LIMBO: Dynamic Map Evaluation Under Ambiguous Evidence

Dynamic object maps must decide whether a detection belongs to a moved object, a replacement, a new arrival, or an object that has disappeared. Benchmarks usually score these decisions against one annotated history, although the same observations may admit several physical histories. We introduce LIMBO, an evaluation protocol built around sensor-matched compatible histories. It distinguishes an observed realization from a persistent entity and credits only claims shared by every history admitted under a declared evidence contract. In 355 controlled ProcTHOR groups, HOTA changes its preferred identity decision between hidden relocation and replacement even though the contracted mapper input is unchanged; 149 groups are also byte-identical in RGB-D. LIMBO preserves their common location and leaves lineage unresolved. In released 3RScan data, 5.92% of persistent targets in the sealed test split and 5.77% in a separate validation cohort have multiple annotation-compatible owners. The primary category-blind utility analysis finds no universal policy winner, whereas a separate audit of two released mappers exposes a coverage–precision trade-off. In ten fresh held-out simulated scenes, an online lifecycle mapper obtains 60.0% relocation retention, removal F1, and addition F1 from anonymous oracle regions. Simple RGB-D connected components reduce these scores to 20.0%, 10.7%, and 10.0%, showing strong sensitivity to proposal quality. LIMBO is intended for settings where unsupported certainty is costly. Realized-world and probabilistic scores remain appropriate for other decision objectives.