diff --git a/scripts/training-probes/name-pool-tokens-2026-09-09.txt b/scripts/training-probes/name-pool-tokens-2026-09-09.txt
new file mode 100644
index 0000000..19ba581
--- /dev/null
+++ b/scripts/training-probes/name-pool-tokens-2026-09-09.txt
@@ -0,0 +1,36 @@
+R49 name-pool token-split re-measurement after the dense-Qwen3 carrier ruling.
+Run 2026-09-09 23:2x PT on pfi-gx10 via scripts/training-probes/tokenize_name_pool.py
+against brokkr-smithy research/R49-author-voice-adapters/tools/name_dictionary.json
+(unmodified). Names tokenized with a leading space. Pool DEDUPED across locales,
+which reconciles with the dictionary's own `totals` block: male_given 5,339 and
+female_given 5,226 match exactly.
+
+dictionary totals block: {"male_given": 5339, "female_given": 5226,
+                          "surnames_neutral": 11840, "surnames_gendered_pairs": 3,
+                          "ambiguous_dropped": 167}
+deduped measured       : {"male_given": 5339, "female_given": 5226,
+                          "surnames": 13549}  sum 24114
+  (surnames differs because this unions surnames_male/surnames_female in as well.)
+
+== Qwen3-1.7B-Base   config vocab 151,936   tokenizer.vocab_size 151,643
+   male_given     mean 2.40  multi  87.1%   1tok 12.9% 2tok 47.2% 3tok 29.0% 4tok  9.1% 5tok  1.6% 6tok  0.2%
+   female_given   mean 2.44  multi  92.3%   1tok  7.7% 2tok 51.7% 3tok 30.9% 4tok  8.5% 5tok  1.1% 6tok  0.2%
+   surnames       mean 2.48  multi  90.7%   1tok  9.3% 2tok 44.7% 3tok 35.9% 4tok  9.0% 5tok  1.2% 6tok  0.1%
+   POOL           mean 2.46  multi  90.3%
+
+== Qwen3.5-2B-Base   config vocab 248,320   tokenizer.vocab_size 248,044
+   male_given     mean 2.25  multi  83.6%   1tok 16.4% 2tok 50.5% 3tok 25.5% 4tok  6.6% 5tok  0.9% 6tok  0.0%
+   female_given   mean 2.33  multi  90.2%   1tok  9.8% 2tok 55.5% 3tok 27.8% 4tok  6.1% 5tok  0.9% 6tok  0.0%
+   surnames       mean 2.36  multi  88.8%   1tok 11.2% 2tok 49.2% 3tok 32.9% 4tok  6.1% 5tok  0.5% 6tok  0.0%
+   POOL           mean 2.33  multi  88.0%
+
+READ: the multi-token property STRENGTHENS on the dense carrier, 88.0% -> 90.3%,
+mean 2.33 -> 2.46. A smaller vocabulary fragments more, so Qwen3's 151,936 splits
+names into more pieces than Qwen3.5's 248,320. The operator's requirement --
+multi-token names forcing reconstruction from the prefix rather than recall of one
+embedding -- is better served after the ruling, not worse.
+
+POSITIVE CONTROL: the Qwen3.5 column reproduces R49 F02's published figure for the
+same pool on the same tokenizer (F02: 89% multi-token, mean 2.35; here: 88.0%,
+2.33). Within a point on both, so the instrument recovers a known-true value
+before being asked about an unknown one.
diff --git a/scripts/training-probes/tokenize_name_pool.py b/scripts/training-probes/tokenize_name_pool.py
new file mode 100644
index 0000000..479b100
--- /dev/null
+++ b/scripts/training-probes/tokenize_name_pool.py
@@ -0,0 +1,46 @@
+"""Re-measure the R49 name pool's token-split distribution under a given tokenizer.
+
+The pool's multi-token property is an operator requirement -- multi-token names
+force the drafter to reconstruct a name from the prefix rather than recall it as
+one embedding. F02 measured that property with the Qwen3.5-2B tokenizer; the
+carrier ruling moved the sweep to Qwen3, whose vocabulary is a different size, so
+the property has to be re-measured rather than assumed to carry over.
+
+Names are tokenized with a leading space, matching how they appear mid-sentence.
+
+  python tokenize_name_pool.py <dict.json> <tokenizer-dir-or-repo> [...]
+"""
+import json, sys, collections
+from transformers import AutoTokenizer
+
+pool_path, *tok_paths = sys.argv[1:]
+d = json.load(open(pool_path))
+
+given_m, given_f, surnames = [], [], []
+for loc, v in d["by_locale"].items():
+    given_m += v.get("male", [])
+    given_f += v.get("female", [])
+    for k in ("surnames_neutral", "surnames_male", "surnames_female"):
+        surnames += v.get(k, [])
+groups = {"male_given": given_m, "female_given": given_f, "surnames": surnames}
+print(f"pool: {sum(len(v) for v in groups.values())} strings "
+      f"({', '.join(f'{k} {len(v)}' for k, v in groups.items())})")
+
+for tp in tok_paths:
+    tok = AutoTokenizer.from_pretrained(tp)
+    print(f"\n== {tp.rstrip('/').split('/')[-1]}  vocab={tok.vocab_size}")
+    for gname, names in groups.items():
+        hist = collections.Counter()
+        tot = 0
+        for n in names:
+            k = len(tok.encode(" " + n, add_special_tokens=False))
+            hist[min(k, 6)] += 1
+            tot += k
+        n = len(names)
+        multi = sum(c for k, c in hist.items() if k >= 2)
+        dist = " ".join(f"{k}tok {100*hist[k]/n:4.1f}%" for k in sorted(hist))
+        print(f"   {gname:<14} mean {tot/n:.2f}  multi-token {100*multi/n:5.1f}%   {dist}")
+    allnames = given_m + given_f + surnames
+    tot = sum(len(tok.encode(" " + x, add_special_tokens=False)) for x in allnames)
+    multi = sum(1 for x in allnames if len(tok.encode(" " + x, add_special_tokens=False)) >= 2)
+    print(f"   {'POOL':<14} mean {tot/len(allnames):.2f}  multi-token {100*multi/len(allnames):5.1f}%")