During December 2025, Vaibhav Verma developed BlockedKV attention for CausalLM models in the quic/efficient-transformers repository, focusing on scalable long-sequence inference. He implemented block-wise key/value cache processing, anchored by an online SoftMax and updates to custom PyTorch operations, enabling more efficient and accurate attention computations. The feature was integrated end-to-end, including from_pretrained initialization, ONNX export, and parameterization through qaic_config, with a PyTorch transform for passing configuration. Vaibhav validated the solution with targeted tests, demonstrating measurable performance and scalability improvements. His work leveraged deep learning, machine learning, and Python, addressing business needs for efficient, accurate inference at scale.