This paper focuses on inverse problems in multi-population aggregation models, aiming to reconstruct diffusion coefficients, advection coefficients, and interaction kernels. The authors employ the high-order variation method and introduce modifications to ensure non-negativity of solutions. They propose a novel approach called the transformative asymptotic technique to recover the diffusion coefficient, which is a pioneering method for this type of problems. The paper provides comprehensive insights into the unique identifiability aspect of inverse problems associated with multi-population aggregation models. The main contributions include: 1. **Inverse Problem Setup**: The paper discusses the setup of multi-population aggregation models and the importance of non-negativity of solutions. 2. **High-Order Variation Method**: This method is used to ensure the non-negativity of solutions and simplify nonlinear models into linear forms. 3. **Transformative Asymptotic Technique**: A novel technique for recovering the diffusion coefficient, which is applicable to parabolic systems with periodic boundary conditions. 4. **Unique Identifiability Results**: The paper presents unique identifiability conclusions for diffusion coefficients, advection coefficients, and integral kernels under specific conditions. The authors apply these techniques to verify the uniqueness of systems and provide detailed proofs for the main theorems. The paper also discusses the well-posedness of the forward problems and the conditions under which the models are well-posed.This paper focuses on inverse problems in multi-population aggregation models, aiming to reconstruct diffusion coefficients, advection coefficients, and interaction kernels. The authors employ the high-order variation method and introduce modifications to ensure non-negativity of solutions. They propose a novel approach called the transformative asymptotic technique to recover the diffusion coefficient, which is a pioneering method for this type of problems. The paper provides comprehensive insights into the unique identifiability aspect of inverse problems associated with multi-population aggregation models. The main contributions include: 1. **Inverse Problem Setup**: The paper discusses the setup of multi-population aggregation models and the importance of non-negativity of solutions. 2. **High-Order Variation Method**: This method is used to ensure the non-negativity of solutions and simplify nonlinear models into linear forms. 3. **Transformative Asymptotic Technique**: A novel technique for recovering the diffusion coefficient, which is applicable to parabolic systems with periodic boundary conditions. 4. **Unique Identifiability Results**: The paper presents unique identifiability conclusions for diffusion coefficients, advection coefficients, and integral kernels under specific conditions. The authors apply these techniques to verify the uniqueness of systems and provide detailed proofs for the main theorems. The paper also discusses the well-posedness of the forward problems and the conditions under which the models are well-posed.