Latent reasoning has improved sequential recommendation by iteratively refining representations before prediction, but does it help spatial prediction? We find that the answer depends on whether reasoning is grounded in the underlying metric space. Without such grounding, latent reasoning degrades spatial prediction below the unmodified baseline, while a learned metric-space bias derived from pairwise distances produces consistent gains. We formalize this finding through MeRa (Metric-space Reasoning), a lightweight backbone-agnostic module that can be inserted between any sequence encoder and its prediction heads. On the GETNext backbone, the gap between reasoning without and with metric-space bias reaches 4.5% NDCG@10. MeRa achieves the best NDCG@10 on all three spatial prediction benchmarks among the compared methods, surpassing recent approaches such as GeoMamba and HMST. We prove that metric-space-constrained reasoning converges to a unique fixed point and that N-step reasoning is strictly more expressive than (N-1)-step reasoning. A controlled experiment on CLEVR with Euclidean distance confirms that the finding generalizes beyond geographic coordinates. The code is included in the supplementary material.

翻译：潜在推理通过在预测前迭代优化表征改进了序列推荐，但它在空间预测中是否同样有效？我们发现，答案取决于推理是否基于底层度量空间。若无此类基础，潜在推理会使空间预测性能低于未修改的基线，而来自成对距离的习得性度量空间偏差则能持续带来性能提升。我们通过MeRa（度量空间推理）形式化了这一发现——这是一个轻量级、与骨干网络无关的模块，可插入任何序列编码器及其预测头之间。在GETNext骨干网络上，无度量空间偏差的推理与带偏差的推理之间NDCG@10差距达到4.5%。在所有三个空间预测基准上，MeRa在对比方法中取得了最佳的NDCG@10，超越了GeoMamba和HMST等最新方法。我们证明了受度量空间约束的推理收敛至唯一不动点，且N步推理严格优于(N-1)步推理。使用欧几里得距离在CLEVR上进行的控制实验证实，该发现可推广至地理坐标以外的场景。代码包含在补充材料中。