{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/141014"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/141014","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Artificial General Intelligence (AGI)-Native Wireless Systems: Digital Twins and World Models for Beyond 6G Networks","abstract":"Building next-generation wireless systems that can reliably support physical artificial intelligence (AI) agents (e.g., robots, autonomous vehicles, etc.) requires advanced levels of intelligence beyond today's state-of-art. On the one hand, the 6G vision of AI-native networks typically relies on standard AI methods (e.g., neural networks) that perform poorly in non-stationary, real-world environments. On the other hand, physical AI agents still lack the capability to generalize and adapt in unforeseen scenarios that appear in real-world settings. As a result, today's AI-native wireless systems and, in turn, their physical AI agents remain far from being autonomous and fall short in terms of quality-of-service. To address this limitation, this dissertation aims to revisit and redefine the concept of AI-native wireless systems, equipping them with common sense capabilities necessary to transform them into artificial general intelligence (AGI)-native systems. This transformation promises to revolutionize wireless systems by enabling unprecedented levels of cognitive wireless intelligence. Notably, such intelligence provides networks with the reasoning, planning, and complex inference needed to operate in dynamic, real-world environments. This envisioned new generation of networks is driven by a cognitive brain architecture that is founded on three components: A perception module, a world model, and an action-planning component. Towards realizing these components, first, we show how the perception module can be built through abstracting real-world elements into generalizable representations. Then, these representations are used to form a world model, founded on principles of causality and hyper-dimensional computing. Subsequently, we design intent-driven and objective-driven planning methods that can maneuver the network to take its actions. Central to this architecture, world models offer a structured approach for mirroring the physical world into a digital counterpart over the network. Cheif among these counterparts are the digital twins (DTs) of physical AI agents. With this interconnection to world models, DTs offer a gateway to instill common sense from the network directly into these agents. Nevertheless, enabling such solution requires addressing novel wireless challenges. Chief among those challenges is preserving the synchronization of DTs and world models with the physical world. To address this challenge, we propose a rigorous decentralized framework that decomposes these world models and their DTs over the network edge. In particular, we pose an optimization problem that aims to minimize the synchronization delays of smaller-scale world models and their associated DTs at the edge, while ensuring their interoperability in terms of association and resource allocation. To solve this problem, we propose an optimal transport theory algorithm that ensures the optimal average synchronization time of the world models, while satisfying the synchronization intensity requirements of the DTs. Results show that synchronization delays can be reduced up to 25 % in comparison to the standard signal-to noise ratio (SNR) association benchmark. Accordingly, the decentralized DTs and world models are then leveraged to drive reasoning back into the physical AI agents in the real world. This reasoning allows the physical AI agents to generalize when facing unforeseen scenarios, thereby enabling a revolutionary test-time scaling law for physical AI agents. In particular, this novel scaling law builds on the first principle of active inference and extends action selection to incorporate inference-driven reasoning that scales the feed-forward policy in unforeseen scenarios. Nevertheless, this decision-making process is formulated as a partially observable Markov decision process (POMDP) that renders an intractable inference problem. To obtain a tractable solution for this POMDP, this problem is solved through a variational Bayesian approach that unifies perception, planning, action, and learning under the minimization of variational and expected free energy. Results showcase how the proposed framework yields AI agents that can act, reason, learn, and maintain generalizable performance in dynamic environments. As continual learning (CL) abilities emerge while updating the DT models with these unforeseen scenarios, we further design a deep CL solution to enable synchronized model updates for physical AI agents. In particular, we propose a novel CL solution that preserves the accuracy and synchronization of the evolving DTs at the edge. To limit the de-synchronization gap arising during the DT model update, this update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered historical episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential historical episodes, an elastic weight consolidation (EWC) technique that continually regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Simulation results show that the proposed solution can achieve an accuracy of 90% while guaranteeing a minimal de-synchronization time. Therefore, this dissertation is expected to shape the future of DTs and world models as enablers of AGI over future networks. Ultimately, this dissertation serves as a blueprint to drive the next generation of wireless networks and its autonomous physical AI agents in the 6G and beyond era.","abstract_html":"Building next-generation wireless systems that can reliably support physical artificial intelligence (AI) agents (e.g., robots, autonomous vehicles, etc.) requires advanced levels of intelligence beyond today's state-of-art. On the one hand, the 6G vision of AI-native networks