Toward Effective and Reliable LLM Agents via Dynamic Ontology

• URL: http://arxiv.org/abs/2608.22974v1
• Date: Mon, 24 Aug 2026 08:33:42 GMT
• Title: Toward Effective and Reliable LLM Agents via Dynamic Ontology
• Abstract summary: Large model language (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context.<n>This often results in incomplete evidence use and brittle multi-step decisions.<n>Ontologies offer a way to externalize domain concepts and relations as machine-interpretable structures.<n>We present OaK, an ontology-as-a- Kernel framework that dynamically constructs and refines task-oriented LLM agents.
• Score: 21.48160246568531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks, this leaves important semantic connections implicit. This often results in incomplete evidence use and brittle multi-step decisions. Ontologies offer a way to externalize domain concepts and relations as machine-interpretable structures, but constructing task-usable ontologies traditionally requires substantial effort from domain experts and is difficult to scale. Automatic construction is also challenging: an ontology that appears semantically plausible may not contain the relational structures needed for actual decision making. We present OaK, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents. Given task requirements and training data, OaK constructs an ontology and its knowledge graph, generates task-adaptation functions for graph reasoning, and uses judge feedback to iteratively refine both. By making relevant concepts and relations explicit, the ontology grounds knowledge retrieval and multi-step decision making. We evaluate OaK on TravelPlanner, CRMArenaPro, and ToolQA. Results show that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.

Related papers

• Knowledge-Centric Agents for Workflow Generation [73.4340391112949]
  We propose a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.<n>Our method produces with richer node diversity, more coherent structures, and higher execution success rates than existing systems.
  arXiv  Detail & Related papers  (2026-07-17T11:03:01Z)
• ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models [73.59878306788815]
  ReasoningLens is an open-source framework designed for the hierarchical visualization and diagnostic auditing of complex reasoning chains.<n> ReasoningLens addresses information necropsy by: (1) structuring traces into interactive hierarchies that separate high-level strategy from low-level execution; and (2) leveraging an agentic auditor for automated error detection and tool-augmented verification.
  arXiv  Detail & Related papers  (2026-06-22T14:28:35Z)
• Doc2AHP: Inferring Structured Multi-Criteria Decision Models via Semantic Trees with LLMs [7.026862437055361]
  We propose Doc2AHP, a novel structured inference framework guided by AHP principles.<n>We introduce a multi-agent weighting mechanism coupled with an adaptive consistency optimization strategy to ensure the numerical consistency of weight allocation.<n> Empirical results demonstrate that Doc2AHP not only empowers non-expert users to construct high-quality decision models from scratch but also significantly outperforms direct generative baselines in both logical completeness and downstream task accuracy.
  arXiv  Detail & Related papers  (2026-01-23T06:20:23Z)
• Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Management [9.901440359781732]
  Traditional knowledge graphs struggle with implicit relationship discovery and lack sufficient semantic understanding for complex question answering.<n>We introduce a unified construct--align--reason framework, the large ontology model (LOM)<n>We propose a unified three-stage training pipeline: ontology instruction fine-tuning to improve structural understanding; text-ontology grounding to strengthen node semantic encoding; and multi-task instruction tuning on ontology-language pairs with curriculum learning to enhance semantic reasoning.
  arXiv  Detail & Related papers  (2026-01-18T05:30:03Z)
• SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs [28.59157823781425]
  SEAL is a novel two-stage semantic parsing framework grounded in self-evolving agentic learning.<n> SEAL achieves state-of-the-art performance, especially in multi-hop reasoning, comparison, and aggregation tasks.<n>The results validate notable gains in both structural accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-12-04T14:52:30Z)
• RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow [19.502882116487005]
  Remote sensing imagery presents vast, inherently unstructured spatial data.<n>We propose RemoteReasoner, a unified workflow for geospatial reasoning.<n>RemoteReasoner achieves state-of-the-art (SOTA) performance across multi-granularity reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-25T13:58:11Z)
• STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking [2.355572228890207]
  StructSense is a modular, task-agnostic, open-source framework for structured information extraction built on Large Language Models.<n>It is guided by domain-specific symbolic knowledge enabling it encoded complex domain content effectively.<n>We demonstrate that StructSense can overcome both the limitations of domain sensitivity and the lack of cross-task generalizability.
  arXiv  Detail & Related papers  (2025-07-04T15:51:07Z)
• KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation [35.555200530999365]
  We introduce KAG-Thinker, which upgrade KAG to a multi-turn interactive thinking and deep reasoning framework powered by a dedicated parameter-light large language model (LLM)<n>Our approach constructs a structured thinking process for solving complex problems, enhancing the the logical coherence and contextual consistency of the reasoning process.
  arXiv  Detail & Related papers  (2025-06-21T14:58:53Z)
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)
• Automated Relational Meta-learning [95.02216511235191]
  We propose an automated relational meta-learning framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-01-03T07:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.