Ontology
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Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
2026-04-01 06:59:15Thanh Luong TuanAbstract
Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS) platform that addresses these limitations through ontology-constrained neural reasoning. Our approach introduces a three-layer ontological framework--Role, Domain, and Interaction ontologies--that provides formal semantic grounding for LLM-based enterprise agents. We formalize the concept of asymmetric neurosymbolic coupling, wherein symbolic ontological knowledge constrains agent inputs (context assembly, tool discovery, governance thresholds) while proposing mechanisms for extending this coupling to constrain agent outputs (response validation, reasoning verification, compliance checking). We evaluate the architecture through a controlled experiment (600 runs across five industries: FinTech, Insurance, Healthcare, Vietnamese Banking, and Vietnamese Insurance), finding that ontology-coupled agents significantly outperform ungrounded agents on Metric Accuracy (p < .001, W = .460), Regulatory Compliance (p = .003, W = .318), and Role Consistency (p < .001, W = .614), with improvements greatest where LLM parametric knowledge is weakest--particularly in Vietnam-localized domains. Our contributions include: (1) a formal three-layer enterprise ontology model, (2) a taxonomy of neurosymbolic coupling patterns, (3) ontology-constrained tool discovery via SQL-pushdown scoring, (4) a proposed framework for output-side ontological validation, (5) empirical evidence for the inverse parametric knowledge effect that ontological grounding value is inversely proportional to LLM training data coverage of the domain, and (6) a production system serving 21 industry verticals with 650+ agents.
Abstract (translated)
企业采用大语言模型（LLMs）受到幻觉、领域漂移以及无法在推理层面执行监管合规的限制。我们提出一种在基础智能体操作系统（FAOS）平台内实现的神经符号架构，通过本体论约束的神经推理来解决这些限制。该方法引入三层本体论框架——角色、领域与交互本体论——为基于LLM的企业智能体提供形式化语义基础。我们形式化了非对称神经符号耦合的概念，其中符号本体论知识约束智能体输入（上下文组装、工具发现、治理阈值），同时提出扩展此耦合以约束智能体输出（响应验证、推理验证、合规检查）的机制。我们通过对照实验评估该架构（在金融科技、保险、医疗、越南银行业和越南保险业五个行业进行600次运行），发现本体耦合智能体在指标准确性（p < .001，W = .460）、监管合规性（p = .003，W = .318）和角色一致性（p < .001，W = .614）上显著优于无基础智能体，且在LLM参数知识最薄弱的领域——特别是越南本地化领域——提升最为显著。我们的贡献包括：（1）形式化三层企业本体论模型，（2）神经符号耦合模式分类法，（3）通过SQL下推评分实现的本体约束工具发现，（4）输出侧本体验证的提出框架，（5）反向参数知识效应的实证证据——本体基础价值与LLM训练数据覆盖领域成反比，（6）服务21个行业垂直领域、拥有650多个智能体的生产系统。
URL
https://arxiv.org/abs/2604.00555
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Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data
2026-03-31 20:01:20Abril Azocar Guzman, Sarath Menon, Tilmann Hickel, Stefan SandfeldAbstract
The reuse of atomistic simulation data is often limited by heterogeneous formats, incomplete metadata, and a lack of standardized representations of workflows and provenance. Here we present an ontology-based infrastructure for representing and integrating atomistic simulation data as a knowledge graph. The approach combines domain ontologies with a software framework that enables data capture both from existing datasets and directly from simulation workflows at the point of generation. Heterogeneous data from multiple sources are normalized into a common, ontology-aligned representation, enabling consistent querying and analysis across datasets. We demonstrate these capabilities through the integration of grain boundary data, cross-dataset analysis of material properties, and extraction of derived thermodynamic quantities from existing simulations. In addition, workflows are represented in a machine-readable form, enabling both forward provenance tracking and partial reconstruction of computational procedures. The resulting knowledge graph contains over 750,000 triples describing nearly 8,000 computational samples. This work provides a practical framework for improving the findability, interoperability, and reuse of atomistic simulation data.
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URL
https://arxiv.org/abs/2604.06230
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Concept frustration: Aligning human concepts and machine representations
2026-03-31 12:17:21Enrico Parisini, Christopher J. Soelistyo, Ahab Isaac, Alessandro Barp, Christopher R. S. BanerjiAbstract
Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. We develop task-aligned similarity measures that detect concept frustration between supervised concept-based models and unsupervised representations derived from foundation models, and show that the phenomenon is detectable in task-aligned geometry while conventional Euclidean comparisons fail. Under a linear-Gaussian generative model we derive a closed-form expression for Bayes-optimal concept-based classifier accuracy, decomposing predictive signal into known-known, known-unknown and unknown-unknown contributions and identifying analytically where frustration affects performance. Experiments on synthetic data and real language and vision tasks demonstrate that frustration can be detected in foundation model representations and that incorporating a frustrating concept into an interpretable model reorganises the geometry of learned concept representations, to better align human and machine reasoning. These results suggest a principled framework for diagnosing incomplete concept ontologies and aligning human and machine conceptual reasoning, with implications for the development and validation of safe interpretable AI for high-risk applications.
