SEVRA saves 26–91 percent tokens during inference through selective verification without compromising accuracy, but presents longer initial solution attempts as partially more cost-effective.
VaSE achieves higher accuracy than existing sparse-attention methods at 4x KV-cache compression, thereby reducing the memory bottleneck of reasoning models.