typically relies on standard AI methods (e.g., neural networks) that perform poorly in non-stationary, real-world environments. On the other hand, physical AI agents still lack the capability to generalize and adapt in unforeseen scenarios that appear in real-world settings. As a result, today's AI-native wireless systems and, in turn, their physical AI agents remain far from being autonomous and fall short in terms of quality-of-service. To address this limitation, this dissertation aims to revisit and redefine the concept of AI-native wireless systems, equipping them with common sense capabilities necessary to transform them into artificial general intelligence (AGI)-native systems. This transformation promises to revolutionize wireless systems by enabling unprecedented levels of cognitive wireless intelligence. Notably, such intelligence provides networks with the reasoning, planning, and complex inference needed to operate in dynamic, real-world environments. This envisioned new generation of networks is driven by a cognitive brain architecture that is founded on three components: A perception module, a world model, and an action-planning component. Towards realizing these components, first, we show how the perception module can be built through abstracting real-world elements into generalizable representations. Then, these representations are used to form a world model, founded on principles of causality and hyper-dimensional computing. Subsequently, we design intent-driven and objective-driven planning methods that can maneuver the network to take its actions. Central to this architecture, world models offer a structured approach for mirroring the physical world into a digital counterpart over the network. Cheif among these counterparts are the digital twins (DTs) of physical AI agents. With this interconnection to world models, DTs offer a gateway to instill common sense from the network directly into these agents. Nevertheless, enabling such solution requires addressing novel wireless challenges. Chief among those challenges is preserving the synchronization of DTs and world models with the physical world. To address this challenge, we propose a rigorous decentralized framework that decomposes these world models and their DTs over the network edge. In particular, we pose an optimization problem that aims to minimize the synchronization delays of smaller-scale world models and their associated DTs at the edge, while ensuring their interoperability in terms of association and resource allocation. To solve this problem, we propose an optimal transport theory algorithm that ensures the optimal average synchronization time of the world models, while satisfying the synchronization intensity requirements of the DTs. Results show that synchronization delays can be reduced up to 25 % in comparison to the standard signal-to noise ratio (SNR) association benchmark. Accordingly, the decentralized DTs and world models are then leveraged to drive reasoning back into the physical AI agents in the real world. This reasoning allows the physical AI agents to generalize when facing unforeseen scenarios, thereby enabling a revolutionary test-time scaling law for physical AI agents. In particular, this novel scaling law builds on the first principle of active inference and extends action selection to incorporate inference-driven reasoning that scales the feed-forward policy in unforeseen scenarios. Nevertheless, this decision-making process is formulated as a partially observable Markov decision process (POMDP) that renders an intractable inference problem. To obtain a tractable solution for this POMDP, this problem is solved through a variational Bayesian approach that unifies perception, planning, action, and learning under the minimization of variational and expected free energy. Results showcase how the proposed framework yields AI agents that can act, reason, learn, and maintain generalizable performance in dynamic environments. As continual learning (CL) abilities emerge while updating the DT models with these unforeseen scenarios, we further design a deep CL solution to enable synchronized model updates for physical AI agents. In particular, we propose a novel CL solution that preserves the accuracy and synchronization of the evolving DTs at the edge. To limit the de-synchronization gap arising during the DT model update, this update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered historical episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential historical episodes, an elastic weight consolidation (EWC) technique that continually regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Simulation results show that the proposed solution can achieve an accuracy of 90% while guaranteeing a minimal de-synchronization time. Therefore, this dissertation is expected to shape the future of DTs and world models as enablers of AGI over future networks. Ultimately, this dissertation serves as a blueprint to drive the next generation of wireless networks and its autonomous physical AI agents in the 6G and beyond era.","abstract_has_math":false,"creators":["Hashash, Omar"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Saad, Walid"],"committee_members":["Dhillon, Harpreet Singh","Reed, Jeffrey H.","Gracanin, Denis","Stilwell, Daniel J."],"year":2026,"date_issued":"2026-01-27","date_published":"2026-01-27","updated_at":"2026-07-24T05:56:26Z","subjects":["Artificial general intelligence","world model","digital twins","active inference","test-time scaling law"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45509"],"render_values":[{"text":"vt_gsexam:45509","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/141014","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Saad, Walid"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Dhillon, Harpreet Singh","Reed, Jeffrey H.","Gracanin, Denis","Stilwell, Daniel J."