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将人类可解释的概念与现代机器学习系统学习的内部表征相对齐，始终是可解释人工智能的核心挑战。我们引入了一个几何框架，用于比较基于监督学习的人类概念与从基础模型嵌入中提取的无监督中间表征。受科学发现中概念飞跃作用的启发，我们形式化了“概念挫折”的概念：当未观测概念在已知概念间诱导出无法在现有本体论中保持一致的关系时，由此产生的矛盾。我们开发了任务对齐的相似性度量方法，能够检测基于监督概念模型与源自基础模型的无监督表征之间的概念挫折，并证明该现象在任务对齐几何中可被检测，而传统的欧几里得比较则失效。在线性高斯生成模型下，我们推导了贝叶斯最优基于概念分类器准确率的闭式解，将预测信号分解为已知-已知、已知-未知和未知-未知的贡献，并分析性地识别出挫折影响性能的具体环节。合成数据及真实语言与视觉任务的实验表明，基础模型表征中可检测到挫折现象，且将挫折概念纳入可解释模型会重组已学概念表征的几何结构，从而更好地对齐人类与机器的推理。这些结果提出了一种诊断不完整概念本体论、对齐人类与机器概念推理的原则性框架，对高风险应用中安全可解释人工智能的开发与验证具有重要意义。
URL
https://arxiv.org/abs/2603.29654
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PRISM: A Multi-View Multi-Capability Retail Video Dataset for Embodied Vision-Language Models
2026-03-31 05:29:22Amirreza Rouhi, Parikshit Sakurikar, Satya Sai Reddy, Narsimha Menga, Anirudh Govil, Sri Harsha Chittajallu, Rajat Aggarwal, Anoop Namboodiri, Sashi ReddiAbstract
A critical gap exists between the general-purpose visual understanding of state-of-the-art physical AI models and the specialized perceptual demands of structured real-world deployment environments. We present PRISM, a 270K-sample multi-view video supervised fine-tuning (SFT) corpus for embodied vision-language-models (VLMs) in real-world retail environments. PRISM is motivated by a simple observation - physical AI systems fail not because of poor visual recognition, but because they do not understand space, physical dynamics and embodied action well enough to operate reliably in the world. To this end, PRISM is grounded in a novel three-dimensional knowledge ontology that spans spatial knowledge, temporal and physical knowledge, and embodied action knowledge. It covers 20+ capability probes across four evaluation dimensions - Embodied Reasoning (ER), Common Sense (CS), Spatial Perception (SP), and Intuitive Physics (IP), and to our knowledge, PRISM is the first dataset to instantiate all three knowledge dimensions within a single real-world deployment domain. The corpus captures data from egocentric, exocentric and 360° viewpoints across five supermarket locations and includes open-ended, chain-of-thought, and multiple-choice supervision. At 4 fps, PRISM spans approximately 11.8M video frames and approximately 730M tokens, placing it among the largest domain-specific video SFT corpora. Fine-tuning on PRISM reduces the error rate across all 20+ probes by 66.6% over the pre-trained baseline, with significant gains in embodied action understanding where the accuracy improves by 36.4%. Our results suggest that ontology-structured, domain specific SFT can meaningfully strengthen embodied VLMs for real-world settings. The PRISM dataset and more details are available at this https URL
Abstract (translated)
当前最先进的物理AI模型在通用视觉理解能力与结构化真实部署环境的专业化感知需求之间存在关键差距。我们提出了PRISM——一个面向真实零售环境中具身视觉语言模型（VLMs）的27万样本多视角视频监督微调（SFT）语料库。PRISM的提出源于一个简单观察：物理AI系统的失败并非由于视觉识别能力不足，而是因为它们对空间、物理动力学及具身动作的理解不足以支撑在现实世界中的可靠运作。为此，PRISM基于一个新颖的三维知识本体论构建，该本体论涵盖空间知识、时序与物理知识以及具身动作知识。它在四个评估维度——具身推理（ER）、常识（CS）、空间感知（SP）和直觉物理（IP）——中覆盖了20余项能力探测。据我们所知，PRISM是首个在单一真实部署领域内实现全部三个知识维度的数据集。该语料库采集自五个超市场景中的第一人称、第三人称及360°视角数据，并包含开放式、思维链及多项选择等多种监督形式。以4帧/秒的帧率计算，PRISM涵盖约1180万视频帧及约7.3亿token，使其成为规模最大的领域专用视频SFT语料库之一。在PRISM上进行微调后，所有20余项探测任务的错误率相比预训练基线降低66.6%，其中具身动作理解领域的准确率提升尤为显著，达到36.4%。我们的结果表明，基于本体论结构化、领域专用的SFT能够有效增强具身VLMs在真实场景中的性能。PRISM数据集及更多详情可通过此https URL获取。
URL
https://arxiv.org/abs/2603.29281
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UniAI-GraphRAG: Synergizing Ontology-Guided Extraction, Multi-Dimensional Clustering, and Dual-Channel Fusion for Robust Multi-Hop Reasoning
2026-03-31 01:39:20Jie Wang, Honghua Huang, Xi Ge, Jianhui Su, Wen Liu, Shiguo LianAbstract
Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While existing GraphRAG frameworks have made progress in structural knowledge organization, they still have limitations in cross-industry adaptability, community report integrity, and retrieval performance. This paper proposes UniAI-GraphRAG, an enhanced framework built upon open-source GraphRAG. The framework introduces three core innovations: (1) Ontology-Guided Knowledge Extraction that uses predefined Schema to guide LLMs in accurately identifying domain-specific entities and relations; (2) Multi-Dimensional Community Clustering Strategy that improves community completeness through alignment completion, attribute-based clustering, and multi-hop relationship clustering; (3) Dual-Channel Graph Retrieval Fusion that balances QA accuracy and performance through hybrid graph and community retrieval. Evaluation results on MultiHopRAG benchmark show that UniAI-GraphRAG outperforms mainstream open source solutions (this http URL) in comprehensive F1 scores, particularly in inference and temporal queries. The code is available at this https URL.
