Kushvinth M. focused on enhancing the physics accuracy and stability of the google-deepmind/torax repository by addressing core computational issues in scientific simulations. He corrected the use of the local major radius (R_major) within geometry, collisionality, collision time, and transport calculations, ensuring that simulations relied on accurate, context-specific values rather than global defaults. Working primarily in Python and leveraging skills in data analysis and numerical methods, Kushvinth also reverted RLti normalization to align with the intended physics model. These targeted improvements restored consistency in critical gradient transport and ohmic heat source calculations, reducing the risk of simulation errors and improving downstream reliability.