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4. Stage-2 follow-ups (flow expert on AR trunk) — decided (frozen default ADOPTED 2026-08-09; arm 1 depth-of-reads + F-then-joint rung stay open)

Tag: seam-screen · idea #4 · index

Inherited questions from mainline §8.11 (banked: 6.57 in-run / 6.62 panel @80k, 2.2× smaller expert): more/deeper export streams (AR adaptation lives in all 35 layers; the expert reads {4,9,14} — untested headroom), expert width h512/h1536 on the better features, a second-generation AR trunk re-measured through the stage-2 lens. Cost: one screen-rung run per arm. Falsification: paired screens at matched steps.

  • Deep read 2026-08-07 00:2x–00:5xZ (post): π0.5 (arXiv:2504.16054) + Knowledge Insulation (arXiv:2505.23705) are the production version of this recipe, and the two dials where theirs differs from ours are now named external arms:

    1. All-layer reads — their expert attends per-layer to the full backbone KV stack; ours cross-attends to 3 exported streams ({4,9,14} of 35). Production-scale evidence for the deep end of the already-flagged export-streams headroom.
    2. Trunk kept adapting under stop-grad — KI’s backbone continues CE-on-FAST during expert training, with stop-gradient on the expert→backbone attention seam (expert queries attend to sg(K_b), sg(V_b)); naive joint training collapses language following (~75%→~5-10%) and is 7.5× slower to converge; with stop-grad the CE/flow loss balance needs no tuning (α=1 vs π0.5’s tuned α=10). Our sequential freeze is “extreme KI” — KI’s frozen-backbone-0% result does NOT indict it (their backbone was action-naive, ours is action-pretrained), but a joint arm (trunk CE continuing + stop-grad seam) vs the frozen baseline is a screen-rung question with a banked anchor. Natural venue: the Molmo2 trunk endpoint (~08-08), where stage-2 attachment becomes a live decision. π0.5 post-training also keeps the discrete head alive beside the flow head — our decoder kinds share the seam, so a both-heads arm is config, not surgery.

  • Lit slice 2026-08-07 03:2xZ — a THIRD independent group ships the KI-joint recipe: LabVLA (lab-bench manipulation, Qwen3-VL-4B trunk) uses exactly the two-stage shape we’re deciding on: FAST-token pretraining makes the backbone action-aware first, then an 18-layer DiT flow expert attaches under stop-grad with the backbone’s FAST/annotation CE kept active during expert training (their words: flow loss updates projection+DiT only; token losses still train the VLM). No isolating ablation published — adoption evidence, not measurement — but the KI-joint arm now has π0.5/KI + LabVLA behind it vs our sequential-freeze default. Also a second 4B-scale trunk data point for the Molmo2-4B port’s size class. Their expert reads a projected “detached prefix slice”, not all-layer KV — arms 1 and 2 remain independently testable.

  • Lit slice 2026-08-07 04:4xZ — the seam question now has a three-way map, all sides published: AEGIS (2604.16067) names the mechanism the KI/stop-grad camp is defending against — “cross-modal gradient asymmetry”: high-magnitude continuous flow/MSE gradients from the action expert overwrite a CE-trained VQA manifold; their middle path is layer-wise orthogonal gradient projection + a Wasserstein-2 anchor to pre-trained activations (claim: sheds <1% of gradient energy yet stops the activation drift; abstract carries no task-success table). A trained-repair alternative to the stop-grad seam if the KI-joint arm ever shows the expert starving for backbone adaptation — bank, don’t build. Wall-OSS-0.5 (2605.30877) ships the opposite corner: gradient-bridged co-training where the DISCRETE action head routes “VLM-native” CE gradients into the backbone while flow matching is the deployment-time interface — which is structurally OUR recipe (FAST-CE trunk + flow expert), argued from the multimodal-preservation side. Net for the attachment decision at the molmo2 endpoint: the frozen-vs-KI-joint screen (arm 2) stays the right first measurement, and both escalation directions now have named citations if it lands either way. **Papers-page re-read 2026-08-07 (~09:2xZ, page) surfaced the load-bearing ablation our skim missed: Wall-OSS’s 5-task from-scratch seam comparison has STOP-GRAD WORST (co-train 57.0% > flow-only 36.6%

    stop-grad 31.9%) — from-scratch/action-naive regime, so it does not indict KI-in-posttraining, but it is the strongest published counter-evidence to stop-grad-as-free-lunch; AEGIS re-read: preservation-only, NO closed-loop success table at all.**