Abstract (translated)
检索增强生成（RAG）系统在复杂推理、多跳查询和领域特定问答中面临显著挑战。尽管现有GraphRAG框架已在结构化知识组织方面取得进展，但在跨行业适应性、社区报告完整性和检索性能方面仍存在局限。本文提出UniAI-GraphRAG，一个基于开源GraphRAG构建的增强框架。该框架引入三项核心创新：（1）本体论引导的知识提取，利用预定义模式引导大语言模型准确识别领域特定实体与关系；（2）多维社区聚类策略，通过对齐补全、基于属性的聚类和多跳关系聚类提升社区完整性；（3）双通道图检索融合，通过混合图检索与社区检索平衡问答准确性与性能。在MultiHopRAG基准测试中，UniAI-GraphRAG的综合F1分数优于主流开源方案（此http链接），尤其在推理与时序查询任务中表现突出。代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.25152
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A Multi-Agent Rhizomatic Pipeline for Non-Linear Literature Analysis
2026-03-30 12:06:25Julio C. Serrano. Joonas Kevari, Rumy NarayanAbstract
Systematic literature reviews in the social sciences overwhelmingly follow arborescent logics -- hierarchical keyword filtering, linear screening, and taxonomic classification -- that suppress the lateral connections, ruptures, and emergent patterns characteristic of complex research landscapes. This research note presents the Rhizomatic Research Agent (V3), a multi-agent computational pipeline grounded in Deleuzian process-relational ontology, designed to conduct non-linear literature analysis through 12 specialized agents operating across a seven-phase architecture. The system was developed in response to the methodological groundwork established by (Narayan2023), who employed rhizomatic inquiry in her doctoral research on sustainable energy transitions but relied on manual, researcher-driven exploration. The Rhizomatic Research Agent operationalizes the six principles of the rhizome -- connection, heterogeneity, multiplicity, asignifying rupture, cartography, and decalcomania -- into an automated pipeline integrating large language model (LLM) orchestration, dual-source corpus ingestion from OpenAlex and arXiv, SciBERT semantic topography, and dynamic rupture detection protocols. Preliminary deployment demonstrates the system's capacity to surface cross-disciplinary convergences and structural research gaps that conventional review methods systematically overlook. The pipeline is open-source and extensible to any phenomenon zone where non-linear knowledge mapping is required.
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社会科学中的系统性文献综述几乎普遍遵循树状逻辑——即层级化关键词筛选、线性筛查与分类学归类——这类方法会压制复杂研究领域中特有的横向关联、断裂点与涌现模式。本研究提出块茎研究智能体V3（Rhizomatic Research Agent V3），这是一个基于德勒兹过程关系本体论的多智能体计算流水线，旨在通过横跨七个阶段架构的12个专业化智能体实现非线性文献分析。该系统是针对（Narayan2023）所建立的方法论基础而开发的，后者在其关于可持续能源转型的博士研究中采用了块茎探究法，但依赖研究者手动驱动探索。块茎研究智能体将块茎的六项原则——连接性、异质性、多元性、无意义断裂、制图与转印——转化为自动化流水线，整合了大语言模型（LLM）编排、来自OpenAlex与arXiv的双源语料库摄入、SciBERT语义地形图以及动态断裂检测协议。初步部署表明，该系统能够揭示出传统综述方法系统性地忽视的跨学科聚合点与结构性研究空白。该流水线采用开源设计，可扩展至任何需要非线性知识映射的现象领域。
URL
https://arxiv.org/abs/2603.28336
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GEAKG: Generative Executable Algorithm Knowledge Graphs
2026-03-30 00:42:48Camilo Chac\'on Sartori, Jos\'e H. Garc\'ia, Andrei Voicu Tomut, Christian BlumAbstract
In the context of algorithms for problem solving, procedural knowledge -- the know-how of algorithm design and operator composition -- remains implicit in code, lost between runs, and must be re-engineered for each new domain. Knowledge graphs (KGs) have proven effective for organizing declarative knowledge, yet current KG paradigms provide limited support for representing procedural knowledge as executable, learnable graph structures. We introduce \textit{Generative Executable Algorithm Knowledge Graphs} (GEAKG), a class of KGs whose nodes store executable operators, whose edges encode learned composition patterns, and whose traversal generates solutions. A GEAKG is \emph{generative} (topology and operators are synthesized by a Large Language Model), \emph{executable} (every node is runnable code), and \emph{transferable} (learned patterns generalize zero-shot across domains). The framework is domain-agnostic at the engine level: the same three-layer architecture and Ant Colony Optimization (ACO)-based learning engine can be instantiated across domains, parameterized by a pluggable ontology (\texttt{RoleSchema}). Two case studies -- sharing no domain-specific framework code -- provide concrete evidence for this framework hypothesis: (1)~Neural Architecture Search across 70 cross-dataset transfer pairs on two tabular benchmarks, and (2)~Combinatorial Optimization, where knowledge learned on the Traveling Salesman Problem transfers zero-shot to scheduling and assignment domains. Taken together, the results support that algorithmic expertise can be explicitly represented, learned, and transferred as executable knowledge graphs.
