Runtime plan verification: gate, refresh, recover

Lit slice 2026-08-08 ~01:1xZ, read while the #6 self-subgoal arms were decoding — the escalation side of tonight’s readout. Three papers that all ask: once a policy (or its planner) has committed to a plan, who checks it at runtime, and what happens when the check fails? Sources: SV-VLA (2604.02965, under review 04-2026), Do What You Say (2510.16281), VINE (2512.03913).

Our #6 probe conditions an action decode on a self-generated subgoal once, per frame, offline. Every escalation beyond it — refresh policies, gated conditioning, candidate selection — is a runtime-loop design. These three papers are the current published shapes of that loop.

SV-VLA — verify cheap, replan heavy (2604.02965)

Contribution. Chunked open-loop execution is fast but blind; per-step closed-loop is robust but slow. SV-VLA runs a heavyweight VLA as a low-frequency macro-planner (chunk K=64) and a 17×-cheaper verifier (frozen ViT-Tiny + the planner’s context feature, 0.081 s vs 1.373 s per call) at high frequency. The verifier predicts a “reference action” from the current observation plus the original plan intent, compares it to the pre-planned action by normalized L1, and triggers a full replan only when the discrepancy crosses τ=0.2.

Experiments. LIBERO (Goal/Spatial/Object): base K=8 96.0% at 1.0× speed; base K=64 79.5% at 3.15×; SV-VLA 90.9% at 2.17× — +11.4 pts over open-loop at two-thirds of its speedup, biggest gains exactly where drift hurts (Object +18.1, Spatial +15.0). The ablation table is the real payload: drop the planning-context feature → 73.7%; drop the current observation → 63.7%; keep verification but drop replanning → 15.5%. A gate with no recovery path is worse than useless. Threshold sensitivity is real (τ=0.1 → 83.1%, τ=0.4 → 77.4%) and the authors name adaptive triggering as open.

Transfers / doesn’t. The design lesson transfers whole: any #6 refresh policy needs (cheap drift score → gated re-decode → recovery, not just detection), and the monitor should carry the plan intent, not just the current frame. The numbers don’t: LIBERO closed-loop success under injected dynamics is a different regime from our offline panel, and their verifier is trained (L1 regression on K−1 steps) — a data+training cost our zero-training rung deliberately avoids. Also directly relevant to #22 (async staleness): this is a drift monitor for chunked execution, competing on cost with PAINT/TT-RTC-class answers.

Do What You Say — the faithfulness gap (2510.16281)

Contribution. Names and measures embodied CoT faithfulness: a reasoning VLA can emit a correct textual plan and then execute actions that don’t follow it. Their runtime fix: sample several candidate action sequences from the same model, predict each one’s outcome (simulation), and let a pre-trained VLM pick the sequence whose outcome best matches the model’s own stated plan. No retraining; up to +15 pts on behavior-composition tasks on a reasoning-annotated LIBERO-100 with OOD perturbations.

Transfers / doesn’t. The concept is the mirror image of tonight’s read. Our Δ_oracle/Δ_self split prices plan generation noise (stage 1’s phase-offset rows); their faithfulness gap is execution infidelity — text right, actions wrong. A conditioned decode can fail either way, and the two need different fixes (better planner vs selection-by-alignment). If tonight lands “oracle helps, self doesn’t,” their sample-and-align is a named escalation — but an expensive one (N action draws + outcome prediction + a VLM judge per decision), and their outcome-prediction step leans on simulation we don’t have. The cheap fragment that does transfer: alignment scoring between subgoal text and decoded actions could reuse our existing draws machinery (#19) with the subgoal as the scoring target rather than a reward model.

VINE — subgoal search with failure-aware values (2512.03913)

Contribution. A hierarchical system where System 2 does batched MCTS over candidate subgoals (nodes = 2D scene graphs, edges = verifiable subgoal transitions), scored by a value function trained on successes AND failures — “probability of reaching the goal before the failure set” — and System 1 executes the chosen subgoal with a flow-matching policy. Test-time compute scales in the expansion width: K=1→5 lifts unseen plug-insertion 28.9%→44.4% (peak K=4, latency ~linear 23.9 s→32.5 s). Failure conditioning alone is +46% relative (28.9→42.2). Uncertainty-triggered replanning adds +6.5 pts on drawer packing without retraining.

Experiments. MuJoCo plug insertion / drawer packing + Simpler + real sponge/towel packing; beats π0 (42.2 vs 26.7 unseen insertion; 65 vs 55, 55 vs 30 real-world) and VLM-as-planner variants (GPT-4o+failures 68.3 vs their 75.2 on drawers).

Transfers / doesn’t. Two fragments transfer. (1) Draws over subgoals: their expansion-width scaling says candidate-subgoal diversity is worth test-time compute — for us, pass 1 of the self arm could sample N subgoal texts (our batched-draws instrument, #1) and score them, a rung strictly cheaper than their MCTS since our “tree” is depth-1. (2) Replanning-without-retraining echoes SV-VLA from an independent group. What doesn’t: the load-bearing piece — the failure-aware value function — needs failure-labeled demonstrations we don’t have (our corpus is success-only, like their System 1, which they name as their limitation); and the scene-graph abstraction presumes an object-relation extractor.

What this fed

• #6 escalation map, ahead of tonight’s readout: the ladder above rung (a) now has published shapes with numbers — refresh = SV-VLA’s gate (cheap monitor carrying plan intent + mandatory recovery; threshold sensitivity is the named open problem); selection = VINE’s subgoal-draws (test-time width scaling, peak at small K) or Do-What-You-Say’s alignment pick (needs outcome prediction). All three are rollout-granularity — none change the frozen reads or the E5 falsifier; they price what an escalation pre-reg would cost if Δ_self earns one.
• #22 async staleness: SV-VLA is a direct competitor entry for the chunked-execution drift-monitor slot (PAINT → A2C2 → TT-RTC ladder) — training-required but 17× cheap at runtime.
• #19/#1 bridge: “sample N subgoals, score, condition on best” is implementable with the existing batched-draws instrument — banked as a hook, gated (like everything here) on tonight’s Δ_oracle being alive.