docs(pfi): evaluate the two gen-seat replacement candidates

preetpatel/Qwen3.8-27B-Uncensored-NVFP4 is disqualified on two independent hard
failures, both read directly off the artifacts via HTTP Range requests against the
safetensors header (about a megabyte, not a 20 GB download):

  - ZERO mtp tensors. The author's recipe.yaml asks to ignore re:.*mtp.*, but the
    written config.json has no mtp ignore entry while re:.*visual.* expanded to 110
    explicit ones. That asymmetry is llm-compressor pruning a pattern that matched
    nothing, i.e. the MTP head was never loaded. Costs roughly half our decode.
  - NVFP4 W4A4, 4-bit activations. Precisely the AEON failure mode: the fidelity
    gradient is W4A4 < W4+FP8 < W4+bf16, W4A4 drove ~15-20% stochastic degeneration,
    and it collapses past ~30k context. The gen seat serves 262K.

orcarouter/Qwen3.8-27B-Uncensored checks out as a quant source: stock-Qwen base
rather than a reasoning-compression finetune, Arditi-style single-direction
abliteration, 15 mtp and 333 visual tensors verified present, chat template
byte-identical to the heresy build we are serving, and the gate is already accepted
on our token.

Also records the author's FP8 release as a noted-but-not-recommended third option:
far more traction, but 30.9 GB against NVFP4's 22 GB, and on a zero-sum GPU0 that
+9 GB comes out of the KV pool and breaks 262K context.

And states the imatrix constraint plainly. Our recipe has always requested
imatrix_mse and always silently fallen back to uniform MSE; playbook 3.13 warns
against assuming an imatrix would help before verifying llm-compressor can consume
external importance data at all. The W4A16 portions are data-free by construction
and cannot use it regardless.

docs/pfi/gen-seat-candidate-eval-2026-08-21.md

@@ -0,0 +1,89 @@
+# Gen-seat candidate evaluation — 2026-08-21
+
+Cold-Fusion was abandoned (see `persistent-memory.md`); the seat is on
+`qwen38-27b-heresy-nvfp4-mixed`. Two replacement candidates were put up. All facts
+below come from the HF registry and from reading the artifacts directly — the
+safetensors headers were fetched with HTTP **Range** requests, so the tensor census
+cost about a megabyte rather than a 20 GB download.
+
+## The candidates
+
+| | `orcarouter/Qwen3.8-27B-Uncensored` | `preetpatel/…-NVFP4` |
+|---|---|---|
+| what | BF16 source weights | NVFP4 quant **of orcarouter** |
+| size | 55.6 GB | 19.7 GB |
+| base | `Qwen/Qwen3.8-27B` (**stock Qwen**) | orcarouter |
+| **MTP tensors** | **15 ✓** | **0 ✗** |
+| visual tensors | 333 ✓ | 333 ✓ |
+| scheme | n/a (bf16) | **NVFP4 W4A4** ✗ |
+| `re:^mtp.*` in ignore | n/a | **absent** ✗ |
+| traction | 3,278 dl / 60 likes | 36 dl / 0 likes |
+| gated | yes — **our token already has access** | no |
+| chat template | **sha `c3cf9e34` — byte-identical to the live heresy seat** | same |
+
+## Verdict: preetpatel is disqualified, on two independent hard failures
+
+**1. Zero MTP tensors.** Read straight from the safetensors header: 2,672 tensors,
+**none** matching `mtp.*`. The author's own `recipe.yaml` asks to ignore
+`re:.*mtp.*`, but the written `config.json` contains no mtp ignore entry at all —
+while `re:.*visual.*` expanded to 110 explicit entries. That asymmetry is the
+signature of llm-compressor pruning an ignore pattern that matched nothing, i.e.
+the MTP head was never loaded and never quantized. It is the same
+`re:^mtp.*`-pruning trap documented in the playbook, seen from the outside.
+
+Cost: no speculative decoding. Our seat runs MTP at ~59% acceptance and 118 tok/s;
+without it, roughly half the decode throughput.
+
+**2. NVFP4 W4A4 — 4-bit activations.** `input_activations: num_bits 4, type float`.
+This is precisely the AEON failure mode we spent a multi-day saga diagnosing and
+purging: the activation-fidelity gradient is W4A4 < W4+FP8 < W4+bf16, W4A4 was
+responsible for ~15-20% stochastic degeneration, and W4A4 collapses past ~30k
+context. **The gen seat serves 262K.**
+
+Either failure alone would rule it out. It is also one day old with 36 downloads.
+
+## orcarouter checks out as a quant source
+
+Stock-Qwen base (not a reasoning-compression finetune — the trait that sank
+Cold-Fusion), Arditi-et-al. single-direction abliteration, MTP and vision both
+explicitly preserved and verified at 15/333, chat template byte-identical to the
+build we are serving right now, and the gate is already accepted on our token.
+
+## Third option, noted and not recommended
+
+`orcarouter/Qwen3.8-27B-Uncensored-FP8` — 76,109 downloads, 693 likes, far more
+traction than either candidate. **But 30.9 GB against NVFP4's 22 GB**, and GPU0 is
+zero-sum with meromero co-resident: +9 GB of weights comes straight out of the KV
+pool, taking it from ~14.4 GiB / 403k tokens to roughly 5 GiB / ~150k — which
+breaks 262K context at 1.5x concurrency. Viable only if the seat gives up long
+context or meromero moves.
+
+## The imatrix constraint — read before committing to it
+
+The operator asked for imatrix if we quant ourselves. **This is not a switch.**
+
+`quant_mixed_nvfp4.py` already sets `observer="imatrix_mse"` on the W4A4 group and
+has **never once used it** — llm-compressor logs `no importance data available.
+Falling back to uniform MSE` and proceeds. Playbook §3.13 documents this and warns
+explicitly: *do not "fix" it by assuming an imatrix would help; verify first that
+your llm-compressor version can consume an externally supplied importance matrix at
+all, and in what format.* Parked as `park/…imatrix-mse…` (id 42) with the
+calibration corpus that would feed it.
+
+Also note the W4A16 portions of the mixed recipe are **data-free by construction** —
+llm-compressor infers `DataFreePipeline` for weight-only quantization and ignores
+calibration data entirely. Imatrix can only ever bite on the W4A4 MLP group.
+
+So "quant with imatrix" is two projects: an unscoped capability investigation, and
+then the ~2h quant. Recommendation is to decouple them — ship the proven recipe
+first, run imatrix as its own bounded experiment. Every A/B we hold is
+uniform-MSE-to-uniform-MSE, so a non-imatrix build stays directly comparable to
+heresy's PPL 6.910 / 47.2% acceptance.
+
+## Mandatory step if we pull
+
+Run `services/gen-seat-mixed-quant/bench/think-leak/think_prior.py` on the bf16
+**before any GPU time**. It is a ~10s CPU measurement and it is the gate that would
+have disqualified Cold-Fusion before its 300-trial study ever ran. Prior is
+favourable — stock-Qwen base, template identical to heresy, which measures <0.002
+against Cold-Fusion's 0.185 — but measure, don't assume.