YANchor-4B: Effective Long-Horizon Reasoning in O(N) Time with O(1) Memory

Abstract: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.

24 pages, 8 figures. Code: this https URL ; Model: this https URL

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2610.10118 [cs.LG]

(or arXiv:2610.10118v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.10118 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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