BEIJING, April 10, 2026 /PRNewswire/ — As the way of managing enterprise data assets evolves from simple accumulation to value extraction, the role of AI has shifted accordingly: it is no longer limited to basic data processing and analysis, but it is now defined by its ability to perform stable, reliable, and deterministic reasoning grounded in real business logic of enterprises. As large models become deeply integrated into industry applications, a challenge has emerged: how to move beyond probabilistic generation and enable AI to assist with reliable knowledge retrieval and operation plan within complex decision making and real-world business processes.
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. These results point out a new paradigm for the scalable and deterministic deployment of enterprise-grade AI.
Moving Beyond the Parameter Race: Empowering AI to Build an Enterprise “Business Logic Universe”
For years, the adoption of large models in enterprise scenarios has largely followed a linear assumption of “more parameters lead to better performance”. However, in real-world deployments, this approach often falls short, such as producing unstable reasoning, inconsistent outputs, and a persistent disconnect from business logic. The core reason is that traditional large models rely on probabilistic token prediction, without a structured understanding of enterprise systems, and even lacking the ability to autonomously construct a logic framework that fits the actual business needs of enterprises.
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. For example, from organizational hierarchies in human resources, to financial account linkages, to upstream-downstream dependencies across supply chains, YonLOM systematically reconstructs the underlying logic embedded in enterprise data for supporting the next-step operations explicit and actionable.
A Tripartite Integrated Architecture for In-depth Fusion of Semantics and Structure
While autonomous ontology construction forms the foundation of LOM, it is the precise alignment of semantics and structure that makes this “logic universe” truly reflect the realities of enterprise business. Enterprise knowledge features in both semantical diversity and structural complexity. Traditional models often process text understanding and structural reasoning separately, leading to information loss and reasoning inaccuracies.
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. This logical reasoning execution transforms AI from simulated reasoning into practical business operations, perfectly adapting to enterprise scenarios where require extremely high accuracy such as finance, supply chain and production.
Surpassing Mainstream Models in Real-World Tests: High Cognitive Density with Fewer Parameter
Genuine technological value brought by paradigm innovation is ultimately validated in real-world scenarios. To this end, Yonyou AI Lab conducted comprehensive testing on LOM using production data from diverse enterprise domains, including human resources, finance, assets management, manufacturing, and supply chain. The benchmark dataset included 19 graph reasoning tasks, comparing LOM’s performance against that of mainstream large language models.
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.
This result underscores a core insight: the true strength of enterprise-grade AI lies not in accumulating parameters, but in cognitive density. 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. For enterprises, this means accessing AI reasoning capabilities that are more aligned with business needs in a more stable and reliable way without investing huge costs in deploying ultra-large parameter models.
7D Logical Autonomy: Unleashing the Next Generation of Enterprise AI
Building on this breakthrough, Yonyou AI Lab has also developed a 10-Dimensional Cognitive Framework that outlines the evolution of AI models. According to this framework, current mainstream large models and agents are still in the 6D stage, capable of optimizing task execution paths but lacking the ability to autonomously construct underlying logic systems. This limitation is the primary reason for the reasoning challenges in enterprise applications.
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. This evolution transforms AI from a data processing tool into a true knowledge expert and decision-making assistant of enterprise business logic, paving the way for future AI paradigms that are capable to make autonomous decisions and actively trigger business executions.
Today, as enterprise digital transformation has entered a more advanced phase, the full realization of data assets’ value increasingly depends on the abilities in generating meaningful ontology base, adopting accurate sub-graph retrieval tools, and thus providing in-depth reliable insights for actions. YonLOM not only offers deterministic reasoning capabilities but also introduces a novel solution for integrating large models into industry practices.
Looking ahead, the 7D logical autonomy of LOM lays the foundation for further advancements, Yonyou AI Lab will continue to evolve the model from a reasoning engine towards a dynamic, intelligent system capable for more reliable action execution advising and strategic decision-making.
For more detailed insights, you are welcomed to find the full preprint here: https://chinaxiv.org/abs/202603.00072
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