Long-horizon reasoning demands access to earlier information at a manageable generation cost.
Full-history attention incurs growing storage and computation, while recurrent compression can lose precise details.
Therefore, we present YANchor-4B, a general-purpose recurrent model that preserves crucial memory as ANchors for retrieval during subsequent reasoning.
Beyond $O(N)$-time generation and $O(1)$ memory, YANchor enables effective long-horizon reasoning through its multidimensional memory mechanism.
For example, on challenging math problems, it achieves 82.93% mean pass@1 on AIME 2024--2026 and 63.64% on HMMT, substantially outperforming linear-time, constant-state counterparts, including larger models.
It also delivers several-fold higher batched long-generation throughput than Transformer and hybrid baselines on H100.
Furthermore, evaluations across dozens of benchmarks demonstrate YANchor's superiority in general-purpose capabilities.