お知らせ • Apr 11
Yonyou AI Lab Releases Large Ontology Model For Deterministic Enterprise Reasoning
Yonyou AI Lab has released the Large Ontology Model (LOM). Built on an integrated end-to-end architecture of Construct-Align-Reason (CAR), LOM enables AI, for the first time, to autonomously construct structured business logic system from raw enterprise data and perform high-precision reasoning on top of this system. Experimental results on real-world enterprise datasets demonstrate the effectiveness of this approach: LOM-4B achieved an accuracy of 88.8% in ontology completion tasks and 94% in complex graph reasoning tasks, significantly outperforming existing mainstream large language models. The core breakthrough of YonLOM lies in moving beyond mere parameter scaling and instead empowering AI with the ability of building up its own autonomous logic system. Much like a senior domain expert, YonLOM can organize business entities, attributes and their interrelationships from fragmented structured and unstructured data, forming a coherent enterprise ontology. The ontology functions as a "business logic universe" that aligns with the real operations within all reasoning tasks place of the enterprise via a structured, internally consistent environment, where all reasoning unfolds, fundamentally ensuring the determinacy of reasoning. In the ontology construction phase, LOM simultaneously processes structured data held in databases and unstructured textual documents. Through a multi-stage generation and validation pipeline, it converts scattered business information into a standardized, machine-interpretable ontology structure, while ensuring logical consistency through iterative verification. A Tripartite Integrated Architecture for In-depth Fusion of Semantics and Structure. Through the tripartite integrated CAR architecture of Construct-Align-Reason, LOM dynamically integrates these three procedures into a cohesive cognitive framework. During the alignment phase, the model leverages a graph-aware encoder and reinforcement learning to accurately match the semantically generated information with the constructed ontology structure. Which enables real-time mapping between abstract graph nodes and real business entities of enterprises, as well recognizing dynamic updates of the ontology at the same time. As new business knowledge and insights merges during the interactions with users, LOM adjusts the ontology structure accordingly, ensuring that this "logic universe" always evolves in line with the development of enterprise business. In the reasoning phase, LOM discards probabilistic guesswork and instead uses the autonomously constructed ontology as immutable business rules to execute strict deterministic reasoning within the system. Whether performing complex graph algorithms such as shortest path and minimum spanning tree, or navigating multi-hop business relationships, LOM produces verifiable and reliable results. Test results show that LOM-4B, with only 4 billion parameters, achieved an accuracy of 93% across all tasks on average. LOM-32B, with 32 billion parameters, further increased the accuracy to 94%, especially excelling in tasks requiring deterministic reasoning such as shortest path, cycle detection and minimum spanning tree. In contrast, mainstream models, though endowed with vastly larger parameter counts, demonstrated proficiency primarily in shallow semantic tasks, where surface-level patterns and associations are sufficient. However, they struggled when confronted with complex structural reasoning challenges, where deterministic logic and sophisticated relationships between business entities are deeply embedded as an intertwined graph. In these tasks, the model's performance deteriorated sharply, with some tasks registering near-zero accuracy, highlighting the inherent limitations of relying solely on probabilistic models for tasks that demand precise, rule-based reasoning and structural coherence. By merging the technical idea of neuro-symbolic fusion, LOM delivers enhanced logical reasoning capabilities with fewer parameters, demonstrating that for industrial AI, logical density is more valuable than parameter scale. Building on this breakthrough, Yonyou AI Lab has also developed a 10-Dimensional Cognitive Framework that outlines the evolution of AI models. The 7D logical autonomy achieved by LOM marks a significant step for Enterprise AI to evolve to higher dimensions. It enables AI to construct the logical frameworks for reasoning from scratch, essentially empowering it with the ability to set the rules of the game, rather than just playing it. YonLOM not only offers deterministic reasoning capabilities but also introduces a novel solution for integrating large models into industry practices.