Q-TensorFormer is an open-source, publication-grade research architecture and inference testbed designed by Premchand Yadav. It investigates a fundamental question in efficient deep learning: Can a Transformer dynamically allocate its computational capacity, tensor rank, attention complexity, and KV-cache precision according to the marginal informational value of each token under explicit hardware constraints? Q-TensorFormer is an experimental PyTorch and Hugging Face research architecture that investigates closed-loop, information-value-driven resource allocation for Transformer inference under explicit memory, latency, bandwidth, and quality constraints. Rather than claiming that dynamic…