Jul 2026· International journal of pattern recognition and artificial intelligence· 0 citations
TL;DR
A knowledge-enhanced LLM framework for power semantic understanding and multi-agent decision learning that consistently improves semantic grounding accuracy, restoration quality, and feasible-decision rate over rule-based, text-centric RAG, and graph-retrieval multi-agent baselines.
Abstract
Power-system decision support increasingly requires the joint interpretation of heterogeneous information sources, including real-time measurements, topology states, alarm logs, operating rules, dispatch instructions, and historical restoration cases. Existing LLM-based approaches remain limited in such settings because their reasoning is often weakly grounded in power-domain knowledge and difficult to verify under physical and procedural constraints. To address this issue, we propose a knowledge-enhanced LLM framework for power semantic understanding and multi-agent decision learning. The framework first organizes heterogeneous grid information into a dynamic semantic graph, then performs graph-grounded semantic understanding, translates the inferred semantics into an executable task graph for hierarchical multi-agent collaboration, and finally applies safety-constrained verification with evidence tracing before action execution. Experiments on Grid2Op/L2RPN WCCI 2022, SMART-DS, RTS-GMLC, and PowerGym show that the proposed framework consistently improves semantic grounding accuracy, restoration quality, and feasible-decision rate over rule-based, text-centric RAG, and graph-retrieval multi-agent baselines, while exhibiting stronger robustness under partial observation. These results indicate that explicit semantic grounding and safety-aware collaborative reasoning are both necessary for reliable LLM-assisted power-system decision support.
A semantic-uncertainty-guided orchestration approach, HASSUM is introduced as a general framework for uncertainty-aware coordination in multi-agent systems and suggests that semantic uncertainty is a practical and general-purpose signal for improving robustness and trustworthiness in agentic AI systems.
John Knowlton, Aritra Guha, Risto Miikkulainen· 0 citations
Modern military decision-making demands real-time integration of structured knowledge, causal reasoning, and dynamic operational context. We introduce ARES-KG (Actionable & Reasoned Edge Support over Knowledge Graphs), a hybrid decision-support framework whose central contribution is the integration of explicit causal graph semantics—relations such as BLOCKS_ROUTE, LIMITS_MOBILITY_OF, and DELAYS, elevated to first-class queryable edges—into an LLM–KG pipeline. This design lets commanders trace multi-order operational effects (e.g., bridge destruction → mobility degradation → resupply delay → mission slippage) and answer counterfactual queries through deterministic graph traversal rather than free-form LLM speculation. Three supporting elements operationalize the central claim: a closed-loop NL→Cypher interaction layer that makes the causal layer accessible at staff tempo with full auditability; a lightweight ontology-driven CSV→Neo4j architecture suitable for hybrid edge-cloud deployment; and an eight-dimensional human–AI evaluation rubric in which one dimension, Causal Foresight, directly tests the central claim. In a synthetic brigade-level case study, ARES-KG generated transparent, explainable reasoning chains and exposed hidden multi-hop dependencies across sustainment, maneuver, fires, ISR, and C2. In a small-scale study with 10 active-duty officers spanning ranks from second Lieutenant to Colonel, an ARES-KG–enabled LLM achieved a Decision Support Score in the upper band of the sample—at the field-grade rank-group mean and above every junior officer—while producing answers in seconds rather than minutes. ARES-KG thus represents a concrete step toward next-generation human–AI collaborative command systems that augment, rather than replace, expert judgment under operational time pressure.
Constructing simulation scenarios manually is time-consuming and often depends on platform-specific modeling experience. Existing large-language-model (LLM) methods are promising for interpreting operational documents, but they still struggle with long-document parsing, incomplete platform interfaces, auditable task execution, and cross-lingual equipment-name normalization. This paper proposes a dual-channel LLM-agent framework for intelligent generation of simulation scenarios. The method standardizes Word-based scenario inputs, extracts scenario elements into a schema-constrained JSON intermediate representation, and decomposes the generation process into planning and execution. A DLL/Lua dual-channel Plan Agent assigns basic object and scenario-property operations to a C# object model, while allocating fine-grained unit, mission, and environment operations to Lua-based scripting interfaces. An Exec Agent further integrates two-stage hot-pluggable tool loading, side-effect-aware read/write-separated scheduling, and a Smart-Matcher module that combines BM25 retrieval, multilingual vector retrieval, reciprocal rank fusion, and low-confidence LLM reranking. Experiments on representative red-blue simulation scenarios show an average end-to-end generation time of 168 s, a first-round planning success rate of 92.0%, a post-Replan success rate of 100.0%, and Top-1/Top-5 equipment matching performance of 95.6% and 98.4%, respectively.
Lei Wang, Zhiqiang Fan, Yikang Song et al.· 2026 IEEE 27th China Confere...· 0 citations
Modern power systems and smart grids are often composed of fragmented and heterogeneous data silos, which lack the cohesion needed for effective cross-domain analysis. For this, this paper introduces a universal ontology framework for the operational representation of intelligent cyber-physical power systems via a unified knowledge graph and an ontology capable of cross-domain reasoning. This work focuses on bridging cyber-physical simulators as a stepping stone towards that vision. By establishing a unified semantic middleware grounded in IEC 61970 (CIM) and IEC 62351/61850 standards, this framework integrates disparate cyber and physical simulation environments, illustrated via OMNeT++ and PowerWorld, into a single knowledge graph. Evaluation across three standard power system benchmarks demonstrates sub-linear scaling in both knowledge graph size and construction time. We further validate the framework's efficacy for real-time decision support, achieving millisecond-level query performance across both domains, maintained across six cumulative structural mutations to the knowledge graph. The resulting unified knowledge graph provides a robust, scalable information corpus for autonomous smart grid operations, enabling complex analysis of real-world power systems.
Sathvik Sankaranarayanan, Michael Mandulak, Ibrahim Shahbaz et al.· 0 citations
Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.
Yu Xia, Anjun Gao, Yueyang Quan et al.· 0 citations
Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench--a deterministic benchmark of 121 edits in a capability-controlled simulator--E3 matches the strongest baseline's 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by actually running the project's real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success--its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position task-aware execution as a step toward engineering-grounded AI (EGAI)--agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.