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Hashash, Omar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-28T09:00:08Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-28T09:00:08Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-27"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial general intelligence","world model","digital twins","active inference","test-time scaling law"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45509"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/141014"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Building next-generation wireless systems that can reliably support physical artificial intelligence (AI) agents (e.g., robots, autonomous vehicles, etc.) requires advanced levels of intelligence beyond today's state-of-art. On the one hand, the 6G vision of AI-native networks typically relies on standard AI methods (e.g., neural networks) that perform poorly in non-stationary, real-world environments. On the other hand, physical AI agents still lack the capability to generalize and adapt in unforeseen scenarios that appear in real-world settings. As a result, today's AI-native wireless systems and, in turn, their physical AI agents remain far from being autonomous and fall short in terms of quality-of-service. To address this limitation, this dissertation aims to revisit and redefine the concept of AI-native wireless systems, equipping them with common sense capabilities necessary to transform them into artificial general intelligence (AGI)-native systems. This transformation promises to revolutionize wireless systems by enabling unprecedented levels of cognitive wireless intelligence. Notably, such intelligence provides networks with the reasoning, planning, and complex inference needed to operate in dynamic, real-world environments. This envisioned new generation of networks is driven by a cognitive brain architecture that is founded on three components: A perception module, a world model, and an action-planning component. Towards realizing these components, first, we show how the perception module can be built through abstracting real-world elements into generalizable representations. Then, these representations are used to form a world model, founded on principles of causality and hyper-dimensional computing. Subsequently, we design intent-driven and objective-driven planning methods that can maneuver the network to take its actions. Central to this architecture, world models offer a structured approach for mirroring the physical world into a digital counterpart over the network. Cheif among these counterparts are the digital twins (DTs) of physical AI agents. With this interconnection to world models, DTs offer a gateway to instill common sense from the network directly into these agents. Nevertheless, enabling such solution requires addressing novel wireless challenges. Chief among those challenges is preserving the synchronization of DTs and world models with the physical world. To address this challenge, we propose a rigorous decentralized framework that decomposes these world models and their DTs over the network edge. In particular, we pose an optimization problem that aims to minimize the synchronization delays of smaller-scale world models and their associated DTs at the edge, while ensuring their interoperability in terms of association and resource allocation. To solve this problem, we propose an optimal transport theory algorithm that ensures the optimal average synchronization time of the world models, while satisfying the synchronization intensity requirements of the DTs. Results show that synchronization delays can be reduced up to 25 % in comparison to the standard signal-to noise ratio (SNR) association benchmark. Accordingly, the decentralized DTs and world models are then leveraged to drive reasoning back into the physical AI agents in the real world. This reasoning allows the physical AI agents to generalize when facing unforeseen scenarios, thereby enabling a revolutionary test-time scaling law for physical AI agents. In particular, this novel scaling law builds on the first principle of active inference and extends action selection to incorporate inference-driven reasoning that scales the feed-forward policy in unforeseen scenarios. Nevertheless, this decision-making process is formulated as a partially observable Markov decision process (POMDP) that renders an intractable inference problem. To obtain a tractable solution for this POMDP, this problem is solved through a variational Bayesian approach that unifies perception, planning, action, and learning under the minimization of variational and expected free energy. Results showcase how the proposed framework yields AI agents that can act, reason, learn, and maintain generalizable performance in dynamic environments. As continual learning (CL) abilities emerge while updating the DT models with these unforeseen scenarios, we further design a deep CL solution to enable synchronized model updates for physical AI agents. In particular, we propose a novel CL solution that preserves the accuracy and synchronization of the evolving DTs at the edge. To limit the de-synchronization gap arising during the DT model update, this update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered historical episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential historical episodes, an elastic weight consolidation (EWC) technique that continually regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Simulation results show that the proposed solution can achieve an accuracy of 90% while guaranteeing a minimal de-synchronization time. Therefore, this dissertation is expected to shape the future of DTs and world models as enablers of AGI over future networks. Ultimately, this dissertation serves as a blueprint to drive the next generation of wireless networks and its autonomous physical AI agents in the 6G and beyond era."