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In this talk, I introduce a quantitative framework for studying large mean-field particle systems through conditional particle distributions. Functional inequalities for such systems are established, where a key step involves constructing a martingale on the space of probability measures. These inequalities yield near-optimal concentration and relaxation estimates for gradient-type particle systems. Finally, I discuss several future directions, with particular emphasis on applications to the convergence of path-dependent mean-field control problems.