  • Lit slice 2026-08-07 ~11:2xZ (pre-endpoint, the last look before the stage-2 decision) — APT (2606.12366, page): the seam damage is an INITIALIZATION problem. Random-init experts learn the language-imbalance shortcut and their noisy early gradients are what wreck the trunk; pretrain the expert first (language-masked, frozen trunk — structurally our F arm) and the best published recipe then unfreezes EVERYTHING with no stop-grad at all (pick-place grid: expert-pretrain+joint 98/84/92/58 vs KI+pretrain 96/74/90/62 vs π0.5’s 84/70/86/50; “stop-gradient is not a necessary condition”). Net for the screen: NOTHING changes pre-readout (K’s seam is corroborated for the random-init regime we are actually in); the escalation map gains a named rung — F-then-joint, warm-starting a joint run from the F checkpoint’s expert (free Stage-1 capital) — and an F≈K tie now has a published interpretation (two working guards; the next contrast is initialization, not the seam). Siblings read same session (~11:5xZ, page): VLM4VLA (2601.03309) — frozen VISION ENCODER is the published frozen-trunk failure mode (Qwen2.5VL-7B Calvin 4.057→2.823, Paligemma 3.506→0.495; word-embedding freeze free; VQA scores poorly predict VLA rank) → F-arm caveat softened for us (our trunk is embodiment-adapted BEFORE the freeze) + the diagnostic if F loses: look at vision-limited frames first; feeds #17 a trunk-selection criterion (probe vision adaptability, not VQA benches). 2605.25802 — LoRA > full-FT for VLA init (“overly reshaping the pretrained representation weakens initialization”); reconciles with APT: what matters is what SHAPED the gradients that move the trunk, not whether it moves.

  • Attachment seam screen PRE-REGISTERED 2026-08-07 ~05:1xZ (post): two arms at matched 10k steps / eff-48 on the molmo2 40k endpoint trunk — F (hard-frozen, our default) vs K (KI-joint: phase-1 CE continuing verbatim + stop-grad seam, α=1 fixed); naive joint NOT run (KI measured the collapse). Surface held constant across arms: residual taps, molmo2 rule pinned (12 taps, stride 3, layers 2,5,…,35, expert depth 12, h1024) — the depth-of-reads dial (arm

    1. stays open, NOT measured by this screen. Primary read Δ_seam = panel_v2 heun30/draws1/stable K−F paired CI; trunk-drift diagnostic (K’s greedy AR panel vs the 40k endpoint number, band 0.3) is the language-following analog; frozen decision rule (ties → frozen default stands). Instrument to land oracle-gated first: molmo2 residual exports + guard lift, seam stop-grad flag, joint CE+flow objective; #20 activation checkpointing is a hard K prerequisite. Cost est. 50–60 GPU-h, ceiling 70 with matched 5k downshift. Opens after the endpoint + #19 box obligations + the attachment-decision item (owner steer window).

