16. Few-shot rig-transfer benchmark — parked for execution (owner 2026-08-05 21:43Z), instruments banked; the north star (owner 2026-08-05 17:20–17:23Z)

Tag: rig-benchmark · idea #16 · index

• OWNER STEER 2026-08-05 21:43Z — execution PARKED, priorities reweighted: the rig datasets are small/noisy and a 12-ep fixed holdout is high-variance (“really depends on which episodes you choose”); owner will collect a better rig dataset later. Short term: improve MAE on the comm holdout and/or attribute where the limit comes from (bigger trunk? bigger image embeddings? video-trained trunk? is the flow expert needed at all vs pure AR?). Empirical anchor from the owner: lower comm-holdout MAE has always translated to good rig fine-tunes; current failure mode is gripper placement accuracy (grounding), motion is fine; aux tasks generalize strikingly (4k ft on AR-100k produced sensible subgoals for a fully-OOD instruction — “coiled USB-C cable”, “glass pot”). The instruments below stay banked: they are corpus-agnostic and re-run on the future dataset in minutes.

• External anchor for the premise (deep read 2026-08-07, post): π0.5’s Fig. 8 is the diversity-buys-transfer bet measured at production scale — held-out-home performance scales monotonically with training locations (3→104), and at 104 locations MATCHES a control trained on the test homes; 97.6% of their phase-1 examples are not the target embodiment. Evidence, not proof (their scale: ~400 h, ~100 homes) — but the north-star premise now has a citable production-scale precedent.

• Pre-reg draft posted 2026-08-05 ~21:2xZ (post): design frozen — 12-ep holdout (SeedSequence(16)) + nested N ∈ {10,25,45} materialized derived corpora (leakage-checked, the #18.8 consumer); owner run_ft_rig.sh protocol constants; best-checkpoint-at-200 selection; co-primary chunk_mae + first-4 pooled MAE; 3·σ_ft decision rule (σ_ft from N25 seed replicates); eligibility gate: init pretrain corpus must certifiably exclude the rig repos — flow-80k is contaminated (rig data in its pretrain mix), rcond/box arms qualify. Two slots (init selection rule + E5 noise scale) fill by finalization amendment after the box reads; execution at the first quiet GPU boundary after.

• Instruments LANDED 2026-08-05 ~21:5xZ (Amendment 1 on the pre-reg): plan frozen (plans/rig_fewshot_v0_k4l2.json, 12 eps / 48 core + 24 labeled; holdout = native split 0.212/seed 16 → v2 {1,2,3,6,11,15,20,24,25,30,41} + clean {2} — mechanism amendment: the draft’s bespoke SeedSequence draw could not feed the leakage checker); subsets materialized + verified (~/datasets/rig_fewshot_v0/, n10 6,223 / n25 15,881 / n45 29,107 frames, videos hardlinked bit-identical, judgments remapped, stats recomputed w/ oracle worst |Δ| 1.2e-4); leakage certs PASSED ×3 (#18.8 provenance path, doctored-provenance negative control fails loud); loader smoke bit-exact incl. shifted mid-file video decode; wrap census CLEAN on both rig repos (hygiene gate 1). Remaining before launch: launcher gen + finalization amendment (slots 1–2) after the box reads.

• Goal statement (owner): “build a VLA for my rig… prove transfer so you can fine-tune a task on a new SO101 arm with tens of examples.” Community-panel MAE is the proxy; the sample-efficiency curve is the product metric.

• Design sketch (pre-reg to write after the box batch lands): fine-tune the best lineage on N ∈ {10, 25, 50} episodes of a held-out rig task; measure panel-style MAE on that task’s holdout (and eventually rollout success) vs N. Protocol precedent: the owner’s ft-rig lineage (4–5k-step fine-tunes, run_ft_rig*.sh on the second box, both AR and flow variants).

• Dependencies: tonight’s aux-off answer + seed-noise floor pick the trunk and set the minimum detectable effect for paired ft comparisons; sign/calibration hygiene (ideas #13, #14) bites hardest on a new arm — keep them warm.

