<p>，/PRNewswire/ -- 隨着企業數據資產管理方式從簡單的積累轉向價值提取，人工智能的角色也相應發生了變化：它不再僅限於基本的數據處理和分析，而是通過基於企業真實商業邏輯的穩定、可靠和確定性的推理能力來定義。隨着大型模型深入集成到行業應用中，出現了一個挑戰：如何超越概率生成，使人工智能能夠在複雜決策和現實商業流程中協助可靠的知識檢索和操作計劃。</p>
<p>用友 AI 實驗室發佈了大型本體模型（LOM）。LOM 基於構建 - 對齊 - 推理（CAR）的集成端到端架構，使人工智能首次能夠自主地從原始企業數據構建結構化的商業邏輯系統，並在此係統上進行高精度推理。對真實企業數據集的實驗結果證明了這種方法的有效性：LOM-4B 在本體補全任務中達到了 88.8% 的準確率，在複雜圖推理任務中達到了 94% 的準確率，顯著優於現有主流大型語言模型。這些結果指出了一種可擴展和確定性部署企業級人工智能的新範式。</p>
<p><strong>超越參數競賽：賦能人工智能構建企業 “商業邏輯宇宙”</strong></p>
<p>多年來，在企業場景中採用大型模型在很大程度上遵循了 “更多參數意味着更好性能” 的線性假設。然而，在實際部署中，這種方法往往不盡如人意，例如產生不穩定的推理、不一致的輸出，以及與商業邏輯的持續脱節。核心原因在於傳統大型模型依賴於概率性標記預測，缺乏對企業系統的結構化理解，甚至缺乏自主構建符合企業實際業務需求的邏輯框架的能力。</p>
<p>用友 LOM 的核心突破在於超越單純的參數擴展，而是賦能人工智能構建自主的邏輯系統。就像一位資深領域專家，LOM 能夠從零散的結構化和非結構化數據中組織商業實體、屬性及其相互關係，形成一個連貫的企業本體。該本體作為一個與企業所有推理任務的真實操作相一致的 “商業邏輯宇宙”，通過一個結構化、內部一致的環境展開所有推理，從根本上確保推理的確定性。</p>
<p>在本體構建階段，LOM 同時處理存儲在數據庫中的結構化數據和非結構化文本文件。通過多階段的生成和驗證流程，它將分散的商業信息轉換為標準化的、機器可解釋的本體結構，同時通過迭代驗證確保邏輯一致性。例如，從人力資源中的組織層級，到財務賬户的關聯，再到供應鏈中的上下游依賴關係，LOM 系統地重構了嵌入企業數據中的基礎邏輯，以支持下一步操作的明確和可執行性。</p>
<p><strong>語義與結構深度融合的三方集成架構</strong></p>
<p>雖然自主本體構建構成了 LOM 的基礎，但語義與結構的精確對齊使得這個 “邏輯宇宙” 真正反映了企業業務的現實。企業知識在語義多樣性和結構複雜性方面具有特徵。傳統模型通常將文本理解和結構推理分開處理，導致信息丟失和推理不準確。</p>
<p>通過構建 - 對齊 - 推理的三方集成 CAR 架構，LOM 動態地將這三個過程整合為一個統一的認知框架。在對齊階段，模型利用圖感知編碼器和強化學習，準確地將語義生成的信息與構建的本體結構匹配。這使得抽象圖節點與企業的真實商業實體之間實現實時映射，同時識別本體的動態更新。當與用户的交互中出現新的商業知識和見解時，LOM 相應地調整本體結構，確保這個 “邏輯宇宙” 始終與企業業務的發展保持一致。</p>
<p>在推理階段，LOM 摒棄了概率性猜測，而是使用自主構建的本體作為不可變的商業規則，在系統內執行嚴格的確定性推理。無論是執行最短路徑和最小生成樹等複雜圖算法，還是導航多跳商業關係，LOM 都能產生可驗證和可靠的結果。這種邏輯推理執行將人工智能從模擬推理轉變為實際商業操作，完美適應需要極高準確性的企業場景，如金融、供應鏈和生產。</p>
<p><strong>在真實世界測試中超越主流模型：更少參數實現高認知密度</strong></p>
<p>範式創新帶來的真正技術價值最終在真實場景中得到了驗證。為此，用友 AI 實驗室對 LOM 進行了全面測試，使用來自人力資源、金融、資產管理、製造和供應鏈等多個企業領域的生產數據。基準數據集包括 19 個圖推理任務，將 LOM 的性能與主流大型語言模型進行了比較。</p>
<p>測試結果顯示，LOM-4B 僅有 40 億個參數，在所有任務上的平均準確率達到了 93%。而 LOM-32B 則擁有 320 億個參數，進一步將準確率提高至 94%，特別是在需要確定性推理的任務中表現出色，如最短路徑、循環檢測和最小生成樹。相比之下，主流模型雖然參數數量大幅增加，但主要在淺層語義任務中表現出色，這些任務只需表面模式和關聯即可。然而，當面臨複雜的結構推理挑戰時，它們卻顯得力不從心，因為這些挑戰中確定性邏輯和商業實體之間複雜關係深深交織在一起。在這些任務中，模型的表現急劇下降，部分任務的準確率接近零，突顯了僅依賴概率模型在需要精確、基於規則的推理和結構一致性的任務中的固有限制。</p>
<p>這一結果強調了一個核心見解：企業級人工智能的真正力量不在於參數的積累，而在於認知密度。通過融合神經符號融合的技術理念，LOM 以更少的參數提供了增強的邏輯推理能力，證明了對於工業人工智能而言，邏輯密度比參數規模更具價值。對於企業而言，這意味着可以以更穩定可靠的方式獲取與業務需求更為契合的人工智能推理能力，而無需在部署超大參數模型上投入鉅額成本。</p>
