- *negative result:* on **Qwen-3.6-35B-A3B**, `2610.20826`'s phase/entropy/CV/Jaccard/top-1 proxy is neutral near 4 bpw and loses at the steep IQ2 floor: uniform KLD `0.034321`, proxy `0.038142–0.038249`, random `0.037728/0.037808` (random marginally better, within about 1 SE of proxy). proxy p99 KLD rises `0.437 -> 0.487–0.492`; mandatory downgrades inject tail damage. this is a coding-domain, layerwise negative result, not a theorem against all mixed precision or a measurement of our IQ2 formats.
- *our boundary:* the K512 header has **144 packed expert tensors**, not 144 independently precision-assigned experts: `68 IQ2_S`, `48 Q2_0`, `24 IQ2_XXS`, `4 IQ3_S`; each tensor packs 512 experts with one type. ukisai's imatrix/GSQ-RCO artifact is not DuoRate's HQQ/GPTQ/MiLo/MoEQuant/BitsMoE baseline. its IQ codes are not prefix-readable StagQ planes; truncating their bytes is corruption, not Stage-2 read-tier selection.
- *rule:* preserve the current quant assignment unless a format-/byte-/arena-matched ablation beats it on mean **and tail** KLD, top-1, persona/JA/ops/tool behavior and measured SSD/GPU time. cache ranks may optimize reuse, but must not silently become precision ranks. cache-conditioned SSD miss weights also differ from distortion weights, so Proposition 1 cannot be copied unchanged into `stream.cpp`.
44. **reading single-token top-1 agreement, or a single-run prefill tok/s, as signal: the teacher-forced decode noise floor** (`audits/opus_prefill_opt.md:82-89`, `call_1381557`, `call_6642651`, `call_1274067`):
- *trap:* treating a `96.88%` vs `98.44%` vs `100.00%` top-1 column, or a `205.28` vs `246.83 tok/s` prefill column, as a property of the knob under test. neither is.
- *decode noise floor, measured:* **base vs base, identical config, run twice** gives `mean KLD 0.0012`, **`top-1 95.3%`**. decode is not run-to-run deterministic even with MoCE off, because residency/timing-dependent read-ahead paths (`EXPERT_SPLIT`, `SPLIT_TURN`) change which slot an expert sits in and when it is read. so a 1-token top-1 flip out of 64 eval tokens (`63/64` vs `64/64`) is exactly one noise event, not a result. `98.44%` vs `100.00%` on a 64-token teacher-forced eval is **one coin toss**.
- *prefill noise floor, measured:* in the 5-arm multi-chunk sweep on `phase4_contrastive_vn_chunk0.txt` (`2,487` tokens), two arms with the **identical** chunk-0 setting (`300.85`) came out at `10,075.62 ms` and `12,115.41 ms` — a **20% spread from ordering alone**, driven by the arena fill read rate (`2.2 s` → `0.8 s` as the sweep warmed the SSD). every prefill number in a sequential sweep of arms is confounded by position.
- *rule:* (a) quote top-1 only when the delta exceeds one token on 64, i.e. state the raw `n/64`; (b) never rank prefill arms from a sequential sweep — interleave them (A/B/A/B, median of >= 5 pairs) or pin the arena with `STRATA_METAL_STREAM_FAKE=1`; (c) a KLD ordering is trustworthy at `> 0.002` separation, a top-1 ordering is not trustworthy below `2` tokens; (d) `rc != 0` arms contribute nothing — `call_6642651` returned `rc=3` on all four arms (the `gdn_prompt.metal:891` `MTL` 3.2 `thread`-qualifier compile failure) and its "results" are arena-setup log lines, not measurements.
### B. ruled out from the original blueprint
details and primary-source checks in `blueprint_review.md`.
- **the blueprint's expert size math and mixed-quant expert tiers:** claimed 224 experts × 48 layers at Q3_K is 14.2 GB (real: **22.7 GB**, or 16.5 GB at Q2_0), and proposed mixing Q3_K hot experts with Q2_K cold experts inside one layer (impossible in GGUF: `ffn_*_exps` is one 3D tensor per layer with one quant type [src: ISTA card]).
- **stage 0 PLE 28.8 GB n-gram table pruning / vector editing:** the table is hashed bigrams/trigrams with no 1-to-1 vocabulary mapping, stays on SSD (~16 row reads/token) with zero VRAM cost, and the cited "3.6% cliff" was from a letter-lipogram paper (banning the letter `e` in Gemma-3 4B) [src: 2505.20501].