Haifeng Jiang maintained the edge quantization workflow in the google-ai-edge/ai-edge-torch repository by ensuring compatibility with PyTorch 2.6. He addressed a critical bug by removing unused private imports from torch.ao.quantization.pt2e.utils and locally redefining them, which preserved quantizer utility functionality after the framework upgrade. This approach prevented regressions in the quantization path and reduced upgrade risk for edge deployments. Working primarily in Python, Haifeng applied his expertise in library updates and quantization to reinforce the maintainability of the codebase. His focused engineering work contributed to stable edge deployment workflows without introducing new features during the reported period.