This paper targets e-commerce search relevance. While Large Language Models (LLMs) have demonstrated significant potential in this field, they often encounter performance bottlenecks in persistent 'corner cases' within complex industrial scenarios. Existing research primarily focuses on optimizing reasoning trajectories via Reinforcement Learning. However, real-world observations suggest that the primary bottleneck stems from knowledge boundaries, where the absence of domain-specific intelligence in the model's parametric memory creates a contextual void. This void persists when interpreting idiosyncratic queries or niche products and cannot be resolved solely through reasoning-path optimization. To bridge this gap, we propose K-CARE, a framework that extends the model's cognitive reach by grounding reasoning in external knowledge. K-CARE comprises two synergistic components: (1) Symmetrical Contextual Anchoring (SCA), which fills the contextual void by anchoring queries and products with behavior-derived implicit knowledge; and (2) Analogical Prototype Reasoning (APR), which leverages expert-curated prototypical knowledge to calibrate decision boundaries through in-context analogy. Extensive offline evaluations and online A/B tests on a leading e-commerce platform demonstrate that K-CARE significantly outperforms state-of-the-art baselines, delivering substantial commercial impact by resolving knowledge-intensive relevance challenges.

翻译：本文针对电商搜索相关性任务。尽管大语言模型在该领域展现出显著潜力，但在复杂工业场景中的持续性"边缘案例"中常遭遇性能瓶颈。现有研究主要聚焦于通过强化学习优化推理轨迹。然而，实际观测表明，核心瓶颈源于知识边界——由于模型参数化记忆缺乏领域专属智能导致的语境真空。这种真空在解释特定查询或小众商品时持续存在，无法仅通过推理路径优化得到解决。为弥合这一鸿沟，我们提出K-CARE框架，通过将推理过程锚定于外部知识来扩展模型的认知边界。K-CARE包含两大协同组件：（1）对称语境锚定（SCA），通过将查询与商品与行为衍生的隐性知识锚定来填补语境真空；（2）类比原型推理（APR），利用专家精选的原型知识，通过上下文类比校准决策边界。在某头部电商平台的大规模离线评测与在线A/B测试表明，K-CARE显著超越当前最优基线模型，通过解决知识密集型相关性难题带来显著的商业价值。