  • Instrument LANDED 2026-08-07 ~06:0xZ, oracle-gated (this commit): all three pre-reg parts. (1) Molmo2Encoder residual exports (the trunk-side tap protocol existed since WP1 — the encoder/config/loading wiring was the gap; queue-title audit paid off again): residual_exports on encoder + Molmo2PromptConfig, molmo2_residual_taps pins the rule (stride 3, last tap = final layer; 36 ⇒ 12 taps at 2,5,…,35), molmo2_residual_expert_config mirrors trunk geometry (GQA 8 kv-heads × head_dim 128, plain RoPE θ=5e6), guard lifted for --decoder flow --conditioning-streams residual, checkpoint load/save round-trips. (2) --seam-stop-grad detaches taps pre-adapter in BijouModel.encode. (3) --joint-ce: Molmo2ARDecoder rider on the model, CE sums inside autocast + fp32 flow outside, THREE-normalizer chunked-backward form, rider tables at decoder-lr, saved as joint_ce.safetensors (+ config section), rider continues from the endpoint’s expert.safetensors under --backbone-init-from (pre-reg AMENDMENT appended: “decoder fresh” = the flow expert; a fresh-table CE branch contradicts “continuing verbatim”). 13 new oracles in tests/test_molmo2_residual.py — taps byte-match trunk hidden states, cache bit-identical with/without taps, K/V contract + padding invariance, stop-grad zero-vs-nonzero with naive-joint negative control, and BOTH α-edges bitwise through the real BijouTrainStep (flow half ≡ F-arm step; trunk grads ≡ phase-1 CE step). check.py 423 passed. Remaining before launch: #20 activation checkpointing (hard K prerequisite), F/K launch scripts + the joint-checkpoint AR-view materializer for read 4.

  • Launch prep LANDED 2026-08-07 ~06:1xZ (work session): both arm launchers exist (launch_box_fontaine_molmo2_attach_{F,K}_10k_ddp4.sh — sequential F-first, sha256-pinned plans, chained panel_v2 evals; K chains materialize_joint_ar_view.py + the greedy k4l2 drift panel for read 4, and a K_MEM_READY guard refuses a blind K launch before #20 + the smoke ladder). The 70 GPU-h cost gate is mechanized (attach_rate_gate.py, median-s/step projection with the batch’s extra term; 5k-downshift marker BOTH launchers honor — matched, never one arm alone). Probe-kill bars pinned in babysit.toml prepared entries: 12.6394@5000, 11.6356@7500, 10.1652@10000 (phase-1 curve + 3.0; the @10000 value from the K1 crossing, green at 7.1652 vs ≤12.0944). AR-view materializer oracle-gated against the REAL save_checkpoint write side (rider bitwise, adapted trunk, taps stripped, greedy decode via from_checkpoint on the tiny fixture). 10 new oracles; check.py 433. Remaining before launch: #20 + the K smoke memory ladder.

  • K smoke-ladder SCRIPT LANDED 2026-08-07 ~06:5xZ (work session): smoke_attach_k_ddp4.sh — the exact K recipe verbatim (warm-start from the 40k endpoint, --joint-ce --seam-stop-grad --activation-checkpointing, zero1 + chunked backward), 150 steps per rung with eval@100 + save@100 so the probe-decode and joint-checkpoint-writer memory shapes are exercised, not just the bare step. Ladder B12c6 → B8c4 → B6c3 (chunk microbatch pinned at 2); pass = rc 0 AND max vram_alloc_peak_gib over the rung’s jsonl ≤ 71.0 (torch alloc peak, the gate babysit enforces — NOT nvidia-smi reserved, the phase-1 ladder lesson); green writes the fontaine/harness/state/k_mem_ready record and echoes the exact K_MEM_READY=1 BATCH= BACKWARD_CHUNKS= launch line; a sub-B12 green is echoed as a MATCHED DOWNSHIFT for BOTH arms (the ladder must run before F, not just before K); all-red = no marker, owner steer. Runs on the box after the endpoint (~08-08), post the #19 box obligations. Remaining before launch: run the ladder green.

  • Δ_seam frozen-read script LANDED 2026-08-07 ~07:2xZ (work session): attach_seam_results.py — the pre-reg’s reads 1–5 as one command, ready before any arm data. Read 1 Δ_seam paired per-frame chunk CI (K − F, panel-v2 core, seeded bootstrap 10k — box_batch_results.py pooling verbatim, arch_batch_results.py paired-read/LORO machinery reused via sibling import); read 2 the frozen decision rule with ALL branches coded (KI-joint adopt / frozen-default-stands + Wall-OSS reading / K-wins-with-named-cost → AEGIS escalation + owner steer / partial-pending-drift); read 3 context anchors quoted-not-deciding, with the state-copy execution oracle pinned pre-data (“decisively” = ≥ 1.0 chunk-MAE below the same-npz state-copy — VOID outranks every seam verdict); read 4 trunk drift from the K ar_view + endpoint k4l2 JSONs, band 0.3 inclusive, strict plan/frame-count semantics guard; read 5 first_mae mirror + per-step curves (record-only). Defaults wired to the launchers’ exact output names incl. the 5k-downshift stems (--steps 5000). Oracle-gated pre-data: v2 anchors 6.7151/1.9453