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Next-generation telecommunication networks (e.g., 6G cellular systems) and artificial intelligence (AI) systems are evolving towards agentic frameworks that autonomously interact with the physical world. While the current generation of AI has shown tremendous impact in fields like language, mathematics, and coding, real-world autonomous agents (e.g., robots, autonomous vehicles, etc.) and telecommunication systems still fall short in showing similar competing performance. To address this limitation, a shift towards AI architectures that support reasoning, planning, and complex inference to deal with the dynamic nature of real-world environments is necessary. This dissertation explores how the intersection of digital twins (DTs) and world models plays a role in enabling these new architectures. In essence, a world model is an internal representation of an environment that encodes its states, dynamics, and uncertainty, enabling an agent to predict outcomes, plan actions, and reason about future scenarios. Notably, integrating world models into next-generation networks provides a unique opportunity to develop a common sense understanding of \"how the world works.\" This ability is a cornerstone for dealing with the countless, unforeseen scenarios that agents encounter in the real world. Effectively, it enables next-generation networks to generalize beyond their training domain and drive new levels of network intelligence. Nevertheless, to enable world models over the network, telecommunication systems should still acquire core cognitive abilities such as perception, abstraction, and analogy. To provide these missing cognitive abilities and close the loop, we present the first cognitive brain architecture tailored to a telecommunication system. Subsequently, we elucidate the design of the cognitive modules embedded into this brain architecture. Ultimately, this cognitive architecture serves as a foundation for transitioning towards artificial general intelligence (AGI)-native networks in the beyond 6G era. More broadly, this advanced level of intelligence further expands beyond the dimensions of the network to augment its autonomous agents. In particular, DTs mirror the physical states of autonomous agents into these world models. The network thus enables agents to reason about their environment and adapt to unforeseen conditions. To this end, DTs are designed from first principles to enable test-time scaling for these agents. Accordingly, algorithms for decentralized world models and continual learning agents at the network edge are proposed. Overall, this dissertation lays the foundations of DTs and world models that promise to advance the intelligence levels of autonomous agents and telecommunication systems in the beyond 6G era."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Artificial General Intelligence (AGI)-Native Wireless Systems: Digital Twins and World Models for Beyond 6G Networks"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Saad, Walid"],"dc:contributor.committeemember":["Dhillon, Harpreet Singh","Reed, Jeffrey H.","Gracanin, Denis","Stilwell, Daniel J."],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Hashash, Omar"],"dc:date.accessioned":["2026-01-28T09:00:08Z"],"dc:date.available":["2026-01-28T09:00:08Z"],"dc:date.issued":["2026-01-27"],"dc:description.abstract":["Building next-generation wireless systems that can reliably support physical artificial intelligence (AI) agents (e.g., robots, autonomous vehicles, etc.) requires advanced levels of intelligence beyond today's state-of-art. On the one hand, the 6G vision of AI-native networks typically relies on standard AI methods (e.g., neural networks) that perform poorly in non-stationary, real-world environments. On the other hand, physical AI agents still lack the capability to generalize and adapt in unforeseen scenarios that appear in real-world settings. As a result, today's AI-native wireless systems and, in turn, their physical AI agents remain far from being autonomous and fall short in terms of quality-of-service. To address this limitation, this dissertation aims to revisit and redefine the concept of AI-native wireless systems, equipping them with common sense capabilities necessary to transform them into artificial general intelligence (AGI)-native systems. This transformation promises to revolutionize wireless systems by enabling unprecedented levels of cognitive wireless intelligence. Notably, such intelligence provides networks with the reasoning, planning, and complex inference needed to operate in dynamic, real-world environments. This envisioned new generation of networks is driven by a cognitive brain architecture that is founded on three components: A perception module, a world model, and an action-planning component. Towards realizing these components, first, we show how the perception module can be built through abstracting real-world elements into generalizable representations. Then, these representations are used to form a world model, founded on principles of causality and hyper-dimensional computing. Subsequently, we design intent-driven and objective-driven planning methods that can maneuver the network to take its actions. Central to this architecture, world models offer a structured approach for mirroring the physical world into a digital counterpart over the network. Cheif among these counterparts are the digital twins (DTs) of physical AI agents. With this interconnection to world models, DTs offer a gateway to instill common sense from the network directly into these agents. Nevertheless, enabling such solution requires addressing novel wireless challenges. Chief among those challenges is preserving the synchronization of DTs and world models with the physical world. To address this challenge, we propose a rigorous decentralized framework that decomposes these world models and their DTs over the network edge. In particular, we pose an optimization problem that aims to minimize the synchronization delays of smaller-scale