• Falsification: the curve itself — if MAE at N=50 is no better than zero-shot, transfer is not proven and the pretraining recipe (not the ft protocol) is the suspect.

• Reweights the whole list: rig-transfer relevance now outranks community-panel micro-optimization at equal cost.

• Metric note (2026-08-05 paired analysis): the pre-reg must fix the deployment replan interval k and quote first-k pooled MAE next to chunk_mae — the flow-vs-AR ranking flips at k≤3 vs k≥5 (post); chunk_mae alone is the most AR-favorable point on that axis.

• Literature (2026-08-05 slice): the ft-protocol arm should include LoRA-r32 + full vision-encoder ft (arXiv:2607.10172, π0 on UR5e precision assembly): LoRA saturates at r=32 with no significant FFT advantage; freezing or LoRA-restricting the vision encoder significantly degrades (independent external support for our grounding-bottleneck reads, idea #11); static peak VRAM 36.2→10.8 GiB — on 1×H100 that headroom converts directly to batch for the few-shot fine-tunes. Papers-page re-read 2026-08-07 (page): CONFIRMED on all counts, now numeric — r=32 at 0.74 vs FFT 0.76 (p=1.000); SigLIP frozen 0.14 / SigLIP-LoRA 0.43 vs 0.74 fully trainable; metric is ATP (sub-goal progress), not success rates; plateau beyond r=32 may partly be the α=r scaling rule.

• Proxy-validity slice (2026-08-07, page, 5 sources): our metric class is measured. CI-MSE (2606.29898) correlated raw validation MSE against real+sim rollout success over 27 VLA checkpoints (π0.5, X-VLA, GR00T N1.7): Spearman −0.61 — and in their data-scale family raw MSE ranked checkpoints backwards (+0.90). Their repair (score only task-critical frames + rollout-like alignment) reaches −0.87. Rung EXECUTED 2026-08-07 same-day (pre-reg + results): every published ranking HOLDS on the critical-frame pool — all 10 pairwise gaps keep their sign with CI95 excluding 0, coverage 99.9%, and the model-vs-state-copy separation widens on critical frames (the opposite of CI-MSE’s easy-frame-dilution failure mode). Robustness citation banked on the leaderboard; instrument critical_frame_repooling.py reusable for future rows (molmo2 endpoint). Rollout-vs-offline stays open until the rig benchmark exists. Also banked: MMRV (SIMPLER 2405.05941) as the scoring rule for any future proxy-vs-rig audit, weighted by real margins rather than Pearson; AutoEval’s (2503.24278) caveat that proxy fidelity is policy-dependent — a proxy validated on one family doesn’t transfer free to the next.

• Frozen-trunk few-shot lever #2 banked (2026-08-07 ~20:2xZ, noise-space-steering page): FRS/DSBC (2606.13675) — recover the latent noise behind ~10 good reference trajectories by reverse-ODE through the frozen flow policy, distill into a tiny auxiliary noise policy (<1 min training, ~1 GB; up to +95% absolute success on real tasks, +60% avg across 6 DROID tasks from 10 human-steered rollouts). Needs reference actions, not rewards — closer to our rig-data reality than DSRL-style RL (which stays gated on this benchmark existing). Joins VLA-Talker’s evidence-injection few-shot hook; flow-family only (explicitly inapplicable to the AR trunk).

• Frozen-trunk rig lever #3 banked (2026-08-07 ~20:3xZ, noise-steering II page): UniSteer (2605.10821) — human corrective actions converted to noise-space supervision by per-step fixed-point inversion through the frozen flow decoder (M=16 iterations, ~0.1 s/sample), then SFT-then-RL on a lightweight noise actor. Real π₀/AgileX: 20%→90% average over four tasks in ~66 min, vs DSRL 55% and DAgger 60%; needed ~1 pure-human trajectory per round where DAgger needed 8; OOD positions 100% where DSRL drops to 0–25% — the strongest evidence yet that the supervised rungs of the noise ladder beat pure noise-RL at small budgets. Ordering prior banked: SFT→RL 95% vs RL-only 60% (corrections first, reward polish second). Needs live teleop corrections — one step up the hardware ladder from DSBC’s 10 recorded demos (lever #2). The rig-time menu is now: ticket (zero machinery) → DSBC (10 demos) → UniSteer (teleop pedal + ~1 h/task) → DSRL (rewards + rollouts). Flow-family only.