Abstract (translated)
在问题求解算法的背景下，过程性知识——即算法设计与操作符组合的实操技能——仍隐含在代码中，在运行间丢失，且必须为每个新领域重新设计。知识图谱（KGs）已被证明能有效组织陈述性知识，但当前KG范式在将过程性知识表示为可执行、可学习的图结构方面支持有限。我们提出了\textit{生成式可执行算法知识图谱}（GEAKG），这是一类KG，其节点存储可执行操作符，边编码习得的组合模式，遍历即可生成解决方案。GEAKG是\textit{生成式}的（拓扑结构与操作符由大语言模型合成）、\textit{可执行}的（每个节点均为可运行代码）且\textit{可迁移}的（习得模式能跨领域零样本泛化）。该框架在引擎层面是领域无关的：相同的三层架构与基于蚁群优化（ACO）的学习引擎可实例化于不同领域，通过可插拔的本体（\texttt{RoleSchema}）进行参数化。两项不共享任何领域特定框架代码的案例研究为该框架假设提供了具体证据：(1) 在两个表格基准测试上进行的跨70个数据集迁移配对的神经架构搜索，以及(2) 组合优化，其中在旅行商问题上学到的知识可零样本迁移至调度与分配领域。综合而言，这些结果支持了算法专长可被显式表示为可执行知识图谱、从中学习并实现迁移的结论。
URL
https://arxiv.org/abs/2603.27922
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Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoring
2026-03-28 20:50:21Jakub Mas{\l}owski, Jaros{\l}aw A. ChudziakAbstract
Large Language Models (LLMs) are being increasingly used as autonomous agents in complex reasoning tasks, opening the niche for dialectical interactions. However, Multi-Agent systems implemented with systematically unconstrained systems systematically undergo semantic drift and logical deterioration and thus can hardly be used in providing ethical tutoring where a precise answer is required. Current simulation often tends to degenerate into dialectical stagnation, the agents degenerate into recursive concurrence or circular arguments. A critical challenge remains: how to enforce doctrinal fidelity without suppressing the generative flexibility required for dialectical reasoning? To address this niche, we contribute the Heterogeneous Debate Engine (HDE), a cognitive architecture that combines Identity-Grounded Retrieval-Augmented Generation (ID-RAG) for doctrinal fidelity and Heuristic Theory of Mind for strategic opponent modeling. Our evaluation shows that architectural heterogeneity is a crucial variable to stability: contrary doctrinal initializations (e.g., Deontology vs. Utilitarianism) have increased the Argument Complexity Scores of students by an order of magnitude, over baselines. These findings validate the effectiveness of ID-RAG and Heuristic ToM as architectural requirements in maintaining high-fidelity (adversarial) pedagogy.
Abstract (translated)
大型语言模型（LLMs）正日益作为自主智能体应用于复杂推理任务，从而开辟了辩证交互的细分领域。然而，采用系统化无约束方式构建的多智能体系统会系统性地经历语义漂移与逻辑退化，因此难以在需要精确答案的伦理辅导场景中应用。当前模拟往往退化为辩证停滞——智能体陷入循环共识或循环论证。一个关键挑战依然存在：如何在保障教义忠实度的同时，不抑制辩证推理所需的生成灵活性？针对这一细分领域，我们提出了异构辩论引擎（HDE），这是一种结合身份锚定检索增强生成（ID-RAG）以确保教义忠实度，以及启发式心智理论以进行策略性对手建模的认知架构。评估表明，架构异构性是稳定性的关键变量：相较于基线，对立教义初始化（如义务论与功利主义）使学生的论证复杂度评分提升了一个数量级。这些发现验证了ID-RAG与启发式心智理论作为维持高保真（对抗性）教学法的架构要求的有效性。
URL
https://arxiv.org/abs/2603.27404
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Generalizable task-oriented object grasping through LLM-guided ontology and similarity-based planning
2026-03-27 13:39:57Hao Chen, Takuya Kiyokawa, Weiwei Wan, Kensuke HaradaAbstract
Task-oriented grasping (TOG) is more challenging than simple object grasping because it requires precise identification of object parts and careful selection of grasping areas to ensure effective and robust manipulation. While recent approaches have trained large-scale vision-language models to integrate part-level object segmentation with task-aware grasp planning, their instability in part recognition and grasp inference limits their ability to generalize across diverse objects and tasks. To address this issue, we introduce a novel, geometry-centric strategy for more generalizable TOG that does not rely on semantic features from visual recognition, effectively overcoming the viewpoint sensitivity of model-based approaches. Our main proposals include: 1) an object-part-task ontology for functional part selection based on intuitive human commands, constructed using a Large Language Model (LLM); 2) a sampling-based geometric analysis method for identifying the selected object part from observed point clouds, incorporating multiple point distribution and distance metrics; and 3) a similarity matching framework for imitative grasp planning, utilizing similar known objects with pre-existing segmentation and grasping knowledge as references to guide the planning for unknown targets. We validate the high accuracy of our approach in functional part selection, identification, and grasp generation through real-world experiments. Additionally, we demonstrate the method's generalization capabilities to novel-category objects by extending existing ontological knowledge, showcasing its adaptability to a broad range of objects and tasks.