    • state-copy 11.7639 reproduced through this file’s own pooling; degenerate K:=F → exact zeros → frozen-default; synthetic ×0.95 / ×1.05 / ×3.0 error effects fire adopt / falsified / VOID; drift band edge inclusive; misaligned index + wrong-plan JSONs hard abort. check.py 437 passed. Remaining: the screen itself (~08-08).

  • Lit slice 2026-08-07 ~08:3xZ — a scale-transfer caveat on reading Δ_seam: Encoder Winners Do Not Reliably Transfer Across VLA Backbone Scale (2606.14153) — frozen-backbone grafting diagnostic (swap the component, freeze the rest, one trainable projector): component RANKINGS flip with backbone scale (SigLIP wins at SmolVLA-450M, DINOv2-small at pi0.5-3.3B; 40 grafting runs, two LIBERO suites), and the wrapper itself has opposing effects across backbones. Two takes for the screen: (i) methodological validation — cheap frozen-graft screens as a pre-commit diagnostic is exactly the F-arm’s role; (ii) the caveat to write into the read — the F-vs-K verdict is a molmo2-at-this-scale fact, not a family-wide law; re-screen, don’t extrapolate, if the trunk or its scale changes (#17 trunks).

  • Lit slice 2026-08-07 ~09:1xZ — the recipe has independent adopters, and the frozen arm has an RL future: (i) LabVLA (2606.13578) trains a lab-robot VLA with EXACTLY our staging — FAST-token pretraining makes the Qwen3-VL-4B backbone action-aware first, THEN flow-matching posttraining attaches a DiT expert under knowledge insulation; tops LabUtopia ID + OOD among their baselines. Independent adoption of the stage-1-AR → stage-2-KI-attach ordering the seam screen measures — the K arm is the field’s incumbent, which is what makes Δ_seam the right pre-commit read. (ii) Q-VGM (2606.08015) does OFFLINE RL on a flow-matching VLA with the backbone FROZEN and only the flow expert updated — Q-gradient ascent on clean-action estimates converted to residual velocity targets (no backprop through the denoising chain); LIBERO 79.0% → 92.5% from a few-shot SFT start. Meaning for the screen: the F-arm configuration (frozen trunk + flow expert) is not a dead end even if Δ_seam favors K — it is the exact substrate the field fine-tunes with offline RL, so “frozen default stands” keeps an escalation path that KI-joint would complicate (RL updates into a live trunk). Record-only prior; no new arm until the screen’s own verdict lands.

  • Lit 2026-08-09 (VLAFlow, 2607.01586): controlled 4-recipe bake-off on one π0-style skeleton (5,000 h) — stop-gradient cost ~26 pts LIBERO-Plus (anti-KI, same side as APT), and the frozen-VLM trade-off measured both ways (VL generalization kept 74.9 vs 68.8, embodiment adaptation lost: WidowX 54.4). Joins the interpretation ladder for the F/K readout; screen unchanged. Caveat carried: their expert co-pretrains from scratch — our random-init K expert is exactly APT’s damage regime, so the screen still adjudicates.

  • Lit 2026-08-09 (FlowDAgger, 2607.08877): steer-window context note, weighed only at F≈K — the frozen-capital aftermarket now has a measured retention number (latent-space adaptation keeps held-out skills at 0.88 where SFT collapses to −0.94 delta). Not seam evidence (their bases are complete post-attach VLAs); it prices what an intact frozen policy composes with after deployment (correction loops, guidance, steering — with Q-guided flow from the inference side).

