In the implementation of enterprise AI, balancing the parameter scale of large models with deployment costs and performance has always been an industry pain point. Traditional large models often have tens or even trillions of parameters, which not only require exclusive high-end computing infrastructure but also make localized deployment difficult for small and medium-sized enterprises and traditional enterprises. LOM(Large Ontology Model) launched by Yonyou AI Lab achieves an 89.47% overall accuracy in complex enterprise graph reasoning tasks with a lightweight architecture of 4 billion parameters, breaking the industry’s inherent perception that “parameter scale determines performance” and providing a brand-new technical path for the large-scale implementation of enterprise AI.

LOM performance breakthroug stems from its original Construct-Align-Reason unified framework. Different from traditional models that simply rely on parameter stacking to improve capabilities, LOM focuses its technical core on “structural optimization” and “task adaptation”. Built on Qwen3-4B-Instruct, the model features a three-layer customized training pipeline: first, ontology instruction fine-tuning equips the base large model with structural cognitive capabilities of enterprise ontology, enabling it to accurately distinguish between the abstract layer of business logic and the instance layer of data entities; second, text-ontology grounding constructs a linear alignment projector to fuse textual semantic features with ontology structural features, eliminating the semantic barrier between text and graph representations; finally, multi-task instruction fine-tuning based on a curriculum learning strategy allows the model to gradually advance from simple graph prediction tasks to complex generative reasoning, mastering the underlying logic of ontology-centric operations rather than mere answer mapping.

This architectural design makes LOM outperform mainstream industry large models in core reasoning tasks: in computationally intensive global reasoning tasks such as PageRank (80% accuracy), Minimum Spanning Tree (60% accuracy), and Topological Sorting (100% accuracy), LOM performance far exceeds that of large-parameter models such as DeepSeek-V3.2. These tasks are exactly the core needs of enterprise supply chain optimization, financial risk analysis, and manufacturing process planning. Meanwhile, the lightweight feature of 4 billion parameters makes LOM perfectly compatible with various enterprise deployment architectures including on-premise, cloud, and hybrid modes, without the need for exclusive computing support. It greatly reduces the deployment threshold and operation and maintenance costs of enterprise AI, truly realizing the dual value of “high performance and easy deployment”.

In addition, the structural optimization design of LOM has been fully verified through ablation studies: removing the CoT reasoning mechanism leads to an 11% drop in model accuracy, while canceling the structural encoding of the graph encoder and targeted instruction fine-tuning directly reduces the accuracy to 18.95%. This fully proves that structural encoding and task-aligned fine-tuning are the core of reliable reasoning for large models in enterprise scenarios, and also confirms the correctness of Yonyou technical route of “replacing parameter scaling with structural scaling”.