world models and their associated DTs at the edge, while ensuring their interoperability in terms of association and resource allocation. To solve this problem, we propose an optimal transport theory algorithm that ensures the optimal average synchronization time of the world models, while satisfying the synchronization intensity requirements of the DTs. Results show that synchronization delays can be reduced up to 25 % in comparison to the standard signal-to noise ratio (SNR) association benchmark. Accordingly, the decentralized DTs and world models are then leveraged to drive reasoning back into the physical AI agents in the real world. This reasoning allows the physical AI agents to generalize when facing unforeseen scenarios, thereby enabling a revolutionary test-time scaling law for physical AI agents. In particular, this novel scaling law builds on the first principle of active inference and extends action selection to incorporate inference-driven reasoning that scales the feed-forward policy in unforeseen scenarios. Nevertheless, this decision-making process is formulated as a partially observable Markov decision process (POMDP) that renders an intractable inference problem. To obtain a tractable solution for this POMDP, this problem is solved through a variational Bayesian approach that unifies perception, planning, action, and learning under the minimization of variational and expected free energy. Results showcase how the proposed framework yields AI agents that can act, reason, learn, and maintain generalizable performance in dynamic environments. As continual learning (CL) abilities emerge while updating the DT models with these unforeseen scenarios, we further design a deep CL solution to enable synchronized model updates for physical AI agents. In particular, we propose a novel CL solution that preserves the accuracy and synchronization of the evolving DTs at the edge. To limit the de-synchronization gap arising during the DT model update, this update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered historical episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential historical episodes, an elastic weight consolidation (EWC) technique that continually regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Simulation results show that the proposed solution can achieve an accuracy of 90% while guaranteeing a minimal de-synchronization time. Therefore, this dissertation is expected to shape the future of DTs and world models as enablers of AGI over future networks. Ultimately, this dissertation serves as a blueprint to drive the next generation of wireless networks and its autonomous physical AI agents in the 6G and beyond era."],"dc:description.abstractgeneral":["Next-generation telecommunication networks (e.g., 6G cellular systems) and artificial intelligence (AI) systems are evolving towards agentic frameworks that autonomously interact with the physical world. While the current generation of AI has shown tremendous impact in fields like language, mathematics, and coding, real-world autonomous agents (e.g., robots, autonomous vehicles, etc.) and telecommunication systems still fall short in showing similar competing performance. To address this limitation, a shift towards AI architectures that support reasoning, planning, and complex inference to deal with the dynamic nature of real-world environments is necessary. This dissertation explores how the intersection of digital twins (DTs) and world models plays a role in enabling these new architectures. In essence, a world model is an internal representation of an environment that encodes its states, dynamics, and uncertainty, enabling an agent to predict outcomes, plan actions, and reason about future scenarios. Notably, integrating world models into next-generation networks provides a unique opportunity to develop a common sense understanding of \"how the world works.\" This ability is a cornerstone for dealing with the countless, unforeseen scenarios that agents encounter in the real world. Effectively, it enables next-generation networks to generalize beyond their training domain and drive new levels of network intelligence. Nevertheless, to enable world models over the network, telecommunication systems should still acquire core cognitive abilities such as perception, abstraction, and analogy. To provide these missing cognitive abilities and close the loop, we present the first cognitive brain architecture tailored to a telecommunication system. Subsequently, we elucidate the design of the cognitive modules embedded into this brain architecture. Ultimately, this cognitive architecture serves as a foundation for transitioning towards artificial general intelligence (AGI)-native networks in the beyond 6G era. More broadly, this advanced level of intelligence further expands beyond the dimensions of the network to augment its autonomous agents. In particular, DTs mirror the physical states of autonomous agents into these world models. The network thus enables agents to reason about their environment and adapt to unforeseen conditions. To this end, DTs are designed from first principles to enable test-time scaling for these agents. Accordingly, algorithms for decentralized world models and continual learning agents at the network edge are proposed. Overall, this dissertation lays the foundations of DTs and world models that promise to advance the intelligence levels of autonomous agents and telecommunication systems in the beyond 6G era."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45509"],"dc:identifier.uri":["https://hdl.handle.net/10919/141014"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Artificial general intelligence","world model","digital twins","active inference","test-time scaling law"],"dc:title":["Artificial General Intelligence (AGI)-Native Wireless Systems: Digital Twins and World Models for Beyond 6G Networks"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-24T05:56:26Z"}