  • Lit 2026-08-09 (Hy-Embodied stack, 2606.14409, read during the live F arm): one more joint-pole ledger entry — 4B MoT trunk + random-init 370M flow expert, everything trainable, no stop-grad, no insulation — but from an embodiment-pretrained VLM, i.e. exactly APT’s named condition for joint being safe; doesn’t re-rank F vs K for our generic-VLM + random-expert regime. Expert sizing corroboration: ~11:1 trunk:expert (370M on 4B), same regime as ours. RoboTwin stack-vs-stack numbers (+25 over π0) are data-mismatched — not seam evidence.

  • Lit 2026-08-09 (ActionX, Frontiers Neurorobotics 1806605, read during the live K arm): the F-then-joint rung’s second independent citation, in its exact shape — supervised expert pre-training on a FROZEN trunk, then full joint unfreeze (no stop-grad), beats joint-from-scratch by +38 pts LIBERO-Long (52 vs 14; their RL variant adds +14 more but needs rollouts we don’t have). No frozen-vs-joint ablation at matched conditions, so it does NOT re-rank F vs K — it prices the escalation rung behind tonight’s Δ_seam readout. Venue caveat loud: Frontiers; ablation ORDERING trusted, magnitudes not (their no-pretrain Spatial row reads 0%, likely matched-budget snapshot).

  • Lit 2026-08-09 later session (RDT2 page, 2602.03310): a production-scale vote for the F-shaped ordering, hours before the Δ_seam read — RDT2’s 7B recipe is discrete-AR pretraining first (“avoided damaging discrete VLM knowledge”, their ablation), then a 400M flow expert trained on a frozen backbone, then 1-step distillation; no joint stage at all in the main recipe. Filed as F-pole ledger context only — the frozen read decides on our own numbers, unchanged; a K win would now have to explain away both APT’s diagnosis and a shipped 10k-hour stack that never unfroze.

  • Lit 2026-08-09 12:3xZ session (Qwen-VLA page, 2605.30280): F-then-joint production vote #2, also filed pre-Δ_seam — Qwen-VLA’s Stage I trains its 1.15B expert with the trunk FROZEN (text-to-action warm-start) before any joint stage; exactly the APT/ActionX escalation shape our f-then-joint rung names. Disanalogy carried: their Stage I is language-only, not a full-recipe F arm. With RDT2 that’s two production stacks this week whose first move protects the trunk from a random-init expert; ledger context only, the frozen read still decides on our own numbers.

  • DECISION 2026-08-09 ~13:5xZ (memo): the frozen default STANDS — sequential hard-freeze is the stage-2 attachment recipe for the Molmo2 trunk class; KI-joint closed-unmeasured (not falsified). The screen ended with one complete arm (owner killed K at ~4160 on cost, 12:38Z). Basis: F valid (panel 9.4157 vs state-copy 11.7639, −2.35 ≥ the 1.0 decisive bar; cond-sens 0.828); 8 matched in-run probe evals show no K advantage (K−F mean +0.208, K ahead 2/8) with K’s CE branch healthy throughout (2.6–2.8 vs phase-1 tail ~3.68 — trunk fine, still not paying); measured cost 4.11× (3.782 vs 0.920 s/step); production frozen-first votes (RDT2, Qwen-VLA). Wall-OSS reading recorded: phase-1 CE already routed the action gradients. Binds: full-length stage-2 on this trunk attaches FROZEN on the pinned surface (taps 12@stride3, h1024×12), pre-reg cites the memo; scale caveat carried (re-screen on trunk/scale change). Residuals priced: Δ_seam@3750 from retained checkpoints ~2.5 GPU-h (own pre-reg, rescue-only); F-then-joint draft must argue vs the 4× joint-step cost; AEGIS repair unbanked-never (its trigger outcome can no longer occur); arm 1 depth-of-reads untouched.

