{"ID":2889457,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21196","arxiv_id":"2507.21196","title":"EdgeAgentX-DT: Integrating Digital Twins and Generative AI for Resilient Edge Intelligence in Tactical Networks","abstract":"We introduce EdgeAgentX-DT, an advanced extension of the EdgeAgentX framework that integrates digital twin simulations and generative AI-driven scenario training to significantly enhance edge intelligence in military networks. EdgeAgentX-DT utilizes network digital twins, virtual replicas synchronized with real-world edge devices, to provide a secure, realistic environment for training and validation. Leveraging generative AI methods, such as diffusion models and transformers, the system creates diverse and adversarial scenarios for robust simulation-based agent training. Our multi-layer architecture includes: (1) on-device edge intelligence; (2) digital twin synchronization; and (3) generative scenario training. Experimental simulations demonstrate notable improvements over EdgeAgentX, including faster learning convergence, higher network throughput, reduced latency, and improved resilience against jamming and node failures. A case study involving a complex tactical scenario with simultaneous jamming attacks, agent failures, and increased network loads illustrates how EdgeAgentX-DT sustains operational performance, whereas baseline methods fail. These results highlight the potential of digital-twin-enabled generative training to strengthen edge AI deployments in contested environments.","short_abstract":"We introduce EdgeAgentX-DT, an advanced extension of the EdgeAgentX framework that integrates digital twin simulations and generative AI-driven scenario training to significantly enhance edge intelligence in military networks. EdgeAgentX-DT utilizes network digital twins, virtual replicas synchronized with real-world e...","url_abs":"https://arxiv.org/abs/2507.21196","url_pdf":"https://arxiv.org/pdf/2507.21196v1","authors":"[\"Abir Ray\"]","published":"2025-07-28T01:42:05Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}