The field of valued constraint satisfaction problems and fair allocation is experiencing significant developments, with a focus on improving the efficiency and scalability of local search methods and allocating resources in a fair and efficient manner. Recent work has explored the limitations of greedy local search and the importance of considering physical constraints in task allocation. Notably, researchers have made progress in establishing the existence of maximal EF1 allocations for various graph structures and have developed novel task allocation frameworks that incorporate multitasking. The study of tractable graph structures in EFX orientation has also led to a deeper understanding of the algorithmic landscape, highlighting the importance of bipartiteness and treewidth in determining tractability. Noteworthy papers include: the construction of a sparse and oriented VCSP that demonstrates the slowness of greedy local search. A significant extension of the result on maximal EF1 allocations to any graph with monotone valuations. A novel task allocation framework for multitasking robots that leverages weighted MAX-SAT and greedy heuristics. The exploration of tractable graph structures in EFX orientation, which has led to a clearer understanding of the algorithmic landscape.