Abstract (translated)
任务导向抓取（TOG）相较于简单物体抓取更具挑战性，因为它需要精准识别物体部件并审慎选择抓取区域，以确保操作的有效性与鲁棒性。尽管近期方法已通过训练大规模视觉-语言模型来整合部件级物体分割与任务感知抓取规划，但其在部件识别与抓取推理方面的不稳定性，限制了跨多样化物体与任务的泛化能力。针对此问题，我们提出一种新颖的、以几何为中心的策略，以实现更具泛化能力的TOG，该方法不依赖视觉识别中的语义特征，从而有效克服了基于模型方法对视角敏感的缺陷。我们的核心提案包括：1）基于直观人类指令的功能部件选择物体-部件-任务本体论，通过大语言模型（LLM）构建；2）一种基于采样的几何分析方法，用于从观测点云中识别所选物体部件，整合多种点分布与距离度量；3）一种用于模仿式抓取规划的相似性匹配框架，利用具有预存分割与抓取知识的相似已知物体作为参考，引导未知目标的规划。我们通过真实实验验证了该方法在功能部件选择、识别与抓取生成方面的高精度。此外，通过扩展现有本体论知识，我们展示了该方法对新颖类别物体的泛化能力，凸显其适应广泛物体与任务的灵活性。
URL
https://arxiv.org/abs/2603.26412
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DAOnt: A Formal Ontology for EU Data Act Compliance
2026-03-27 10:03:40Sheyla Leyva-S\'anchez, Fabian Linde, Meem Arafat Manab, Mar\'ia Poveda-Villal\'on, V\'ictor Rodr\'iguez-DoncelAbstract
The EU Data Act establishes comprehensive rules governing data access and sharing across business-to-consumer (B2C), business-to-business (B2B), and business-to-government (B2G) contexts. This paper presents a comprehensive ontology for the EU Data Act, enabling reasoning over data sharing agreements through machine-readable representations. The DAOnt ontology reuses elements from three established ontologies, LKIF-Core, ODRL, and DPV, to capture the normative structure of the Data Act. The ontology captures the main concepts and relationships in the Regulation, and it also operationalises three articles to facilitate compliance checking: Article 4(1) (B2C user access rights), Article 8(6) (B2B trade secret exceptions) and Article 19(2)(a) (B2G competitive use prohibitions). The ontology supports compliance checking through SPARQL queries that return obligations, permissions, and prohibitions, allowing organisations to verify whether data-sharing agreements meet the requirements of the EU Data Act and to assess conditions such as FRAND obligations. By representing key legal concepts in RDF, our work helps bridge the gap between the legal provisions of the Data Act and their computational interpretation. The complete ontology, along with example instances and queries, is available online.
Abstract (translated)
URL
https://arxiv.org/abs/2604.16386
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Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings
2026-03-26 00:38:38Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert, Annika M\"utze, Marvin Keller, Sparsh Tiwari, Georgii Mikriukov, Diedrich Wolter, Jae Hee Lee, Matthias RottmannAbstract
Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to explain, verify, and align the semantic hierarchies induced by a VLM over a given set of child classes. First, we extract a binary hierarchy by agglomerative clustering of class centroids and name internal nodes by dictionary-based matching to a concept bank. Second, we quantify plausibility by comparing the extracted tree against human ontologies using efficient tree- and edge-level consistency measures, and we evaluate utility via explainable hierarchical tree-traversal inference with uncertainty-aware early stopping (UAES). Third, we propose an ontology-guided post-hoc alignment method that learns a lightweight embedding-space transformation, using UMAP to generate target neighborhoods from a desired hierarchy. Across 13 pretrained VLMs and 4 image datasets, our method finds systematic modality differences: image encoders are more discriminative, while text encoders induce hierarchies that better match human taxonomies. Overall, the results reveal a persistent trade-off between zero-shot accuracy and ontological plausibility and suggest practical routes to improve semantic alignment in shared embedding spaces.
Abstract (translated)
URL
https://arxiv.org/abs/2603.26798
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Probing Ethical Framework Representations in Large Language Models: Structure, Entanglement, and Methodological Challenges
2026-03-24 19:01:29Weilun Xu, Alexander Rusnak, Frederic KaplanAbstract
When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B--72B parameters. Our analysis reveals differentiated ethical subspaces with asymmetric transfer patterns -- e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice. Disagreement between deontological and utilitarian probes correlates with higher behavioral entropy across architectures, though this relationship may partly reflect shared sensitivity to scenario difficulty. Post-hoc validation reveals that probes partially depend on surface features of benchmark templates, motivating cautious interpretation. We discuss both the structural insights these methods provide and their epistemological limitations.
Abstract (translated)
URL
https://arxiv.org/abs/2603.23659
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General Machine Learning: Theory for Learning Under Variable Regimes
Abstract
We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time. This paper is a foundational and structural contribution: its goal is to define the core learning-theoretic objects required for such settings and to establish their first theorem-supporting consequences. The paper develops a regime-varying framework centered on admissible transport, protected-core preservation, and evaluator-aware learning evolution. It records the immediate closure consequences of admissibility, develops a structural obstruction argument for faithful fixed-ontology reduction in genuinely multi-regime settings, and introduces a protected-stability template together with explicit numerical and symbolic witnesses on controlled subclasses, including convex and deductive settings. It also establishes theorem-layer results on evaluator factorization, morphisms, composition, and partial kernel-level alignment across semantically commensurable layers. A worked two-regime example makes the admissibility certificate, protected evaluative core, and regime-variation cost explicit on a controlled subclass. The symbolic component is deliberately restricted in scope: the paper establishes a first kernel-level compatibility result together with a controlled monotonic deductive witness. The manuscript should therefore be read as introducing a structured learning-theoretic framework for regime-varying learning together with its first theorem-supporting layer, not as a complete quantitative theory of all learning systems.
