SAFER: Selective FFN Readout for Cross-Task VLA Failure Detection

Reliable failure detection is essential for deploying vision-language-action models (VLAs) across diverse manipulation tasks. Existing methods have explored using VLA hidden states to detect execution failures, but these aggregated representations do not reveal which internal computational contributions are most useful for failure detection on unseen tasks. We propose SAFER, a selective feed-forward network (FFN) readout method for cross-task VLA failure detection. The method uses successful and failed rollouts from the training set and performs training-free FFN neuron selection. It ranks neurons based on the separability of their activations between successful and failed rollouts and selects a subset of neurons for failure monitoring. At each execution step, we construct the detector input representation by accumulating the activation-weighted output directions of the selected neurons. The selected neuron subset is directly applied to unseen tasks, while the VLA parameters remain frozen and the original action computation process is preserved. Experiments show that SAFER can effectively improve failure detection performance on unseen tasks compared with full hidden-state readout.