<p><strong>7D 邏輯自主性：釋放下一代企業人工智能的潛力</strong></p>
<p>在這一突破的基礎上，用友 AI 實驗室還開發了一個 10 維認知框架，概述了人工智能模型的演變。根據該框架，目前主流的大型模型和代理仍處於 6D 階段，能夠優化任務執行路徑，但缺乏自主構建基礎邏輯系統的能力。這一限制是企業應用中推理挑戰的主要原因。</p>
<p>LOM 所實現的 7D 邏輯自主性標誌着企業人工智能向更高維度演進的重要一步。它使人工智能能夠從零開始構建推理的邏輯框架，實質上賦予其設定遊戲規則的能力，而不僅僅是參與遊戲。這一演變將人工智能從數據處理工具轉變為真正的知識專家和企業業務邏輯的決策助手，為未來能夠自主決策並主動觸發業務執行的人工智能範式鋪平了道路。</p>
<p>如今，隨着企業數字化轉型進入更高級的階段，數據資產價值的全面實現越來越依賴於生成有意義的本體基礎、採用準確的子圖檢索工具，從而為行動提供深入可靠的洞察。YonLOM 不僅提供確定性推理能力，還為將大型模型整合到行業實踐中引入了一種新穎的解決方案。</p>
<p>展望未來，LOM 的 7D 邏輯自主性為進一步的進展奠定了基礎，用友 AI 實驗室將繼續將模型從推理引擎演變為一個動態智能系統，能夠提供更可靠的行動執行建議和戰略決策。</p>
<p>欲獲取更詳細的見解，歡迎訪問完整的預印本： https://chinaxiv.org/abs/202603.00072</p>
<p>來源：用友</p>
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用友網絡

<p>用友網絡人工智能實驗室推出了大型本體模型（LOM），該模型使人工智能能夠從原始企業數據中自主構建結構化的業務邏輯系統。該模型解決了傳統大型模型的侷限性，提供了穩定、可靠和確定性的推理。LOM 在本體補全任務中達到了 88.8% 的準確率，在複雜圖推理任務中達到了 94% 的準確率，超越了現有模型。其三元架構整合了語義和結構，允許實時更新和精確推理，使其適用於金融、供應鏈和生產等高準確度的企業應用</p>

<p>, /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.</p>
<div class="lb-trans"><p>，/PRNewswire/ -- 隨着企業數據資產管理方式從簡單的積累轉向價值提取，人工智能的角色也相應發生了變化：它不再僅限於基本的數據處理和分析，而是通過基於企業真實商業邏輯的穩定、可靠和確定性的推理能力來定義。隨着大型模型深入集成到行業應用中，出現了一個挑戰：如何超越概率生成，使人工智能能夠在複雜決策和現實商業流程中協助可靠的知識檢索和操作計劃。</p>
</div><p>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.</p>
<div class="lb-trans"><p>用友 AI 實驗室發佈了大型本體模型（LOM）。LOM 基於構建 - 對齊 - 推理（CAR）的集成端到端架構，使人工智能首次能夠自主地從原始企業數據構建結構化的商業邏輯系統，並在此係統上進行高精度推理。對真實企業數據集的實驗結果證明了這種方法的有效性：LOM-4B 在本體補全任務中達到了 88.8% 的準確率，在複雜圖推理任務中達到了 94% 的準確率，顯著優於現有主流大型語言模型。這些結果指出了一種可擴展和確定性部署企業級人工智能的新範式。</p>
</div><p><strong>Moving Beyond the Parameter Race: Empowering AI to Build an Enterprise &#34;Business Logic Universe&#34;</strong></p>
<div class="lb-trans"><p><strong>超越參數競賽：賦能人工智能構建企業 “商業邏輯宇宙”</strong></p>
</div><p>For years, the adoption of large models in enterprise scenarios has largely followed a linear assumption of &#34;more parameters lead to better performance&#34;. 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.</p>
<div class="lb-trans"><p>多年來，在企業場景中採用大型模型在很大程度上遵循了 “更多參數意味着更好性能” 的線性假設。然而，在實際部署中，這種方法往往不盡如人意，例如產生不穩定的推理、不一致的輸出，以及與商業邏輯的持續脱節。核心原因在於傳統大型模型依賴於概率性標記預測，缺乏對企業系統的結構化理解，甚至缺乏自主構建符合企業實際業務需求的邏輯框架的能力。</p>