Abstract (translated)
我们研究机制变异下的学习问题，其中学习者、其记忆状态以及评估条件均可能随时间演变。本文是一项基础性与结构性的贡献：其目标在于定义此类情境所需的核心学习理论对象，并建立其首批支持定理的推论。本文发展了一个以容许传输、保护核心保持以及感知评估者的学习演化为核心的机制变异框架。它记录了容许性的直接闭包推论，为真实多机制情境下的忠实固定本体归约发展了结构性障碍论证，并引入了保护稳定性模板，以及在受控子类（包括凸性与演绎性情境）上的显式数值与符号见证。它还在语义可通约的层间建立了关于评估者因子化、态射、复合以及部分核级对齐的定理层结果。一个双机制工作示例在受控子类上明确展示了容许性证书、保护评估核心以及机制变异成本。符号组件故意限制了范围：本文建立了首个核级兼容性结果，以及一个受控单调演绎见证。因此，本稿应被视为引入了机制变异学习的结构化学习理论框架及其首批定理支持层，而非对所有学习系统的完整量化理论。
URL
https://arxiv.org/abs/2603.23220
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EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews
2026-03-23 22:36:54Naman Ahuja, Saniya Mulla, Muhammad Ali Khan, Zaryab Bin Riaz, Kaneez Zahra Rubab Khakwani, Mohamad Bassam Sonbol, Irbaz Bin Riaz, Vivek GuptaAbstract
We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native trial PDFs while guaranteeing per-cell provenance for audit and human verification. EviSearch pairs a PDF-query agent (which preserves rendered layout and figures) with a retrieval-guided search agent and a reconciliation module that forces page-level verification when agents disagree. The pipeline is designed for high-precision extraction across multimodal evidence sources (text, tables, figures) and for generating reviewer-actionable provenance that clinicians can inspect and correct. On a clinician-curated benchmark of oncology trial papers, EviSearch substantially improves extraction accuracy relative to strong parsed-text baselines while providing comprehensive attribution coverage. By logging reconciler decisions and reviewer edits, the system produces structured preference and supervision signals that bootstrap iterative model improvement. EviSearch is intended to accelerate living systematic review workflows, reduce manual curation burden, and provide a safe, auditable path for integrating LLM-based extraction into evidence synthesis pipelines.
Abstract (translated)
URL
https://arxiv.org/abs/2604.14165
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The Semantic Ladder: A Framework for Progressive Formalization of Natural Language Content for Knowledge Graphs and AI Systems
2026-03-23 15:59:59Lars VogtAbstract
Semantic data and knowledge infrastructures must reconcile two fundamentally different forms of representation: natural language, in which most knowledge is created and communicated, and formal semantic models, which enable machine-actionable integration, interoperability, and reasoning. Bridging this gap remains a central challenge, particularly when full semantic formalization is required at the point of data entry. Here, we introduce the Semantic Ladder, an architectural framework that enables the progressive formalization of data and knowledge. Building on the concept of modular semantic units as identifiable carriers of meaning, the framework organizes representations across levels of increasing semantic explicitness, ranging from natural language text snippets to ontology-based and higher-order logical models. Transformations between levels support semantic enrichment, statement structuring, and logical modelling while preserving semantic continuity and traceability. This approach enables the incremental construction of semantic knowledge spaces, reduces the semantic parsing burden, and supports the integration of heterogeneous representations, including natural language, structured semantic models, and vector-based embeddings. The Semantic Ladder thereby provides a foundation for scalable, interoperable, and AI-ready data and knowledge infrastructures.
Abstract (translated)
URL
https://arxiv.org/abs/2603.22136
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gUFO: A Gentle Foundational Ontology for Semantic Web Knowledge Graphs
2026-03-21 21:03:02Jo\~ao Paulo A. Almeida, Giancarlo Guizzardi, Tiago Prince Sales, Claudenir M. FonsecaAbstract
gUFO is a lightweight implementation of the Unified Foundational Ontology (UFO) suitable for Semantic Web OWL 2 DL applications. UFO is a mature foundational ontology with a rich axiomatization and that has been employed in a significant number of projects in research and industry. Moreover, it is currently in the process of standardization by the International Organization for Standardization as the ISO/IEC CD 21838-5. gUFO stands out from other foundational ontology implementations (such as those provided for BFO and DOLCE) given its unique support for a typology of types (operationalizing OntoClean guidelines), its reification patterns for intrinsic and relational aspects, and its support for situations and high-order types. gUFO provides well-founded patterns to address recurrent problems in Semantic Web knowledge graphs. In this paper, we present gUFO with its constituting categories, relations and constraints, discuss how it differs from the original UFO reference ontology, elaborate on its community adoption, and systematically position it in relation to existing OWL-based implementations of popular alternative foundational ontologies.
