Sequential recommendation increasingly employs latent multi-step reasoning to enhance test-time computation. Despite empirical gains, existing approaches largely drive intermediate reasoning states via target-dominant objectives without imposing explicit feasibility constraints. This results in latent drift, where reasoning trajectories deviate into implausible regions. We argue that effective recommendation reasoning should instead be viewed as navigation on a collaborative manifold rather than free-form latent refinement. To this end, we propose ManCAR (Manifold-Constrained Adaptive Reasoning), a principled framework that grounds reasoning within the topology of a global interaction graph. ManCAR constructs a local intent prior from the collaborative neighborhood of a user's recent actions, represented as a distribution over the item simplex. During training, the model progressively aligns its latent predictive distribution with this prior, forcing the reasoning trajectory to remain within the valid manifold. At test time, reasoning proceeds adaptively until the predictive distribution stabilizes, avoiding over-refinement. We provide a variational interpretation of ManCAR to theoretically validate its drift-prevention and adaptive test-time stopping mechanisms. Experiments on seven benchmarks demonstrate that ManCAR consistently outperforms state-of-the-art baselines, achieving up to a 46.88% relative improvement w.r.t. NDCG@10. Our code is available at https://github.com/FuCongResearchSquad/ManCAR.

翻译：序列推荐系统日益采用潜在多步推理来增强测试时计算能力。尽管经验上取得了进展，但现有方法主要通过目标主导的优化目标驱动中间推理状态，而未施加明确的可行性约束。这导致了潜在漂移现象，即推理轨迹偏离到不合理区域。我们认为，有效的推荐推理应被视为在协作流形上的导航，而非自由形式的潜在精炼。为此，我们提出了ManCAR（流形约束自适应推理），这是一个将推理过程锚定在全局交互图拓扑结构中的原理性框架。ManCAR从用户近期行为的协作邻域构建局部意图先验，并将其表示为物品单纯形上的概率分布。在训练过程中，模型逐步将其潜在预测分布与该先验对齐，迫使推理轨迹保持在有效流形内。在测试时，推理过程自适应地进行，直至预测分布趋于稳定，从而避免过度精炼。我们为ManCAR提供了变分解释，从理论上验证了其漂移预防机制和自适应测试时停止机制。在七个基准数据集上的实验表明，ManCAR始终优于现有最先进的基线方法，在NDCG@10指标上实现了高达46.88%的相对性能提升。我们的代码公开于https://github.com/FuCongResearchSquad/ManCAR。