arXiv:2606. 26488v1 Announce Type: new Abstract: Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state.
By Pearse Jim, Steven Kolawole, Opegbemi Matthias Busoye, Glory Bagai, Virginia Smith
The paper introduces DAMP, a decay‑aware mixed‑precision quantization scheme for recurrent‑state representations in GDN and KDA language models. By identifying high‑risk channels through quantization‑error energy and decay persistence, DAMP stores these channels at higher precision while compressing the rest to INT8, achieving a 9.9‑bit average precision. Experiments on Qwen3.6‑35B and Kimi‑Linear‑48B show a 69.1% reduction in recurrent‑state storage, up to 2.01× faster state‑update kernels, and up to 10.9% lower full‑model TPOT while preserving accuracy close to the FP32 baseline.
By Tao Zhang, Jianchao Tan, Pingwei Sun, Yanqi Yu, Zixu Jiang, Yuchen Xie, Xunliang Cai, Ziqian Zeng
arXiv:2606. 28876v1 Announce Type: cross Abstract: Long-context language models often conflate two different goals: compressing history into an efficient state, and maintaining reliable long-term memory.
By Junyi Zou, Avrova Donz
The paper investigates how the dynamical regime of recurrent-depth reasoners—whether they settle, drift, or remain marginal—affects the reliability of test‑time depth. It establishes a depth‑safety condition based on per‑step displacement relative to the decoder margin, showing that operators in a settling regime can safely increase depth without degrading performance and can even improve accuracy on harder unseen tasks such as Sudoku. The authors provide empirical evidence from algorithmic tasks trained on limited data, demonstrate the impact of a terminal fixed‑point objective on depth behavior, and offer operational criteria to identify useful test‑time depth while cataloguing failure modes.
By Ivan Viakhirev, Kirill Borodin, Amirah Almutairi, Serguei Barannikov, Maxim Abramov, Grach Mkrtchian
arXiv:2608. 15797v1 Announce Type: new Abstract: KV-cache eviction caps the memory cost of long reasoning traces but is inherently lossy because the model decodes from a partial view of its history.
By Minsoo Cheong, Woosang Lim, Vincent-Daniel Yun, Sungjoo Yoo
arXiv:2603. 22016v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) often reach a correct solution before their long Chain-of-Thought trace ends, yet continue with redundant verification, repeated attempts, or unnecessary exploration that wastes computation and can even overturn the correct answer.
By Xinyan Wang, Xiaogeng Liu, Ming Pei, Chaowei Xiao