Abstract (translated)
gUFO是统一基础本体论（UFO）的轻量级实现，适用于语义网OWL 2 DL应用。UFO是一个成熟的基础本体论，具有丰富的公理化体系，已在众多研究和工业项目中得到应用。此外，它目前正由国际标准化组织推进标准化进程，作为ISO/IEC CD 21838-5标准。相较于其他基础本体论实现（如为BFO和DOLCE提供的实现），gUFO的独特之处在于：其对类型分类体系的独特支持（将OntoClean指南操作化）、对内在与关系方面的具体化模式，以及对情境和高阶类型的支持。gUFO提供了基于良好理论基础的解决方案，以应对语义网知识图谱中反复出现的问题。本文介绍了gUFO的构成类别、关系与约束，讨论了其与原始UFO参考本体论的差异，阐述了其社区采用情况，并系统性地将其与现有基于OWL的流行替代基础本体论实现进行了对比定位。
URL
https://arxiv.org/abs/2603.20948
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Semantic Sections: An Atlas-Native Feature Ontology for Obstructed Representation Spaces
Abstract
Recent interpretability work often treats a feature as a single global direction, dictionary atom, or latent coordinate shared across contexts. We argue that this ontology can fail in obstructed representation spaces, where locally coherent meanings need not assemble into one globally consistent feature. We introduce an atlas-native replacement object, the semantic section: a transport-compatible family of local feature representatives defined over a context atlas. We formalize semantic sections, prove that tree-supported propagation is always pathwise realizable, and show that cycle consistency is the key criterion for genuine globalization. This yields a distinction between tree-local, globalizable, and twisted sections, with twisted sections capturing locally coherent but holonomy-obstructed meanings. We then develop a discovery-and-certification pipeline based on seeded propagation, synchronization across overlaps, defect-based pruning, cycle-aware taxonomy, and deduplication. Across layer-16 atlases for Llama 3.2 3B Instruct, Qwen 2.5 3B Instruct, and Gemma 2 2B IT, we find nontrivial populations of semantic sections, including cycle-supported globalizable and twisted regimes after deduplication. Most importantly, semantic identity is not recovered by raw global-vector similarity. Even certified globalizable sections show low cross-chart signed cosine similarity, and raw similarity baselines recover only a small fraction of true within-section pairs, often collapsing at moderate thresholds. By contrast, section-based identity recovery is perfect on certified supports. These results support semantic sections as a better feature ontology in obstructed regimes.
Abstract (translated)
近期可解释性研究常将特征视为跨上下文的单一全局方向、字典原子或潜在坐标。我们认为这种本体论在阻塞表示空间中可能失效——局部一致的含义未必能整合为全局一致的特征。我们提出一种图集原生的替代对象：语义截面，即定义在上下文图集上的传输兼容的局部特征代表族。我们形式化语义截面，证明树支撑传播始终可路径实现，并表明循环一致性是真正全局化的关键判据。这区分出树局部、可全局化与扭曲截面三类，其中扭曲截面捕获局部一致但受全息阻塞的含义。随后我们开发了基于种子传播、重叠区同步、基于缺陷剪枝、循环感知分类与去重的发现认证流程。在Llama 3.2 3B Instruct、Qwen 2.5 3B Instruct与Gemma 2 2B IT的第16层图集上，我们发现非平凡的语义截面群体，包括去重后存在循环支撑的可全局化与扭曲态。最重要的是，语义同一性无法通过原始全局向量相似度恢复：即使认证的可全局化截面也呈现跨图表低带符号余弦相似度，原始相似度基线仅恢复小部分真实截面内配对，且在中等阈值时常崩溃。相比之下，基于截面的同一性恢复在认证支持集上完全准确。这些结果支持语义截面作为阻塞 regimes 中更优的特征本体论。
URL
https://arxiv.org/abs/2603.20867
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Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models
2026-03-21 06:16:56Ruixiang Liu, Zhenlong Li, Ali Khosravi KazaziAbstract
The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures still rely largely on keyword-based search with limited semantic support, which often fails to capture user intent and leads to weak retrieval performance. To address these challenges, this study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery, powered by large language models. The framework introduces a unified geospatial metadata ontology as a semantic mediation layer to align heterogeneous metadata standards across platforms and constructs a geospatial metadata knowledge graph to explicitly model datasets and their multidimensional relationships. Building on the structured representation, the framework adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer synthesis, forming an interpretable and closed-loop discovery process from user queries to results. Results from representative use cases and performance evaluation show that the framework substantially improves intent matching accuracy, ranking quality, recall, and discovery transparency compared with traditional systems. This study advances geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, providing a practical foundation for next-generation intelligent and autonomous spatial data infrastructures and contributing to the broader vision of Autonomous GIS.
Abstract (translated)
地理空间数据在体量、种类和速度上的快速增长，催生了高度分布式、异构且语义不一致的数据生态系统。现有数据目录、门户和基础设施仍主要依赖基于关键词的搜索，语义支持有限，往往难以捕捉用户意图，导致检索性能薄弱。为应对这些挑战，本研究提出一种由知识图谱驱动、基于大语言模型的多智能体智能地理空间数据发现框架。该框架引入统一的地理空间元数据本体作为语义中介层，以对齐跨平台的异构元数据标准，并构建地理空间元数据知识图谱，显式建模数据集及其多维关系。基于这一结构化表示，框架采用多智能体协同架构执行意图解析、知识图谱检索与答案合成，形成从用户查询到结果的可解释闭环发现流程。代表性用例与性能评估结果表明，相较于传统系统，该框架在意图匹配准确率、排序质量、召回率及发现透明度方面均有显著提升。本研究推动地理空间数据发现迈向更语义化、意图感知和智能化的范式，为下一代智能自主空间数据基础设施提供了实践基础，并贡献于自主GIS的宏观愿景。
URL
https://arxiv.org/abs/2603.20670
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Where can AI be used? Insights from a deep ontology of work activities
2026-03-21 03:28:39Alice Cai, Iman YeckehZaare, Shuo Sun, Vasiliki Charisi, Xinru Wang, Aiman Imran, Robert Laubacher, Alok Prakash, Thomas W. MaloneAbstract
Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's widely used O*NET occupational database. Next, we use this framework to classify descriptions of 13,275 AI software applications and a worldwide tally of 20.8 million robotic systems. Finally, we use the data about both these kinds of AI to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform. We find a highly uneven distribution of AI market value across activities, with the top 1.6% of activities accounting for over 60% of AI market value. Most of the market value is used in information-based activities (72%), especially creating information (36%), and only 12% is used in physical activities. Interactive activities include both information-based and physical activities and account for 48% of AI market value, much of which (26%) involves transferring information. These results can be viewed as rough predictions of the AI applicability for all the different work activities down to very low levels of detail. Thus, we believe this systematic framework can help predict at a detailed level where today's AI systems can and cannot be used and how future AI capabilities may change this.
