Show HN: A 6M-token movable window on a single 46GB GPU (arxiv.org)

A groundbreaking approach has been introduced where a model, instead of undergoing retraining, remains static while leveraging a growing persistent memory of verified solutions. This innovation allows the model to achieve 100% accuracy across 180 test instances from various problem families, requiring zero generation tokens per response. By decoupling execution-bound capabilities from parameter scaling, the research demonstrates that performance can be sustained without constantly altering model parameters or incurring high computational costs. The implementation boasts a memory selection speed of 1.4 microseconds and a full reuse time of 6-23 milliseconds, significantly improving efficiency compared to current models. This methodology is especially impactful for the AI/ML community as it addresses the limitations of existing frontier models, which rely heavily on incurring costs for each new query while this new system can provide identical responses at no additional token generation cost. The capability to handle a 6,000,000-token movable window on a single 46 GB GPU exemplifies a leap in memory efficiency, surpassing the maximum limits of contemporary engines. With the release of a public testbench providing free access, the implications of this work could fundamentally alter how AI deployments leverage memory and verified reasoning, making models not only more efficient but also more reliable in generating consistent outputs.