  • F-then-joint rung PRE-REG DRAFT posted 2026-08-09 ~14:2xZ (draft) — the escalation the memo unblocked, drafted in the adamc_100k shadow. Design: both arms warm-start from the banked F@10k endpoint (APT’s Stage-1 capital, already paid for); J = trunk unfrozen, NO stop-grad, CE rider continuing (the APT best-row analog) vs F2 = frozen continuation control (exists because F’s probe was still falling — beats crediting the joint phase with plain extra training). Matched +5k eff-48, fresh shared shuffle seed 2; primary Δ_joint = J@+5k − F2@+5k paired CI; conditional 10k extension only on a negative CI; adoption bar −0.3 at +10k; drift band 0.3 vs 60k 5.8602 inherited as read 4. Cost committed ~32 GPU-h (ceiling 35; extension → global 70), J’s rate anchored on K’s MEASURED 3.782 s/step. The 4×-cost burden answered up front: the rung prices a bounded final phase, not a lineage. Code audit found --init-from does the warm-start nearly free; instrument gaps named = composite materializer (F@10k + phase-1 rider tables), a narrowly-scoped escape for the naive-joint guard (refusal stays for random-init), AR-view compat check, J-config memory smoke. DRAFT status: finalizes on instrument + owner go + execution queue item; venue opens ~08-12 post-adamc-endpoint, and the rung-vs-attach sequencing question goes to the owner at finalization.

  • Instrument LANDED 2026-08-09 ~15:0xZ (finalization condition 1 of 3 satisfied). materialize_fjoint_init.py builds the composite warm start (F expert/prompt/trunk bytes verbatim + phase-1 FAST tables as the rider; refuses byte-differing trunks — the wrong-phase-1 trap); --joint-unfrozen-seam is the guard escape, warm-start-only (requires --init-from, refusal verbatim-preserved for fresh runs, launch banner says seam UNFROZEN (flow grads enter the trunk)); AR-view drift-read compat verified against J-written checkpoints on the fixture family. 12 new oracles in tests/test_fjoint_init.py, check.py 596 green. Remaining before launch: owner go at the sequencing decision + the box memory smoke (§4) at launch time.

  • 2026-08-09 fresh sweep — the fjoint joint phase as a conditional escalation (Z-1 page, 2606.31846): fourth same-shape vote for frozen-first (after LP-FT, APT, ActionX), and the first to make the JOINT half conditional in production: Z-1’s GRPO on a flow VLA trains the action expert on a frozen PaliGemma by default, unfreezing the trunk per-task only on training-stage diagnostics (SFT success level, early expert-only progress, rollout failure modes), configuration frozen before final eval. Maps onto the fjoint rung’s conditional-extension clause — a prior for making the joint phase trigger-gated rather than scheduled. Evidence thin (one task shown, no final-number decomposition); ledger prior, no gate change.

2026-08-09 — lit 0815: the seam question’s capacity axis, measured (Decoupled Action Expert page, 2511.12101): pretrain a generic denoiser on observation-free forward-kinematics data, freeze it, retrain only the conditioning pathway — a 5M MLP matches (and beats) Diffusion Policy’s 244M U-Net: LIBERO avg 84.7 vs 79.3, and the frozen-backbone version keeps 84.2. The capacity prior for the fjoint read: the pure denoising job fits in ~5M params, so the F arm is not expert-capacity-starved — if J beats F2, read it as trunk-representation adaptation, not expert relief; if J≈F2, this paper is the null’s mechanism. The conditioning-mechanism ablation is the load-bearing datum for us: cross-attention conditioning collapses under backbone freezing (DP-T 76.4→5.9 LIBERO, −41.5 avg) while modulation (FiLM/AdaLN) survives within ~0–8 pts — task knowledge migrates into whatever pathway is trainable, so the banked F@10k expert (cross-attn to residual taps) is task/trunk-entangled capital, not a portable module. Framing caveats carried loudly: the testbed is Diffusion Policy — no VLM trunk, no flow matching, VLA validation explicitly deferred; the freeze direction is inverted vs our seam (they freeze the action backbone and train conditioning); and “5M matches 244M” is partly plain capacity (DP-MLP beats DP-C end-to-end too, 84.7 vs 79.3). Capacity datum only; silent on frozen-vs-joint.

