让机器人在有限行动预算下智能分配任务，避免卡死一个目标影响后续导航。

Long-horizon semantic navigation asks a robot to localize multiple open-vocabulary targets under a finite action budget. This setting exposes an execution failure that is largely hidden in single-goal ObjectNav: a low-yield or occluded target can monopolize the action budget of a reactive navigator and leave later goals unattempted. We present BAT-Nav, a training-free online goal arbitrator above a frozen VLM-guided navigation backbone. Rather than modifying the low-level policy, BAT-Nav monitors execution telemetry and updates the mission goal queue through four interventions: PERSIST, SWITCH, ABORT, and COMMIT. The controller separates allocation from verification: progress stagnation and budget retention drive ABORT/SWITCH, whereas temporal variance filtering governs COMMIT. In the deployable observable-proxy setting, BAT-Nav improves the CR-WSF frontier over fixed-patience, single-signal, dynamic-cap, cap-plus-verification, revisit-cap, and frontier-utility controllers. On HM3D, BAT-Nav-Observable reaches 0.345 CR / 0.682 WSF, improving CR by 0.063 and reducing WSF by 0.053 over DynamicCapOnly. On MP3D, it reaches 0.263 CR / 0.748 WSF, improving CR by 0.061 and reducing WSF by 0.047 over DynamicCapOnly. Behavior diagnostics show that BAT-Nav attempts more goals per episode and reduces unresolved-goal monopoly rate from 0.39 to 0.18 on HM3D and from 0.44 to 0.27 on MP3D. Oracle telemetry is reported as an upper-bound ceiling rather than a deployable setting.