When to Ask for Help: A Decision-Theoretic Account and Measurement Standard for Slow-Module Scheduling in Dual-System Vision-Language-Action Models
Dual-system vision-language-action (VLA) models pair a high-frequency fast policy with a slow, expensive module, and published trigger mechanisms for invoking it lack a shared objective, measured costs, and any standard separating schedule quality from module quality.
ProofPaper ↗
Key points
- We provide the missing account: invocation is formalized as a calibrated decision problem whose fixed-ratio, fixed-budget, and history-gated schedules are degenerate cases of one quantity, the Value of Intervention (VoI); a renewal baseline proves the optimal schedule periodic under state-independent collapse, with adaptive gains governed by the cost ratio $ρ$ and the dispersion of collapse.
- Measured systems span three orders of magnitude in $ρ$ (FiS-VLA $0.95$; RoboICL about $10^3$), and a direct test confirms the low-$ρ$ prediction: calibrated gains vanish under homogeneous collapse and grow with heterogeneity.
- A four-measure leak-free protocol cuts apparent early-warning AUC from $0.93$ to $0.62$ on $929$ deployment traces, a detection-limited frontier theorem converts detection quality into a schedulability statement, and a compilation loop turns measured value functions into per-task refresh periods within $2.4%$ of the closed-loop oracle.
- Together these results turn slow-module invocation from a design opinion into a measured decision.
Sources (1)
- [1]When to Ask for Help: A Decision-Theoretic Account and Measurement Standard for Slow-Module Scheduling in Dual-System Vision-Language-Action ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 03:20 AM
Dual-system vision-language-action (VLA) models pair a high-frequency fast policy with a slow, expensive module, and published trigger mechanisms for invoking it lack a shared objective, measured costs, and any standard separating schedule quality from module quality.
We provide the missing account: invocation is formalized as a calibrated decision problem whose fixed-ratio, fixed-budget, and history-gated schedules are degenerate cases of one quantity, the Value of Intervention (VoI); a renewal baseline proves the optimal schedule periodic under state-independent collapse, with adaptive gains governed by the cost ratio $ρ$ and the dispersion of collapse.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 15, 2026VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models
- Oct 14, 2026ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction
- Oct 14, 2026Back on Track: Deployment-time Policy Improvement via World Model Planning
- Oct 14, 2026Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution
- Oct 14, 2026G^3VLA: Geometric Inductive Bias for Vision-Language-Action Models
- Oct 14, 2026MorphAct: Robot Policies Morphing with the Worlds They Act In