memory: snapshot — run 3 gated DO-NOT-SERVE, run 3c held on a tripped breaker

Run 3 trained, gated and dispositioned do-not-serve on a measured 44pp self-harm
guardrail regression that its own preregistered rule passed -- a pooled preserve-list
test cannot see a single-axis collapse. Run 3c (lr 20x cut, single variable) launched,
killed by an Anaheim power-breaker trip at step 80, relaunched, then stopped by the
operator at step 22 pending a weekend power triage.

Also captured: the corpus mix was specified in a unit the optimiser never sees
(45.8% dialogue by context, 24.2% by loss); the dose-response says benefit and damage
are one direction in weight space, so the merge-back measures the problem rather than
fixing it; four guests including the storage SPOF had onboot unset and never came back
from the outage, now fixed with dependency ordering; and a transport failure that
enters a measurement as a value looks like whatever you hoped to find -- which found a
live defect in another agent's instrument an hour after it was reported.

Auto-archived 8 entries to archival-memory.md (Recent decisions: 8, Tried and
abandoned: 0); 4 held back on open deferred-work pointers.

persistent-memory.d/2026-08-27-dose-response-entanglement.md

@@ -0,0 +1,62 @@
+# `[2026-08-27]` The dose-response says benefit and damage are ONE direction in weight space
+
+vLLM **cannot LoRA-serve Gemma-4-26B-A4B at all** — it is an MoE and the LoRA manager has no
+expert mapping (`AttributeError: To support LoRA for MoE model, 'get_expert_mapping' must be
+implemented`, `vllm/lora/utils.py:398`, v0.24.0). Found by trying it: one container start,
+which is exactly what playbook §3.10 exists to buy.
+
+So each scale point was **pre-merged** instead — `merge_and_unload` with the adapter's alpha
+pre-scaled, which reproduces exactly what a LoRA at scale s would serve, on the same serving
+path as the gate. Artifacts held at `/tank/erp-tune/serve/merged-run03{,-s075,-s050,-s025}`.
+
+    scale     attractor   gain kept   noise@31   damage     prose@40 cost
+    s=0.50       0.9684        0.0%     1.0000     0.00pp          0.00pp
+    s=0.75       0.8966       42.0%     0.9688     3.12pp          6.25pp
+    s=1.00       0.8049      100.0%     0.5938    40.62pp         15.62pp
+
+    first 42% of the gain costs   3.12pp of noise coherence
+    last  58% of the gain costs  37.50pp more   -> 12x worse per point
+
+**Every axis is monotone in scale — attractor, distance, marker density, RP length, both
+coherence measures, even truncation count. No knee anywhere.**
+
+## The conclusion, which is stronger than an exchange rate
+
+An exchange rate says the trade is expensive. This says **there is no trade to make**: the
+adapter learned ONE direction and everything rides it. brokkr's framing, worth keeping:
+**scaling moves you along the direction the adapter already learned; it cannot give you a
+different one.**
+
+That converts the merge-back from a candidate fix into a **measurement of the problem**.
+zerofata's MeroMero v1 ships the merge-back as its answer, so it was worth testing — and
+testing it is what proved it cannot be the answer here.
+
+## The T4 mechanism is NOT register capture
+
+    pad type    base            tuned
+    prose       1.0000 flat     mild knee, onset ~1,600 tok
+    noise       1.0000 flat     1.0000 -> 0.5938    <- SEVERE
+
+The prose-gradient "wedge" predicted **prose** should be the worst case. Instead prose is the
+mild axis and **shuffled tokens are where it collapses** — a continuation prior has nothing to
+continue in shuffled tokens. Every miss is `wrong-name`: correct format, on task, wrong answer.
+**State mis-tracking, not register capture.**
+
+brokkr: *"The tune did not teach it to continue prose — it destroyed its ability to skip text
+that is not worth reading."*
+
+## ⚠ INSTRUMENT FLOOR — temperature 0 is NOT deterministic under concurrency
+
+    same seat, same items, temperature 0, --jobs 8
+      prose reps 31   0.9375  0.9688  0.9688    spread 3.12pp
+      noise reps 24   0.7500  0.7188  0.7188    spread 3.12pp
+
+vLLM's continuous batching is not numerically invariant to batch composition. **Any eval delta
+under ~3.1pp at n=64/jobs=8 is inside the floor.** Only `--jobs 1` shrinks it; more n averages
+over it. Also in auto-memory as
+`reference_vllm_temp0_not_deterministic_under_jobs`.
+
+Corollary: it retro-closes the window-2 concurrency worry — measured dirty-vs-clean divergence
+was 1.56pp, comfortably inside this floor.
+
+See [[2026-08-27-mix-specified-in-the-wrong-unit]] for the wedge this displaced.