</div><p>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 &#34;business logic universe&#34; 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.</p>
<div class="lb-trans"><p>用友 LOM 的核心突破在於超越單純的參數擴展，而是賦能人工智能構建自主的邏輯系統。就像一位資深領域專家，LOM 能夠從零散的結構化和非結構化數據中組織商業實體、屬性及其相互關係，形成一個連貫的企業本體。該本體作為一個與企業所有推理任務的真實操作相一致的 “商業邏輯宇宙”，通過一個結構化、內部一致的環境展開所有推理，從根本上確保推理的確定性。</p>
</div><p>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.</p>
<div class="lb-trans"><p>在本體構建階段，LOM 同時處理存儲在數據庫中的結構化數據和非結構化文本文件。通過多階段的生成和驗證流程，它將分散的商業信息轉換為標準化的、機器可解釋的本體結構，同時通過迭代驗證確保邏輯一致性。例如，從人力資源中的組織層級，到財務賬户的關聯，再到供應鏈中的上下游依賴關係，LOM 系統地重構了嵌入企業數據中的基礎邏輯，以支持下一步操作的明確和可執行性。</p>
</div><p><strong>A Tripartite Integrated Architecture for In-depth Fusion of Semantics and Structure</strong></p>
<div class="lb-trans"><p><strong>語義與結構深度融合的三方集成架構</strong></p>
</div><p>While autonomous ontology construction forms the foundation of LOM, it is the precise alignment of semantics and structure that makes this &#34;logic universe&#34; 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.</p>
<div class="lb-trans"><p>雖然自主本體構建構成了 LOM 的基礎，但語義與結構的精確對齊使得這個 “邏輯宇宙” 真正反映了企業業務的現實。企業知識在語義多樣性和結構複雜性方面具有特徵。傳統模型通常將文本理解和結構推理分開處理，導致信息丟失和推理不準確。</p>
</div><p>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 &#34;logic universe&#34; always evolves in line with the development of enterprise business.</p>
<div class="lb-trans"><p>通過構建 - 對齊 - 推理的三方集成 CAR 架構，LOM 動態地將這三個過程整合為一個統一的認知框架。在對齊階段，模型利用圖感知編碼器和強化學習，準確地將語義生成的信息與構建的本體結構匹配。這使得抽象圖節點與企業的真實商業實體之間實現實時映射，同時識別本體的動態更新。當與用户的交互中出現新的商業知識和見解時，LOM 相應地調整本體結構，確保這個 “邏輯宇宙” 始終與企業業務的發展保持一致。</p>
</div><p>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.</p>
<div class="lb-trans"><p>在推理階段，LOM 摒棄了概率性猜測，而是使用自主構建的本體作為不可變的商業規則，在系統內執行嚴格的確定性推理。無論是執行最短路徑和最小生成樹等複雜圖算法，還是導航多跳商業關係，LOM 都能產生可驗證和可靠的結果。這種邏輯推理執行將人工智能從模擬推理轉變為實際商業操作，完美適應需要極高準確性的企業場景，如金融、供應鏈和生產。</p>
</div><p><strong>Surpassing Mainstream Models in Real-World Tests: High Cognitive Density with Fewer Parameter</strong></p>
<div class="lb-trans"><p><strong>在真實世界測試中超越主流模型：更少參數實現高認知密度</strong></p>
</div><p>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.</p>
