Look Before You Flow: Inference-Time Risk Steering for Frozen Flow-Matching VLAs

Existing vision-language-action (VLA) policies execute natural-language instructions in physical environments but lack mechanisms to avoid scene hazards during inference, including fragile objects, bystanders, and obstacles. Recent safety methods for VLAs either require retraining the policy, depend on known object geometry and dynamics models, or specify constraints in natural language without modifying the continuous action output. In this work, we introduce **MARS** (*Multi-risk Action Repulsion and Steering*), a training-free inference-time framework that steers frozen flow-matching VLAs away from scene risks by translating semantic risk knowledge from a vision-language model into bounded action-space corrections. MARS applies *Grounded Risk Verbalization* (GRV) to identify scene risks via a frozen VLM and express each as a counterfactual action prompt in the VLA's native instruction format, and *Trajectory-Anchored Flow Repulsion* (TAFR) to steer the flow-matching ODE by accumulating clipped velocity residuals in a correction state decoupled from the nominal trajectory, preventing guidance perturbations from compounding across integration steps. These designs enable MARS to bridge semantic risk recognition and bounded action-space intervention without retraining, geometric models, or learned critics. In comprehensive evaluations across three safety-critical simulation benchmarks and real-world manipulation tasks on two robot platforms, MARS improves safety across four VLA backbones ranging from 0.45B to 3B parameters. It reduces safety cost by approximately 40%, improves task success rate by up to 9.9 percentage points, and increases real-world safe success rate by up to 20 percentage points, all without modifying any model parameters. Anonymous project page: [https://mars-iclr-2026.github.io/](https://mars-iclr-2026.github.io/)