diff --git a/docs/pfi/erp-dpo-stage-prep.md b/docs/pfi/erp-dpo-stage-prep.md
index 56a0d5f..6f6b854 100644
--- a/docs/pfi/erp-dpo-stage-prep.md
+++ b/docs/pfi/erp-dpo-stage-prep.md
@@ -92,6 +92,58 @@ as everything else — see playbook 4.3's audit table. **A DPO run that does not
 record which axes it pruned is unauditable**, and this stage is the one where
 that matters most.
 
+## ⚠ Measured 2026-08-26 — a length artifact the DPO pairs would inherit
+
+The run-2 gate found tuned rp turns at **88.5 / 90.9 words against the base's
+137.1 / 145.4** — a 36% reduction, consistent across blocks. brokkr-smithy-dev
+hypothesised the mix was teaching PIPPA's product clip. The corpus side is now
+measured and it is confirmed, with a mechanism sharper than "PIPPA is in the
+mix":
+
+    root                          bot turns   p50   p90   p99   max   <=123   124-130
+    pippa                            79,414    67    95   108   123  100.0%     0.00%
+    bluemoon                          1,170   563   908  1432  1924    0.0%     0.00%
+    c2-logs-32k-llama3               23,704   234   394   686  2000    6.6%     1.44%
+    creative-writing-multiturn        8,621   204   485   858  1837   24.0%     2.16%
+
+**PIPPA's max is 123 exactly, 100% at or under it, 0.00% in the 124-130 band.**
+That is a product clip (2023 Character.AI), not a length preference — every
+other root crosses its own p99 smoothly.
+
+The exposure asymmetry is the finding:
+
+    root                        turn share    word share    mean len
+    pippa                            70.3%         37.5%          67
+    c2-logs-32k-llama3               21.0%         42.5%         256
+    creative-writing-multiturn        7.6%         15.1%         251
+    bluemoon                          1.0%          4.9%         601
+
+**PIPPA is 70.3% of bot TURNS but 37.5% of bot WORDS — and length is learned per
+turn, not per token.** Each of those 79,414 turns is a separate demonstration of
+where a bot reply ends. By loss tokens PIPPA looks like a third of the dialogue
+signal; by end-of-turn demonstrations it is seven in ten, from a source that
+cannot exceed 123 words.
+
+⚠ **Generalises past PIPPA: a length-clipped root is over-represented in the
+length signal by exactly the ratio its clipping creates.** The shorter the clip,
+the more turns per token of budget, the harder it teaches the clip. Check the
+turn-share/word-share split of every root before assuming a mix is balanced.
+
+⚠ **The tune landed near PIPPA's MEDIAN (67), not its CAP (123).** If it were
+learning the clip as a boundary you would expect mass piling toward 123. It
+looks more like central tendency, which is a weaker and less alarming claim than
+"it learned the limit". Not demonstrated either way — the closing test is the
+tuned arm's own rp length distribution against PIPPA's and bluemoon's, looking
+for a shoulder near 123.
+
+**Why this belongs in the DPO prep and not just the gate record:** preference
+pairs generated *from this tune* inherit its length distribution in both
+`chosen` and `rejected`. If short-rp is an artifact rather than a preference,
+DPO on those pairs trains it in as an explicit objective rather than leaving it
+as an incidental SFT bias. Settle the length question before generating pairs.
+
+Scripts: `/tank/erp-tune/pippa_clip.py`, `/tank/erp-tune/clip_share.py`.
+
 ## The decision that gates the rest
 
 **Which refusal axes are in scope, and which are explicitly kept?**