When the Gate Does Not Gate: Bounded Memory Injection with Normalized Direction for Vision-Language-Action Models

Memory-dependent manipulation requires robots to act on visual cues that are no longer observable. Existing memory-augmented vision-language-action (VLA) models retrieve history from an external memory bank and add it to the current representation as a gated residual. The gate is expected to control how strongly memory is injected. However, we show that this expectation often fails. Across common designs, the gate degenerates. It either saturates or collapses toward zero, losing its variation across inputs. Control over injection strength then shifts to the memory projection, which undermines the fusion of memory. We trace this to scale coupling between the gate and the projection. Because training constrains only their product, the gate value neither reflects nor bounds injection strength. We propose BIND-VLA, which decouples the direction and magnitude of the memory update. The projection is orthogonalized against the current representation and normalized, so it determines only the update direction. The gate sets the fraction of a fixed update budget. It thus becomes identifiable, and memory injection is explicitly bounded. BIND-VLA also compresses each past timestep into a single memory token and keeps history outside the backbone, leaving the input length unchanged. On nine MIKASA-Robo memory tasks, BIND-VLA reaches 38.4% average success, 6.0 points above dot-product gated fusion with the same architecture. Its gate no longer degenerates, with a coefficient of variation of about 50% compared with below 5% for the dot-product gate. Fixing it to its mean value lowers average success by 9.2 points. On LIBERO, the same gate switches memory off when history is not needed, and BIND-VLA matches the memory-free policy at 92.4% versus 92.2%.