• Lever #3 corroborated + retention number banked (2026-08-09, FlowDAgger page, 2607.08877): the same inversion-of-corrections recipe as UniSteer (per-step fixed point, M=5 vs 16) run as an explicit DAgger loop at 5–20 interventions/task on π0.5, Cosmos-Policy, Gr00t and vanilla diffusion — MetaWorld +0.25 mean vs SFT +0.18 / LoRA-DAgger +0.15 / DSRL +0.02; real bimanual 13%→80% w/ 10 corrections. The new number the rung was missing: held-out retention 0.88 under latent adaptation vs LoRA −0.66 / SFT −0.94 — weight-space adaptation’s forgetting cost, measured. ~8 GB train budget = deployment hardware.

• The weight-space pole of the post-SFT menu banked (2026-08-09, Hy-Embodied stack page, 2606.14409): FlowPRO/RPRO — preference RL on a flow policy with the flow loss itself as implicit reward (r = (β/2)(ℓ_ref − ℓ_θ), no reward model), labels from an intervention-and-rollback teleop loop (failure + correction = preference pair; same intervention currency as levers #3/#3′). Real bimanual: 94–99% SR, +6–12 pts over DAgger with the same interventions, and faster executions (16 s vs 27 s — preference pairs penalize dithering, positive-only imitation can’t). Caveat loud: retention never measured — FlowDAgger’s −0.94 SFT forgetting critique stands unanswered against any weight-space recipe; if this menu ever runs on the rig, held-out retention is the first read to demand. Same page banks the deployment lever: latency-aware cubic-Bézier chunk stitching + async producer-consumer loop at exactly our H=50 chunk length — the chunk-boundary-continuity piece our decode-cost story doesn’t measure yet.

• Rollout-eval design inputs banked (2026-08-09, async execution II): (1) the reaction-time identity E[Δt_react] = Δt_infer + ½·Δt_exec (FASTER, 2603.19199) — on the rig the execution horizon we choose will likely dominate decode latency, so TTFA (time-to-first-action) is the metric to instrument, not raw inference ms; FASTER’s streaming numbers + HyperVLA’s 4 ms pole bracket the latency design space. (2) ABPolicy’s (2602.23901) jerk instruments — 95th-percentile acceleration + velocity zero-crossing rate — a ready-made smoothness read for rig rollouts, same family as the SDN jerk read that showed our ODE draws already uniformly smooth within-chunk (boundary jerk is the open term). Zero measurement now; both slot into the bench design when the owner’s better rig dataset lands.

• 2026-08-09 — weight-space post-training pole, second recipe (ForesightFlow page, 2606.04968): decoupled advantage-weighted flow matching (advantage weights on action velocities ONLY; uniform on the self-scoring potential channel — coupled weighting demonstrably hallucinates value, staged ablation 42.0 vs 51.0 final-stage). One joint stage, −38% compute vs critic-based IDQL, ~1K added params. Sits beside FlowPRO in the post-SFT menu; retention unmeasured in both (the FlowDAgger critique stands). Needs stage labels + mixed-quality rollouts — a rig-data-era option, not a panel-era one.

• 2026-08-09 fresh sweep — RL pole data-efficiency datum (Z-1 page, 2606.31846): task-wise GRPO over a flow-SDE conversion of the flow decode (Gaussian noise into intermediate transitions → per-action log-probs) lifts a π0.5-based policy +13.2 pts over its SFT init (67.4 → 80.6 avg on 24 RoboCasa tasks) from 1,199 public demos and sparse binary success rewards with a 0.998 success-aware decay — no reward engineering. Shared-prefix rollouts + tree branching are the cost levers; paper reports zero compute figures and is sim-only. Sits in the post-SFT menu beside FlowPRO/ForesightFlow (weight-space) and the noise-space column.

