CereVLA: Cerebellum-Inspired Consequence-Aware Residual Governance for Efficient Vision-Language-Action Execution

arXiv:2609.27468 · cs.CV, cs.SY, eess.SY · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.SY, eess.SY

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Adaptation — 4× in this paper · explained in 1 episode
• Action Chunking — 3× in this paper · explained in 1 episode
• Conference on Robot Learning — 3× in this paper
• World Model — 3× in this paper · explained in 7 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Flow Matching — 2× in this paper · explained in 8 episodes
• Latent Dynamics — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
• Residual Policy Learning
• FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking

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