Abstract (translated)
人工智能（AI）有望深刻改变工作的执行与组织方式，但我们目前仍缺乏深入框架来理解AI的应用场景。本文提供了一个全面的工作活动本体论，可帮助系统性地分析和预测AI的使用。为此，我们解构并重组了美国劳工部广泛使用的O*NET职业数据库中约2万项活动。接着，我们利用该框架对13,275项AI软件应用及全球2,080万台机器人系统进行了分类。最后，我们基于这两类AI的数据，生成了图形化展示，呈现当今全球所有AI系统预估数量与市场价值如何分布于其辅助完成的各类工作活动中。我们发现AI市场价值在活动间分布极不均衡：前1.6%的活动占据了超过60%的市场价值。大部分市场价值（72%）集中于基于信息的活动，尤其是信息创造（36%），而仅12%用于体力活动。交互式活动涵盖基于信息和体力两类活动，占AI市场价值的48%，其中26%涉及信息传递。这些结果可视为对所有不同工作活动AI适用性的粗略预测，甚至细化到极低层级。因此，我们认为这一系统性框架有助于在详细层面预测当今AI系统能否应用于特定工作，以及未来AI能力可能如何改变这一格局。
URL
https://arxiv.org/abs/2603.20619
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Can Large Multimodal Models Inspect Buildings? A Hierarchical Benchmark for Structural Pathology Reasoning
2026-03-20 17:24:46Hui Zhong, Yichun Gao, Luyan Liu, Hai Yang, Wang Wang, Haowei Zhang, Xinhu ZhengAbstract
Automated building facade inspection is a critical component of urban resilience and smart city maintenance. Traditionally, this field has relied on specialized discriminative models (e.g., YOLO, Mask R-CNN) that excel at pixel-level localization but are constrained to passive perception and worse generization without the visual understandng to interpret structural topology. Large Multimodal Models (LMMs) promise a paradigm shift toward active reasoning, yet their application in such high-stakes engineering domains lacks rigorous evaluation standards. To bridge this gap, we introduce a human-in-the-loop semi-automated annotation framework, leveraging expert-proposal verification to unify 12 fragmented datasets into a standardized, hierarchical ontology. Building on this foundation, we present \textit{DefectBench}, the first multi-dimensional benchmark designed to interrogate LMMs beyond basic semantic recognition. \textit{DefectBench} evaluates 18 state-of-the-art (SOTA) LMMs across three escalating cognitive dimensions: Semantic Perception, Spatial Localization, and Generative Geometry Segmentation. Extensive experiments reveal that while current LMMs demonstrate exceptional topological awareness and semantic understanding (effectively diagnosing "what" and "how"), they exhibit significant deficiencies in metric localization precision ("where"). Crucially, however, we validate the viability of zero-shot generative segmentation, showing that general-purpose foundation models can rival specialized supervised networks without domain-specific training. This work provides both a rigorous benchmarking standard and a high-quality open-source database, establishing a new baseline for the advancement of autonomous AI agents in civil engineering.
Abstract (translated)
自动化建筑外立面检测是城市韧性与智慧城市维护的关键环节。传统方法依赖专用判别模型（如YOLO、Mask R-CNN），虽擅长像素级定位，却受限于被动感知能力，且因缺乏对结构拓扑的视觉理解而泛化能力不足。大型多模态模型（LMMs）有望推动向主动推理的范式转变，但其在高风险工程领域的应用尚缺乏严谨的评估标准。为填补这一空白，我们提出了人机协同的半自动标注框架，通过专家提案验证机制，将12个分散数据集整合为标准化分层本体。基于此基础，我们发布首个多维基准测试\textit{DefectBench}，旨在超越基础语义识别，深入检验LMMs的认知能力。该基准从三个递进认知维度评估18个前沿LMMs：语义感知、空间定位与几何生成分割。大量实验表明，当前LMMs虽展现出卓越的拓扑意识与语义理解能力（能有效诊断"何类缺陷"与"如何形成"），却在度量级定位精度（"何处缺陷"）方面存在显著不足。尤为关键的是，我们验证了零样本几何生成分割的可行性——证明通用基础模型无需领域特定训练即可媲美专业监督网络。本研究既提供了严谨的基准测试标准与高质量开源数据库，也为自主AI智能体在土木工程领域的进步确立了新基线。
URL
https://arxiv.org/abs/2603.20148
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