• 2026-08-09 lit-radar-0811 — two post-SFT menu entries, the poles priced at both infrastructure extremes. (RLDT page, 2606.08602): RL-pole roster entry #3 — SVGD density transport on flow policies; the only update native to flow matching (no likelihoods, no backprop-through-time; per-depth gradients stay well-conditioned; repulsion term preserves multimodality by construction). Honest price: 64–1,000 parallel envs + trained critic + 30–48 GPU-h per task at SMALL policy scale — the whole RL pole is sim-first; parallel-env infrastructure, not sample count, is the blocker. Its expected-target trick is the same 1-NFE endpoint estimate ForesightFlow benchmarked (τ 0.80–0.86), now used for gradients. (FAN page, 2604.01570, CVPR26): the ZERO-infrastructure pole — one KL term at SFT time toward a Gaussian around the policy’s own argmax (self-referential smoothing, no rollouts/critic/labels); modest ID gains, real OOD/perturbation wins (+5–6 pts; 1/30→7/30 on their hardest real task). Discrete-token heads only → AR-trunk candidate for a future rig fine-tune pre-reg; α benchmark-tuned, unimodality-per-state assumption untested on bimodal states.

2026-08-09 — lit 0812b: RL-pole entry 4, and the pole’s first measured IND-vs-OOD trade (π-StepNFT page, 2603.02083): critic-free step-wise contrastive updates on flow-SDE transitions (binary success only, no value net, no likelihoods, one forward pass) roughly match PPO in-distribution but beat it +11.1 pp OOD on ManiSkill (semantic shift 49.1 vs 25.4, π0) — value-based buys peak IND, critic-free buys OOD retention. For the few-demo/shifted rig regime that trade favors the critic-free end. Price unchanged: 8×H100, co-located sim rollouts, sparse success flags; the pole stays sim-first. Weakness noted: LIBERO-Long 86.7 vs PPO 90.2 — sparse credit assignment degrades on long horizons. Also filed (DFM-VLA page): iterative-refinement decoders nearly double AR at 10% data (CALVIN 3.21 vs 1.71) — a few-shot-regime prior for the head axis.

2026-08-09 — lit 0813: RL-pole entry 5, the first measured NEGATIVE sign (SA-VLA page, 2602.00743): sparse-reward actor-critic PPO on a π0.5 flow policy lands below no-RL (77.5 vs 81.0 on LIBERO-Plus spatial OOD) — the pole’s emerging shape is that the RL update itself is the risk and published gains are protective machinery (dense geometric rewards +5.5, frozen spatial injection +2.25 zero-shot, learned exploration noise +0.75; full pipeline nets +2.75 over SFT, 154 GPU-h, 64 parallel envs, privileged sim rewards). Reusable design pattern: the noise-parameterization taxonomy — external SDE noise is invisible to PPO’s likelihood ratio; the variance must be a learned policy output (annealed floor for early coverage). Also (silent-failures page, 2606.03134): a bench-design constraint for the north star — telemetry-style success flags run 32–48% false-positive among flagged successes even with scripted policies in clean sim, so any rig benchmark (and every RL-pole recipe trained on binary success) needs an exteroceptive label audit; cheapest sufficient check is a final-frame scene read.

2026-08-09 — lit 0814: RL-pole entry 6 (FPO page, 2510.09976, ICRA 2026): the missing gradient route — a likelihood-free PPO ratio from the change in CFM training loss on the action (batch-normalized, exponentiated; “mild local monotonicity” assumed, not proven), no SDE conversion, no BPTT. π₀-FPO in sim: ALOHA Transfer Cube ~40% → 65%+ own-baseline sparse reward (the bankable number); LIBERO 87.2 avg is cross-base-model. The ablation is the roster datum: removing the ratio proxy costs 46 pp and clipping 33, while dropping the Q-ensemble to one critic costs 7 — the gradient route carries the method, critic elaboration is seasoning. Third independent frozen-trunk vote (decoder frozen, actor-only), and its degraded variants collapse below SFT level — consistent with SA-VLA’s negative sign. Env count/compute unreported (the pole’s open cost axis gets nothing); zero OOD/retention measurement. Pole stays sim-first.

