A Conceptual Framework for Next-Generation Hadron Colliders: An Integrated Perspective on Stability, Control, and Data Challenges

Current high-energy physics colliders, such as the LHC, face fundamental operational challenges related to cryogenic stability, beam control, and data throughput that limit their scalability. This perspective paper puts forward a conceptual framework for a next-generation hadron collider that addresses these challenges through the synergistic integration of three key technological domains: High-Temperature Superconducting (HTS) magnets, AI-driven beam control systems, and on-detector neuromorphic data processing. This paper does not present new experimental data but rather synthesizes existing, yet disparate, research streams to propose a holistic design philosophy. We explore the potential of REBCO-based magnets operating in liquid nitrogen to enhance thermal resilience, discuss how reinforcement learning could offer proactive beam stabilization, and examine how neuromorphic hardware might overcome the impending data deluge. For each technological pillar, we explicitly identify critical engineering, financial, and scientific challenges that must be addressed. The primary contribution of this work is to provide a high-level, interdisciplinary blueprint intended to stimulate discussion and guide future collaborative research toward the realization of more robust, efficient, and scientifically potent colliders.