We present Odin, the first production-deployed graph intelligence engine for autonomous discovery of meaningful patterns in knowledge graphs without prior specification. Unlike retrieval-based systems that answer predefined queries, Odin guides exploration through the COMPASS (Composite Oriented Multi-signal Path Assessment) score, a novel metric that combines (1) structural importance via Personalized PageRank, (2) semantic plausibility through Neural Probabilistic Logic Learning (NPLL) used as a discriminative filter rather than generative model, (3) temporal relevance with configurable decay, and (4) community-aware guidance through GNN-identified bridge entities and inter-community affinity scores. This multi-signal integration, particularly the bridge scoring mechanism, addresses the "echo chamber" problem where graph exploration becomes trapped in dense local communities. We formalize the autonomous discovery problem, prove theoretical properties of our scoring function, and demonstrate that beam search with multi-signal guidance achieves $O(b \cdot h)$ complexity while maintaining high recall compared to exhaustive exploration. To our knowledge, Odin represents the first autonomous discovery system deployed in regulated production environments (healthcare and insurance), demonstrating significant improvements in pattern discovery quality and analyst efficiency. Our approach maintains complete provenance traceability -- a critical requirement for regulated industries where hallucination is unacceptable.

翻译：我们提出了Odin，这是首个投入生产部署的图智能引擎，用于在无需预先设定的情况下自主发现知识图谱中的有意义模式。与基于检索的、回答预定义查询的系统不同，Odin通过COMPASS（复合导向多信号路径评估）分数引导探索，这是一种新颖的度量标准，它结合了：（1）通过个性化PageRank计算的结构重要性；（2）通过神经概率逻辑学习（NPLL）计算的语义合理性，此处NPLL被用作判别式过滤器而非生成模型；（3）具有可配置衰减的时间相关性；以及（4）通过GNN识别的桥接实体和社区间亲和度分数实现的社区感知引导。这种多信号集成，特别是桥接评分机制，解决了图探索陷入密集局部社区而导致的"回音室"问题。我们形式化了自主发现问题，证明了我们评分函数的理论性质，并证明了在多信号引导下的束搜索实现了$O(b \cdot h)$的复杂度，同时与穷举探索相比保持了高召回率。据我们所知，Odin代表了首个在受监管的生产环境（医疗保健和保险）中部署的自主发现系统，在模式发现质量和分析师效率方面均展现出显著提升。我们的方法保持了完整的溯源可追溯性——这对于幻觉不可接受的受监管行业而言是一项关键要求。