<div class="lb-trans"><p>範式創新帶來的真正技術價值最終在真實場景中得到了驗證。為此，用友 AI 實驗室對 LOM 進行了全面測試，使用來自人力資源、金融、資產管理、製造和供應鏈等多個企業領域的生產數據。基準數據集包括 19 個圖推理任務，將 LOM 的性能與主流大型語言模型進行了比較。</p>
</div><p>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.</p>
<div class="lb-trans"><p>測試結果顯示，LOM-4B 僅有 40 億個參數，在所有任務上的平均準確率達到了 93%。而 LOM-32B 則擁有 320 億個參數，進一步將準確率提高至 94%，特別是在需要確定性推理的任務中表現出色，如最短路徑、循環檢測和最小生成樹。相比之下，主流模型雖然參數數量大幅增加，但主要在淺層語義任務中表現出色，這些任務只需表面模式和關聯即可。然而，當面臨複雜的結構推理挑戰時，它們卻顯得力不從心，因為這些挑戰中確定性邏輯和商業實體之間複雜關係深深交織在一起。在這些任務中，模型的表現急劇下降，部分任務的準確率接近零，突顯了僅依賴概率模型在需要精確、基於規則的推理和結構一致性的任務中的固有限制。</p>
</div><p>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.</p>
<div class="lb-trans"><p>這一結果強調了一個核心見解：企業級人工智能的真正力量不在於參數的積累，而在於認知密度。通過融合神經符號融合的技術理念，LOM 以更少的參數提供了增強的邏輯推理能力，證明了對於工業人工智能而言，邏輯密度比參數規模更具價值。對於企業而言，這意味着可以以更穩定可靠的方式獲取與業務需求更為契合的人工智能推理能力，而無需在部署超大參數模型上投入鉅額成本。</p>
</div><p><strong>7D Logical Autonomy: Unleashing the Next Generation of Enterprise AI</strong></p>
<div class="lb-trans"><p><strong>7D 邏輯自主性：釋放下一代企業人工智能的潛力</strong></p>
</div><p>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.</p>
<div class="lb-trans"><p>在這一突破的基礎上，用友 AI 實驗室還開發了一個 10 維認知框架，概述了人工智能模型的演變。根據該框架，目前主流的大型模型和代理仍處於 6D 階段，能夠優化任務執行路徑，但缺乏自主構建基礎邏輯系統的能力。這一限制是企業應用中推理挑戰的主要原因。</p>
</div><p>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.</p>
<div class="lb-trans"><p>LOM 所實現的 7D 邏輯自主性標誌着企業人工智能向更高維度演進的重要一步。它使人工智能能夠從零開始構建推理的邏輯框架，實質上賦予其設定遊戲規則的能力，而不僅僅是參與遊戲。這一演變將人工智能從數據處理工具轉變為真正的知識專家和企業業務邏輯的決策助手，為未來能夠自主決策並主動觸發業務執行的人工智能範式鋪平了道路。</p>
</div><p>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.</p>
<div class="lb-trans"><p>如今，隨着企業數字化轉型進入更高級的階段，數據資產價值的全面實現越來越依賴於生成有意義的本體基礎、採用準確的子圖檢索工具，從而為行動提供深入可靠的洞察。YonLOM 不僅提供確定性推理能力，還為將大型模型整合到行業實踐中引入了一種新穎的解決方案。</p>
</div><p>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.</p>
<div class="lb-trans"><p>展望未來，LOM 的 7D 邏輯自主性為進一步的進展奠定了基礎，用友 AI 實驗室將繼續將模型從推理引擎演變為一個動態智能系統，能夠提供更可靠的行動執行建議和戰略決策。</p>
</div><p>For more detailed insights, you are welcomed to find the full preprint here: https://chinaxiv.org/abs/202603.00072</p>
<div class="lb-trans"><p>欲獲取更詳細的見解，歡迎訪問完整的預印本： https://chinaxiv.org/abs/202603.00072</p>
</div><p>SOURCE Yonyou</p>
<div class="lb-trans"><p>來源：用友</p>
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02:48 ETLOM：統一本體構建和語義對齊以實現大規模的確定性企業推理