Act, Think or Abstain: Complexity-Aware Adaptive Inference for Vision-Language-Action Models
Current research on Vision-Language-Action (VLA) models predominantly focuses on enhancing generalization through reasoning techniques. While effective, these improvements increase computational complexity and inference latency. Furthermore, these mechanisms are typically applied indiscriminately, wasting resources on trivial tasks while failing to provide the uncertainty estimation necessary to prevent catastrophic failure on out-of-distribution scenarios. Inspired by human cognition, we propose an adaptive framework that dynamically routes VLA execution based on the complexity of the perceived state. Our approach transforms the VLA's vision-language backbone into an active detection too...
| Authors | Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci |
|---|---|
| Primary category | cs.CV |
| Published | 5 March 2026 |
| Last updated at source | 25 July 2026 |
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