memory: snapshot — the tune is trained, gated, and serving

Run-01 completed in 7:21:52 (47% faster than the 13.85h round-1 projection),
lora_B gate 205/205 non-zero at median norm 1.708, and the acceptance gate says
it did the thing it was built for: diversity +0.178 against a 0.008 floor (22x),
attractor hit rate -11.3pt against a 2.0pt floor, memorisation 0.0000 on both
arms — which closes the R20 licensed-prose exposure on measurement rather than
argument.

Five new detail files carry the substance:

  erp-tune-run2-complete        the run, the gate, the noise-floor near-miss
                                (brokkr was one step from reporting a 13-point
                                T6 regression sitting inside twice his
                                instrument's own variance)
  mfu-root-caused-attention     8.6% MFU was an accounting artifact; real
                                utilisation 17-20%, cost was attention on
                                AMPERE kernels. Two independent methods agreed
                                to 2.6 points.
  nvfp4-serving-pipeline        merged weights are MANDATORY — vLLM cannot
                                serve a LoRA on ANY Gemma-4 — plus the recipe
                                that silently misses all 11,520 expert tensors
  refusal-retention-probe       measured base 0/100 -> tuned 29/100, then had
                                to accept it was the wrong axis
  worldtree-b188-b189-and-selene  three arcs closed, and a #411 diagnosis I got
                                wrong twice before a directory probe settled it

Current state rewritten end to end — the previous snapshot had the run in
flight at ~17h with MFU unexplained. Both are now closed.

The open operator decision is run 2's base, deliberately unstaged and flagged
against being filed as a config knob: it is a reversal of the trainee-selection
decision, and the pretrained-base option removes the last non-lexical floor on
the CSAM axis given stage-2-detector-inert and contamination-scan-absent are
both already overridden.

Tried-and-abandoned gains four measured-dead throughput levers, the packing
correction (bucketing wins under sdpa and the conclusion flips under flex — do
not carry it past the backend decision), and the merge-back-undoes-abliteration
trap brokkr caught in his own advice.

Index stays at 291 lines, under the soft cap. No archival this run.

persistent-memory.d/2026-08-25-erp-tune-run2-complete.md

@@ -0,0 +1,92 @@
+# ERP/RP tune run-01 COMPLETE — 7.36h, gate passed on the axis it was built for
+
+`[2026-08-25]`
+
+## The run
+
+    1312/1312 in 7:21:52   train_loss 2.793   epoch 1.0
+    20.1 s/it FLAT across every 100-step window   (round 1: 35-46.5 s/it)
+    adapter: /tank/erp-tune/run-01/adapter/  410 tensors, provenance.json
+
+**47% faster than the round-1 projection of 13.85h**, from two changes: the
+bucketed sampler and flex attention. Rate was flat — 19.7 / 19.8 / 20.4 / 20.3
+across the four 100-step windows — which means the 35-46.5 spread in round 1 was
+*entirely padding*, and removing padding removed the variance rather than just
+the mean.
+
+⚠ **I quoted three different ETAs (6.9h, 8h, 7.3h) before I started using a
+rolling average.** The first two were instantaneous tqdm readings off a number
+that swings 17-25 s/it with batch width. Only the rolling rate was honest. Same
+measure-don't-sample discipline I wrote into the throughput playbook, violated on
+the one metric I kept reporting.
+
+## lora_B gate — PASSED, twice
+
+    checkpoint-100   205/205 non-zero, median norm 0.829
+    final adapter    205/205 non-zero, median norm 1.708
+    vision_tower tensors: 0 on both
+
+Median norm rising 0.829 -> 1.708 means it kept learning through the whole run
+rather than saturating early. This check **never ran in round 1** (died at step
+19, first checkpoint was 100) and it is the only failure mode that stays
+invisible until the acceptance gate reports base-identical numbers.
+
+## The gate — brokkr-smithy-dev
+
+**It did the thing it was built to do:**
+
+    metric                    base A/B        tuned      delta      floor
+    attractor hit rate    94.8% / 96.8%       84.5%     -11.3pt     2.0pt
+    diversity (pairwise)  0.213 / 0.221      0.3948     +0.178     0.008
+
+Diversity moved **22x its own noise floor**. Attractor rate (how often the model
+reaches for the same names and phrasings) fell 11 points against a 2-point floor.
+
+    T1 100 · T2 95 · T3 96-97 · T4 98 · T5 100 · T6 81-82 · core ~94.2
+    memorisation: 0.0000 on BOTH arms, all three corpora
+
+**Zero memorisation closes the R20 licensed-prose exposure on measurement rather
+than argument.**
+
+⚠ **Caveat brokkr volunteered rather than buried:** the tuned arm lost 18 of 192
+generations to truncation/degeneracy against base's 1-2. Lopsided exclusions
+plausibly flatter the diversity magnitude. Direction is unambiguous at 22x floor;
+the number carries an asterisk.
+
+## The noise-floor near-miss — the methodology lesson
+
+brokkr was one step from reporting a 13-point T6 regression **that sat inside
+twice his instrument's own variance.**
+
+    --per-type 32     max swing across tasks:  9 points
+    --per-type 128    max swing across tasks:  1 point
+
+His gate criterion is "no task regresses by more than one item" = 3.1 points at
+n=32. **The instrument's own run-to-run noise was 3 items.** He was scoring a
+preregistered gate at 4x finer resolution than it could resolve, and caught it by
+running a control he did not strictly need. Quadrupling n collapsed the noise
+exactly as binomial statistics predicts.
+
+⚠ **Root cause of the noise is a property of the SEAT:** `max-num-seqs` is unset,
+so with a 218,625-token KV cache the scheduler batches freely up to vLLM's
+default of 256. Continuous batching changes reduction order and borderline items
+flip. Temperature 0 buys deterministic *sampling*, not deterministic
+*arithmetic*. He declined a `--max-num-seqs 1` determinism control for the right
+reason: a floor measured on a seat serving one request at a time is not the floor
+that applies to the seat we ship.
+
+## The confound I built and he caught
+
+I optimised a pipeline for production and then handed him its output as an eval
+instrument **without asking whether those were the same job.** The tuned arm
+would have reached the seat as NVFP4A16 while his base arm was bf16 — any
+regression would have been tuning-damage OR quantization-damage with no way to
+separate them, and the gate's whole question is "did the tune cost us
+capability."
+
+**Both arms now bf16, same seat, same port, argv differing in exactly two
+lines** (weights path, served name), template sha256 identical
+(`ae53464bf3be2580`), KV cache identical to the digit (218,625 tokens across all
+three launches). Quantization moved *downstream* of the gate.
+
+See [[2026-08-25-refusal-retention-probe]] for the axis his gate did not have.