The field of safety-critical systems is moving towards the development of more robust and efficient methods for ensuring safety and reliability in complex systems. Researchers are exploring new approaches to controller synthesis, model learning, and formal verification to address the challenges posed by uncertainty, nondeterminism, and stochasticity in these systems. A key direction is the integration of machine learning and formal methods to improve the scalability and generalizability of safety controllers. Another important trend is the development of formal frameworks for modeling and analyzing distributed real-time systems, which enables the specification, verification, and synthesis of dependable systems operating under uncertainty. Noteworthy papers include:

• Universal Safety Controllers with Learned Prophecies, which introduces an approximation algorithm for USC synthesis that addresses computational limitations via learning.
• Formal Foundations for Controlled Stochastic Activity Networks, which provides a rigorous foundation for the specification, verification, and synthesis of dependable systems operating under uncertainty.
• Achieving Safe Control Online through Integration of Harmonic Control Lyapunov-Barrier Functions with Unsafe Object-Centric Action Policies, which proposes a method for combining HCLBFs with any given robot policy to turn an unsafe policy into a safe one with formal guarantees.
• Model Learning for Adjusting the Level of Automation in HCPS, which presents a model-based framework that enables design-time exploration of safe shared-control strategies in human-automation systems.
• Synthesis of Safety Specifications for Probabilistic Systems, which develops a new approach that supports more general temporal properties expressed in PCTL.