💡 The paper introduces VLA-Corrector, a lightweight framework that improves the robustness of vision-language-action models in contact-rich manipulation tasks. The problem addressed is the limitation of action chunking, where a fixed action horizon can lead to compounding errors due to small local perturbations. To solve this, VLA-Corrector proposes a detect-and-correct mechanism that continuously monitors the visual feature evolution and detects deviations from the predicted trajectory. When a deviation is detected, the system triggers a corrective replanning event, discarding the remaining stale actions and invoking online gradient guidance to replan the actions. This approach induces an adaptive action horizon, preserving long-horizon execution when the current chunk is reliable and invoking short-horizon corrective replanning when execution begins to drift. The method can be integrated into different vision-language-action models without retraining the backbone, and it mitigates the trade-off between execution robustness and policy-call frequency. The results show that VLA-Corrector substantially improves robustness in long-horizon, contact-rich robotic manipulation tasks while preserving much of the efficiency benefit of action chunking.