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：潜推理通过在预测前迭代精炼表征，改进了序列推荐；但它是否有助于空间预测？我们发现答案取决于推理是否基于底层度量空间。若无此基础，潜推理会使空间预测性能下降至低于未修改基线，而基于成对距离习得的度量空间偏置则能带来一致的改善。我们通过MeRa（度量空间推理）将这一发现形式化，这是一个轻量级且与骨干网络无关的模块，可插入任何序列编码器与其预测头之间。在GETNext骨干网络上，不带与带度量空间偏置的推理之间差距达到4.5% NDCG@10。在所有三个空间预测基准上，MeRa在对比方法中均取得了最佳NDCG@10，超越了GeoMamba与HMST等近期方法。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步推理严格比(N-1)步推理更具表达力。一项在CLEVR上使用欧氏距离的控制实验证实，该发现可以泛化至地理坐标之外。代码包含在补充材料中。