Gaurav Arya developed partial symmetry detection for tensor operations in the EnzymeAD/Enzyme-JAX repository, focusing on enhancing correctness and efficiency in high-performance machine learning workloads. He implemented generation and propagation logic for symmetry in operations such as transpose and dot_general, optimizing performance and expanding support for symmetry-aware transformations. Using C++ and MLIR, Gaurav refactored symmetry generation, improved handling of n-dimensional transposes, and tightened aliasing checks to ensure robust operation even in complex cases. He also expanded test coverage and performed code cleanup, demonstrating depth in algorithm optimization and tensor analysis while improving maintainability and reliability of the codebase.