2026-08-09 — lit 0816: a third pole for the attachment frontier, and a sixth production frozen vote (VLA-GSE page, 2605.06175 + LWD page, 2605.00416): VLA-GSE initializes a tiny adapter-MoE from the frozen backbone’s own SVD spectrum (leading components → always-on shared expert, disjoint residual blocks → 7 routed rank-2 experts, 2.51% params) and beats full finetune on LIBERO-Plus perturbation robustness (81.2 vs 74.9) while retaining LoRA-grade VLM knowledge — an anti-unfreeze datum from the PEFT direction, though it never tests our sequential-converged-F-then-brief-joint design. The ablation isolates the mechanism: Gaussian-init same-architecture lands at 60.9, below plain LoRA’s 69.2 — the spectral init carries the gain, the MoE plumbing is worth ~4–6 pts (PiSSA already at 74.5). Frontier now has three poles: pure-frozen F (ours), spectral-init trunk adapters (F-cost, trunk never moves), brief joint unfreeze (fjoint, ~32 GPU-h). Cheapest decisive probe if this pole ever opens: PiSSA-vs-LoRA-vs-nothing on the tap layers. Hook corrections logged: “zero-shot” = held-out perturbations of trained tasks; “insulation-by-construction” oversells — retention is empirical and LoRA-grade (51.1 vs LoRA 51.8 MMMU). And LWD’s production datum for the ledger: 16-robot fleet RL updates only the flow expert on a frozen trunk even mid-RL with every incentive to adapt — the sixth production frozen-first vote. The fjoint pre-reg’s frozen reads are untouched.

2026-08-09 — MolmoAct2 deep dive (post, 2605.02881): the seam ledger’s most relevant production entry. Their staging IS our debate: post-train the 621M flow expert with knowledge insulation (KV conditioning detached — F’s philosophy), then at finetune drop insulation and unfreeze everything — measured: expert-only finetune 93.05 vs full-FT 97.20 (+4.15, the strongest joint-pole vote banked), insulation-at-finetune a wash (97.05 vs 97.20), discrete co-training rider +0.25, LoRA −0.95. Caveats before this re-ranks anything: their “expert-only” starts from a jointly post-trained system, not a converged frozen-trunk expert like F; LIBERO is at ceiling; and it’s the same benchmark family every ledger entry leans on. Net: doesn’t overturn the frozen memo, but it predicts fjoint > F2 and raises the rung’s expected value. Also: per-layer KV cross-attention beats final-hidden-state 95.9 vs 94.0 — the deep-read direction of our 12-tap surface, priced at ~+2 at ceiling.

2026-08-09 — lit 0819 (CL triangle): two free riders for the fjoint rung. (1) A zero-cost drift instrument: per-layer weight-delta effective rank + nuclear norm vs the pretrained trunk (the papers’ cleanest full-FT-vs-LoRA statistic: 324.7±465.0 / 4.31 vs 27.5±5.7 / 0.48) — computable from checkpoints we already save. (2) A LoRA-joint candidate first rung: LoRA-32 on the trunk preserves geometry at ~1.4% params and is proven insufficient alone under SFT but cheap insurance under a joint phase. Downside bound banked: “forgotten” competence recovers in <10% of original training steps — a bad joint phase is a recoverable experiment, not a lost trunk.

2026-08-10 — T1 tiny-expert capacity rung READ OUT (results, pre-reg): the Decoupled-Action-Expert width prior CONFIRMED at the pinned band on our stack. h256/d12 (86.8M expert, 4.2× smaller total — the identical tap/adapter surface is a fixed cost) vs F h1024/d12 (367.5M), same frozen 60k trunk, fully step- and batch-matched at 10k. Primary paired read on 15,056 panel-v2 core frames: Δ_capacity@10k = +0.188 [CI95 +0.155, +0.221] — inside |Δ| ≤ 0.3 (“prior confirmed”), far from the ≥ +1.0 capacity-binds line, but the CI excludes zero: width buys a real, small +2.0% margin, concentrated late-horizon (per-step Δ grows +0.106 → +0.374 across the 50-step chunk). State-copy execution oracle byte-green across machines. Consequences for this idea: the fjoint expert does not need h1024 to hold the frozen-trunk score — expert sizing is now a cost knob, not a risk knob, and the cheap-expert pole (#16 rig inference) has a measured price tag. Probe-vs-panel sign flip logged (probe had tiny −0.069 UNDER F; the panel flips it to +0.188 over) — small-sample probes kill runs, panels make claims.