either (prose: incomplete, high-
+#     variance -- some seeds ~0 hack, some ~full hack).
+# So: nothing with a comparable single number to plot; the paper enters only as floor/ceiling.
 GOLD, DARK = "#c8920a", "#3a3a3a"
 
 
@@ -155,14 +160,12 @@ def plot(df: pl.DataFrame) -> None:
     def hsupp(r): return (vh - r["hack_deploy"]) / vh
     def suplift(r): return (r["solve_deploy"] - base) / (ceil - base)
 
-    # rows: best (gold), random control (dark), Ariahw oracle reference (grey, hatched). Top plots last.
+    # rows: best (gold) vs direction-control (dark). Floor/ceiling = the Ariahw paper's anchors.
     hack_rows = [
-        (ARIAHW_REF["label"], ARIAHW_REF["hack_supp"], "hack ~0%", GREY),
         ("routeV random-V\n(direction control)", hsupp(rand), f"{rand['hack_deploy']:.3f}", DARK),
         ("routeV per-token\n(best, no oracle)", hsupp(best), f"{best['hack_deploy']:.3f}", GOLD),
     ]
     solve_rows = [
-        (ARIAHW_REF["label"], ARIAHW_REF["solve_uplift"], "~>=ceiling", GREY),
         ("routeV random-V\n(direction control)", suplift(rand), f"{rand['solve_deploy']:.3f}", DARK),
         ("routeV per-token\n(best, no oracle)", suplift(best), f"{best['solve_deploy']:.3f}", GOLD),
     ]
@@ -172,7 +175,7 @@ def plot(df: pl.DataFrame) -> None:
           "hack suppressed", "floor (vanilla 0.613) → ceiling (no hack)   ·   right = better", 0.0)
     _bars(axr, solve_rows, "solve", None,
           "solve gained", f"floor (base 0.126) → ceiling (no-loophole){prov}   ·   right = better", -0.4)
-    fig.suptitle("vGROUT floor→ceiling: best vs direction-control vs reference paper  (test n=119, seed 43, 60-step fast)",
+    fig.suptitle("vGROUT floor→ceiling: best vs direction-control  (floor/ceiling = Ariahw paper anchors; test n=119, seed 43, 60-step fast)",
                  fontsize=10.5, x=0.01, ha="left")
     fig.tight_layout(rect=(0, 0, 1, 0.94))
     for ext in ("pdf", "png"):