2026-08-09 — lit 0815: RL-pole entry 7, the first fully offline + real-robot entry (RedFlow page, 2607.27782): failed deployment rollouts become action-level corrective supervision with no environment, no teleop, no critic — an off-the-shelf progress model (Robo-Dopamine GRM) scores chunks, HDBSCAN clusters matched proprio+progress contexts, and correctable failures get advantage-weighted attraction / margin suppression / correction-redirection targets on the flow endpoint. Real-world average 56.7% → 74.7% across three AgileX tasks from 100–200 deployment rollouts + binary outcomes per task; matches PPO/GRPO/DDPO on LIBERO-Spatial at ~10× fewer samples (1,536 offline vs 13K–24K on-policy). This re-prices the pole: parallel-env infra is no longer the universal entry fee, and it bridges to the intervention levers (UniSteer/FlowDAgger) — corrections without a human in the loop. Sharpest ablation repeats the protective-structure-carries-the-sign pattern: dropping the uncorrectable-failure separation costs −11.5 avg (−20.4 on Goal) — knowing which failures NOT to correct is the biggest single component. Caveats for the roster: retention/OOD unmeasured (the FlowDAgger critique stands), the headline gain is from a deliberately weakened base policy, per-task real numbers are figure reads, and the GRM is unvalidated on rig-like scenes.

2026-08-09 — lit 0816: RL-pole entry 8, the fleet-scale tier (Learning While Deploying page, 2605.00416, AgiBot): the pole’s first offline-to-online entry on real hardware at fleet scale — 16 dual-arm G1 robots stream experience to a central learner (policy broadcast every 50 steps), humans intervene reactively, and the VLM trunk stays frozen with only the flow expert updating, in production RL. Avg task score SFT 0.76 → offline RL 0.88 → online 0.95 (short-horizon 0.99, long-horizon 0.91) after ~60 robot-hours online. The load-bearing ablation: their novel DIVL critic (categorical distribution over dataset action-values, quantile-extracted implicit max, entropy- adaptive τ) vs plain expectile is a wash short-horizon but +9.7/+16.7 pts on long-horizon — the distributional representation keeps rare successes visible in heterogeneous fleet data. Policy extraction is QAM — flow-native critic-gradient-to- velocity-field regression via adjoint dynamics — adopted from Li & Levine, not theirs (hook corrected). Borrowable pre-rig: the whole offline column (0.88 beats SFT before any online loop), with the stated prerequisite that their offline buffer contains failures

• play data with terminal labels — success-only corpora collapse the advantage signal, so our entry runs through banked rig-day failure rollouts. Honesty flags: 0.95 mixes binary success with human rubric scores, trial counts and intervention rates unreported, per-task robot pools not one generalist deployment.

2026-08-09 — lit 0817: the RL-pole’s missing ingredient goes public, and two rig-benchmark metrics join the design set (ArmnetBench 2607.24481 + Legato 2602.12978): LWD’s stated prerequisite — failure rollouts with terminal labels, which success-only corpora can’t provide — now exists as a public artifact on our exact embodiment: 2,288 labeled failures + 106 suboptimal across 3,718 LeRobot-v3.0 episodes (Apache 2.0, 7 policy families incl. flow-based π0/π0.5 and Molmo-trunk MolmoAct 2 — which ranked 6/7 at 18.9% under the 50-demo budget, with a camera-conditions asterisk). Banked as the designated offline calibration/eval corpus for the pole’s pre-rig column. Flags carried: no inter-rater agreement, n≈30 per task–policy cell (±15–18 pt CIs), task confounded with cell. From Legato: completion time (−19–23% vs RTC at equal scores = hesitation, not frame-level smoothness) and boundary-overlap RMSE join the benchmark’s candidate metric set — offline chunk-MAE panels are structurally blind to seam behavior. Menu unchanged; still the benchmark-design ledger.

