To address the need for robots to complete tasks in dynamic environments, researchers from multiple institutions have introduced the evaluation benchmark LIBERO-MAX. Through paired execution design, this benchmark constructs 8,000 paired cases, covering eight types of environmental changes during task execution. A total of 224,000 simulations have been conducted to evaluate 14 mainstream robot strategies. The results show that the success rates of all tested strategies declined by 11.0-25.7 percentage points under dynamic changes, with target/container movement and camera/sensor-related changes having the greatest impact. Increasing observation frequency could not fully eliminate the performance gap. LIBERO-MAX can accurately distinguish the impact of the robot's inherent limitations from that of environmental changes during task execution, identify model weaknesses, verify improvement effects, and provide a lightweight version, offering a reproducible testing platform for the development of robot technologies in dynamic environments.