ForesightNav: Persistent Belief-Aware Planning for Multi-Object Navigation

Multi-Object Navigation (MON) requires an embodied agent to reach an ordered sequence of semantic target categories within a continuous episode under partial observability. When a target category has multiple physical instances, the current-instance choice determines not only the immediate navigation cost, but also the agent's subsequent starting state and the scene evidence available for the remaining goals. However, existing MON methods typically center decision-making on the current goal, without explicitly modeling how alternative current-instance choices influence the completion cost of the remaining ordered task. Thus, we propose ForesightNav, a persistent belief-aware planning framework that turns accumulated cross-target scene evidence into persistent candidate states and explicitly looks ahead over the remaining goal sequence. ForesightNav builds an online open-vocabulary semantic 3DGS scene memory and jointly queries the current and future targets. It then uses Persistent Candidate Cluster Query to associate observations across time and maintain candidate states according to semantic evidence, observation sufficiency, and visitation history. Finally, Belief-Aware Long-Horizon Planning converts these states into candidate beliefs and evaluates each current-instance choice using both its immediate navigation cost and the expected cost of completing the remaining goals. Experiments on HM3D have demonstrated the effectiveness of the proposed method.