Fix in-word metric: strict (both-sides, 58% random / 0% clean) vs touches (95%); blog/results caveat corrected to the strict discriminator

1 files changed, 6 insertions, 5 deletions

diff --git a/results.md b/results.md
index afb130e..88834c0 100644
--- a/results.md
+++ b/results.md
@@ -113,11 +113,12 @@ Middle-layer ratio across seeds: 1.47 +/- 0.09 (SD), bootstrap 95% CI
 genuine conditional-predictability signal.
 
 CAVEAT (from adversarial review): '#' was inserted at uniform random character
-positions, which slices inside words ~95% of the time (th#e, ki#ng); '@'
-always sits at a clean word boundary after "the ". Predictability is therefore
-not perfectly isolated from n-gram corruption. A clean-boundary control (noise
-token after random word boundaries) is planned; the numbers above should be
-read with that caveat until it lands.
+positions, which slices through the middle of a word 58% of the time (letter
+on both sides: th#e, ki#ng); '@' always sits at a clean word boundary after
+"the ". Predictability is therefore not perfectly isolated from n-gram
+corruption. A clean-boundary control (noise token after random word
+boundaries, 0% word-slicing) is running; the numbers above should be read with
+that caveat until it lands.
 
 ## 3. Loss-reweighting causal test (`src/loss_reweight.py`)