2026-08-09 — lit 0819: the rollout-substrate blocker is mechanically GONE, and the rig phase gets its binding forgetting precedent (Squint 2602.21203 + SO-101 VLA benchmark 2606.08881 + CL triangle): Squint ships an MIT SO-101 digital twin as registered ManiSkill3 gym envs — success predicates, arbitrary-resolution RGB (sensor_configs kwarg), pd_joint_pos with normalize_action=False (LeRobot-convention absolute joints, 5+gripper), verified installable file-by-file; sim compute is negligible next to Molmo2-4B inference. Correction: SO-101 was never upstreamed to ManiSkill3 (vendored from a community lerobot-sim2real PR into their repo). What #16 inherits is a design problem, not an access problem: the default visual world is one wrist cam over black-composited primitives — far OOD for our multi-view 229h policies (their in-domain BC baseline: 41.9% sim) — so first use is relative A/B screens + probe-label generation, with mitigations already in-repo (ThirdCameraEnv one-line switch, apply_overlay=False, swappable overlay). Their 96.1%→91.3% ranking-preserving transfer (4 methods) is the first quantitative sim↔real correlation on our exact arm. From the benchmark paper, the anti-pattern list with one keepable axis set: pilot tasks into the 20–80% success band (2 of their 4 tasks wasted on ceiling/floor), ≥50 trials/cell or paired designs (n=20 = ±22pp), pre-register multi-label vs primary-label failure annotation; keep their execution-dimension framing (control fidelity / grounding / temporal consistency / precision). From the CL triangle, the rig phase is literally 2605.26820’s experiment: pre-register that rig FT carries 229h-corpus episode replay at ρ ∈ [0.02, 0.2] on ~20% of batches — naive rig-only FT wipes prior competence within a few thousand steps (BWT −81 by 4×4k), and replay beat joint retraining at matched compute.

Lit 0820 2026-08-09 (rollout-free eval 2607.01060 + 2512.16881, + FACTR 2): the eval-substrate menu gets its priced third tier. PolaRiS (MIT code live, 224 stars) scans a real scene into IsaacSim in <1 h (2DGS→mesh + TRELLIS assets, wrist cams render — the SIMPLER blocker gone) and calibrates at r=0.9 over 24 policy-env points (worst env 0.81, best MMRV) — but the certificate needs per-checkpoint co-training (1k steps, 10% sim, ~350 teleop sim demos; over-tuning degrades the instrument) and is DROID-only, so SO-101 restarts calibration from zero. The world-model route (RoboWorld r=0.989 vs RoboArena, n=8) is not actionable: no artifact, GPT-4o judge never human-validated. Shared lesson: every rollout-free certificate was purchased with real rollouts. Two rig-day riders banked: (a) capture a 2–5 min workspace scan + a calibration board when the better rig dataset is collected — minutes of cost, unlocks the PolaRiS route retroactively; (b) FACTR 2’s 10-min free-motion protocol — log Present_Load + positions, train the 1-minute LSTM, check residual spikes on contact (unproven at the STS3215 servo class; paper floor is a $2,500 Piper). Design constants worth keeping: 20 real rollouts/policy/env sufficed for ranking ground truth; progress-scale scoring beat binary (ρ 0.970 vs 0.922); report Pearson + MMRV. PolaRiS also independently replicates our offline-validation read (action MSE poorly correlated; sim-success saturation with real performance spanning the spectrum). No new arm — execution stays parked.

