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Methods to simulate physics systems simultaneously across a range of temperatures provide a natural way to study thermodynamic phases, phase transitions, and criticality in many systems. These multi-canonical methods already represent a very promising approach for upcoming lattice field theory (LFT) calculations, and ongoing research is working towards achieving state-of-the-art applications. Recently, additional momentum has been generated by new connections to machine learning (ML) methods. This workshop aims to bring together the community of researchers working on developments in multi-canonical methods, both within LFT and in other domains, with the objective of cross-pollinating ideas and identifying future directions for this field. Key topics to be discussed include density-of-states methods, nested sampling, parallel tempering, out-of-equilibrium simulation, and connections to flow and diffusion ML models.