Digital twinHybrid Edge-Cloud Digital Twin for Poultry ControlarXiv — Systems and Control (eess.SY) · yesterdayModellingShared Control Between DRL and MPC for Multi-Class Transport NetworksarXiv — Systems and Control (eess.SY) · yesterdayDigital twinMulti-Viewpoint Modeling Framework for DT Integration with LLM AnalysisarXiv API — полнотекстовый поиск "digital twin" · 19hDigital twinStructured Persona Extraction for LLM-Based Digital TwinsarXiv — Computers and Society (cs.CY) · yesterdayForecastingLLM-Agent Stress Test: Feed Effects on Lexical DiversityarXiv — Multiagent Systems (cs.MA) · yesterdayModellingHow to manage behavior of multi-agent systemsХабр · yesterdayDigital twinDigital Twin for Ground-to-QEYSSat Quantum LinkarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinAligning Upper Ontologies for Defence: IES, HQDM, and BFOarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinReliability-Aware Scheduling for Digital Twin MaintenancearXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinAI disrupts glass fiber manufacturing: predictive maintenance, digital twins, and infrastructureGlobeNewswire · 3dDigital twinKMG implements digital twins at 12 fieldsdprom.kz — промышленность · 4dDigital twinDigital Twin of SWER Networks for Narrowband CommunicationarXiv API — полнотекстовый поиск "digital twin" · 4d
Twin is presented as a system that automatically constructs an executable world model for continual learning tasks like ARC-AGI-3. A coding agent recovers rules and goal from simulation and interaction alone. Replay validation occurs in a twin world model, and an action is not taken until the program reproduces every previous observed transition. Mismatches become counterexamples to repair the model.
Why it matters
Demonstrates using digital twins for automatic modeling of unknown systems, relevant for adaptive control.
A hybrid edge-cloud digital twin architecture is introduced for climate control in poultry facilities. It integrates distributed sensing, on-device state estimation, a grey-box physics-data model, and model predictive control to handle biological variability and welfare constraints.
A multi-viewpoint modeling framework is proposed for integrating and reusing digital twins. It treats integration as a cross-model consistency problem and uses LLM for compatibility analysis.
The study investigates how structuring persona information affects accuracy of LLM-based digital twins. It argues that structural organization, not information volume, is the key bottleneck. Unstructured summaries are compared with structured persona representations.
