EvolvingNav predicts where targets go with a time-indexed 4D belief, lifting first-inspection success from 45.33% to 61.32% on EvoWorld-Bench
The work introduces EvolvingNav, which builds a persistence–relocation belief from timestamped 3D object histories and closes the loop with an event-driven predict–observe–replan filter so an agent can infer where a target is when targets may move before the query or during navigation, and it releases EvoWorld-Bench with 54 scenes and 803,680 tasks, reporting improved navigation success and search efficiency over the evaluated baselines in simulation and real-robot experiments.