Stage-2 attachment decision: the frozen default stands

2026-08-09 ~13:5xZ. Decision memo closing the molmo2-stage2-attachment-decision queue item, written from banked artifacts only (no new GPU work). It closes the pre-registered attachment seam screen (#4) — with the honesty flag up front: the screen’s frozen decision rule cannot fire, because the K arm was killed by the owner on cost at step ~4160/10k (12:38Z 08-09) before its panel eval existed. There is no Δ_seam paired CI, no trunk-drift read. What follows is the decision the evidence that does exist supports, recorded as a default-stands verdict, not a measured KI-joint falsification.

TLDR

The stage-2 attachment recipe for the Molmo2 trunk class is the sequential hard-freeze: train the AR trunk first, then attach the flow expert to the frozen trunk (residual taps, 12 @ stride 3, layers 2,5,…,35; expert h1024×12). Every future full-length stage-2 run on this trunk pre-registers citing this memo. The KI-joint direction (trunk CE continuing under a stop-grad seam) is closed-unmeasured for this trunk class: not falsified, but priced out — it showed no probe advantage in 4k steps of matched training while costing 4.1× per step, and the production field’s first move is frozen-shaped anyway. If a seam number is ever wanted, the cheap read below is pre-priced at ~2.5 GPU-h.

What the screen was, and what actually happened

The pre-reg put two arms on the 60k AR endpoint at matched 10k steps / eff-48: F (trunk hard-frozen, flow loss only — our lineage default) vs K (the π0.5/KI production recipe: phase-1 CE continuing verbatim, stop-grad on the expert→trunk seam, α=1). Primary read: Δ_seam = paired panel chunk-MAE difference at 10k.

F ran first (04:58–07:42Z, ~10.2 GPU-h) and banked its panel. K launched 08:01Z and was killed by the owner at step ~4160 — a cost call (“way too slow per step”), not a gate: its probes were healthy, ~1.4 under the 5k kill bar. K’s measured step cost was 3.782 s/step vs F’s 0.920 (median over jsonl windows) — 4.11×, against the pre-reg’s 2.4–2.8 s/step estimate. The screen therefore ends with one complete arm.

The evidence that exists

1. F is a valid, working attachment — the screen is not void. F @10k panel_v2 (heun30/draws1/stable, core 15,056 frames): chunk MAE 9.4157, first-MAE 2.958, vs the state-copy execution oracle 11.7639 — beaten by 2.35, more than double the ≥1.0 “decisively” bar the pre-reg’s read 3 pinned. Condition sensitivity 0.828 (the conditioning fields are consumed). In-run probe closed at 9.38@10k, still trending down.

2. The only matched F-vs-K evidence shows no K advantage. Both arms ran identical data order, batch, surface, and eval cadence, so their in-run 256-frame probes pair at matched steps — 8 evals before the kill (chart above). K−F mean +0.208, median +0.37; K ahead at 2 of 8 evals (−0.82 at 500, −0.16 at 4000), F ahead at 6. This is probe-quality evidence (256 frames, no CI machinery — the pre-reg deliberately did not make it a read), but its direction is uniform: through 4,000 steps, trunk adaptation under stop-grad bought nothing the frozen trunk didn’t already have. K’s CE branch held at 2.6–2.8 against the phase-1 tail ~3.68 throughout — the trunk was healthy and still not paying rent.

3. The cost asymmetry is measured, not estimated. 4.11× per step. A full K screen would have cost ~42 GPU-h against F’s 10.2; a full-length (100k-class) KI-joint attachment run would price at ~4× every frozen alternative, forever. Any joint-flavored escalation now has to argue against that measured number.

4. The production field’s first move is frozen-shaped. Filed on #4’s ledger before any of tonight’s evidence: RDT2 (10k-hour stack: flow expert trained on a frozen backbone, no joint stage at all) and Qwen-VLA (Stage I expert training with the trunk frozen). The KI-joint camp’s own motivating failure (KI’s frozen-backbone-0%) was an action-naive backbone; ours is action-pretrained — the Wall-OSS reading stands as the recorded interpretation: phase-1 CE already routed the action gradients into the trunk; there was little left for K to add.

The decision and what it binds

Frozen default stands, per the pre-reg’s own tie-logic (“ties go to cheaper + simpler”) extended to the absence case: no evidence K clears any bar, at a 4.11× measured price, with production practice pointing the same way.

Binding consequences:

• The eventual full-length stage-2 attachment on a Molmo2-class trunk (the adamc_100k endpoint is the natural substrate) attaches frozen, on the screen’s pinned surface: residual taps stride 3 (12 taps, layers 2,5,…,35), expert h1024 / depth 12 / adarms / bidirectional, decoder-lr 1e-4. Its pre-reg cites this memo.
• The frozen configuration keeps the offline-RL escalation path open (Q-VGM: frozen trunk + flow expert is exactly the substrate the field fine-tunes with offline RL) and the aftermarket-adaptation path (FlowDAgger).
• Scale caveat carried from 2606.14153: this is a molmo2-4B-at-this-scale fact. A different trunk or scale re-screens; it does not extrapolate.

What was NOT measured, and what each residual costs

• Δ_seam at panel quality: unmeasured. The cheap version, if ever wanted, is fully specified by existing machinery: K’s step_003750 checkpoint is retained on the box, F saves at 1250 cadence, so a matched panel_v2 pair at step 3750 needs two single-GPU panel evals (~1.24 GPU-h each, local H100 qualifies) + attach_seam_results.py --steps 3750. ~2.5 GPU-h total. Needs its own pre-reg (this memo is not it), and note the asymmetry: a 3750-step read can rescue KI-joint only if it shows a large K advantage that the probes somehow missed — the probe evidence says it will show a tie or worse.
• The F-then-joint rung (#4’s named escalation: APT, ActionX, and both production votes warm-start joint from a trained expert) survives this memo untouched — it was never K. Its pre-reg draft (idea4-f-then-joint-prereg-draft) unblocks with this memo as its basis, and must argue against the measured ~4× joint-step cost with the expert riding a trainable trunk.
• Depth-of-reads (#4 arm 1) was a held-constant surface, not a contrast — still open, unaffected.
• AEGIS orthogonal-projection repair: stays banked; it was the escalation for a K-wins-with-drift outcome that can no longer occur.

Ledger

Screen cost actually spent: F train ~10.2 GPU-h + F panel ~1.24 + K train ~13.6 (sunk at the kill) ≈ 25 GPU-h against the 70 ceiling. Artifacts: F endpoint + panel json/npz (box reports/), K checkpoints through 3750 (box, retained, not uploaded — partial arm), both train logs. Probe-curve chart: fontaine/scripts/attach_screen_probe_chart.py.