Lit 0821 2026-08-10 (Curse of Precision, 2607.23108 + NeuralActuator, 2607.11734 + GigaWorld-1 / WMBench, 2607.02642). Three bench-design inputs in one slice. (1) Precision tasks get a design rule: build ONE task at 2–3 tolerance levels (re-sleeving the peg/hole is the knob that keeps cells inside the banked 20–80% band), fit the precision ceiling c across levels as a target-SR-independent headline metric, and report config changes as Δc rather than ΔSR-at-one-tolerance (their wrist-cam removal = +1.5 mm on c; the smooth-vs-erratic degradation curve doubles as a debug instrument). Caveat carried: c is a rollout-sweep fit — sim-only, Franka-only, diffusion-only in the paper — so it is a rig-phase instrument, not a pre-rig computable. (2) The FACTR 2 rig-day rider is SUPERSEDED by a shovel-ready one: NeuralActuator’s third platform is our exact arm — force MAE 0.47–0.73 N from Feetech load registers alone (no current sensor; torque via differentiable simulation, no calibration), MIT code + 3 SO-101 checkpoints + teleop code all verified live. Rig day should log their 46-column servo schema (pos/goal/vel/load/volts/temp @~62 Hz); that makes a virtual force sensor + motor-health monitor nearly off-the-shelf. Caveats: vertical-payload-only validation at our class, ~0.5 N noise floor; and the corpus still can’t feed it — the #9 zero-GPU Δq_d contact gate stands exactly as banked (their two-stage contact-probability gate shape is the one upgrade). (3) The eval-substrate menu’s world-model tier updates: the banked “no artifact” half of the verdict is dead (GigaWorld-1 Nano 1.3B / Pro 5B Apache-2.0 weights + LeRobot-format pipeline + a VLM judge with measured 87.8% human agreement, all verified live 08-10; Ctrl-World MIT + DROID checkpoint live too) — and WMBench contributes a zero-rollout pre-trust replay screen that runs on our corpus as-is (replay held-out actions, compare generated vs real video). But the “uncalibratable” half stands: its 324K “rollouts” are human-graded world-model videos under replayed actions — no policy drives, and Corr(real policy success, WM score) is defined in the paper and never computed. Screen ≠ certificate (Ctrl-World’s MMRV 0.22 is the proof); policy-ranking calibration still costs real rollouts. No new arm; execution stays parked.

New 2026-08-10 (lit 0822, the final slice before the owner pause; PhAIL 2605.29710, Positronic Robotics — full release verified: ~990-episode dataset + build/stats.py analysis pipeline + Rerun-based annotation audit tooling, phail.ai live): the bench’s statistical-protocol question ANSWERED. Their protocol — per-event time-to-success instead of binary outcomes, Kaplan–Meier CDFs with timeouts right-censored and hard failures absorbed at T=∞, macro-averaged two-sample KS across objects with episode-clustered bootstrap p-values — resolves 2 of 3 close VLA pairs at 25–30 episodes/cell where their binary-test sizing needs 600–1500 paired rollouts (the closest pair still fails at N=30). The radar’s human-anchor worry DISSOLVES: the KS machinery is purely model-vs-model; the human teleop reference only normalizes the headline scalar (HRT = RMST ratio at τ=240 s, best VLA 13.8% of human pace) — collect one teleop block per rig day for the readable number, skip it with zero statistical cost. Adopted as design inputs (not commitments): keep ≥50 single-attempt trials/cell as the BUDGET (their N counts ~4.4-event episodes ⇒ ~130 correlated events per cell — 30 episodes ≠ 30 trials; SO-101 servo noise pushes required N up), adopt KS-on-CDFs as the ANALYSIS that lets some comparisons close early; blinded same-session policy rotation

• spatial-nuisance logging become hard protocol requirements (their camera/tote side swap moved GR00T 22.2 pp — larger than the model gap under study); per-item timestamps from synchronized video with telemetry as proposer-not-truth (their 42% telemetry/operator disagreement independently replicates our 32–48% telemetry false-positive finding); lift build/stats.py rather than re-derive. Rider: their aggregation-disagreement result (macro-AUC and RMST rank the same three models in opposite order) is the loudest argument yet for publishing the full CDF panel, not one scalar.

2026-08-10 — deployment-cost rider from the T1 capacity rung (results): a 4.2× smaller flow expert (86.8M vs 367.5M; h256 vs h1024 blocks on the identical tap surface) holds the frozen-60k-trunk panel score to within +0.188 [+0.155, +0.221] paired (+2.0%, late-horizon). For the rig VLA this re-prices the expert side of inference: the expert’s per-draw FLOPs are the marginal cost of every extra noise draw (Reflex cost-model split), so a near-free 4× shrink there compounds with any draws-based decode. Decode-latency microbench on the tiny expert not yet run — would need its own (cheap) registered read.