让推理模型扎根度量空间，可显著提升空间预测效果。
When Does Latent Reasoning Help? MeRa: Metric-Space Bias for Spatial Prediction
- 引入度量空间偏差机制，使推理过程与真实距离关系对齐。
- 在三个空间预测数据集上，NDCG@10提升超4.5%，优于最新方法。
- 适用于任意序列编码器，且理论证明推理步数越多表达力越强。
隐式推理通过迭代优化表示提升了序列推荐性能，但在空间预测中是否有效？我们发现其效果取决于推理是否基于底层度量空间。若无此约束，隐式推理会使空间预测性能低于基础模型；而通过成对距离学习的度量空间偏差则能持续带来提升。为此提出MeRa（Metric-space Reasoning）——一种轻量级、与主干无关的模块，可插入任意序列编码器与预测头之间。在GETNext架构上，无度量空间约束的推理与有约束的推理之间，NDCG@10差距达4.5%。MeRa在三个空间预测基准上均取得最佳表现，超越GeoMamba和HMST等近期方法。理论证明：受度量空间约束的推理收敛至唯一不动点，且N步推理比(N-1)步更具表达能力。在具有欧氏距离的CLEVR控制实验中，该结论亦成立。代码见附录。
原文摘要 · Abstract (English)
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.
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