Dynamic Compression in Recurrent Networks proposes a method that lets recurrent models revisit and revise their fixed-size state through additional updates, rather than compressing all information in a single causal pass. This approach allows the model to retain lower-fidelity history and refine only the relevant parts when needed, reducing the required state size for accurate task reuse. Experiments show that dynamic compression lowers the recurrent state needed and scales better as the number of stored functions increases.
By Jyothish Pari, Ryan Bahlous-Boldi, Pulkit Agrawal
Dynamic Compression in Recurrent Networks proposes a method for recurrent models to selectively revisit and update past tokens, rather than compressing all history in a single causal pass. By allowing the model to refine its fixed-size state only when needed, it can maintain lower-fidelity information in the raw sequence and revisit it later. Experiments show that this selective re-scanning reduces the recurrent state needed for accurate task reuse and scales better as the number of stored functions increases.
arXiv:2603. 11395v3 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks.
By Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst, Luke Yang, Levin Kuhlmann, Gideon Kowadlo
arXiv:2605. 24709v2 Announce Type: replace Abstract: Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.
By Noah Farr, Aryaman Reddi, Carlo D'Eramo, Jan Peters
The paper introduces Recursive Quadrature Filters (RQFs), complex‑valued temporal filters that act as band‑pass filters within diagonal state‑space models. By making each layer’s bottom‑up input prospective through a parameter‑free two‑tap update, the authors mitigate depth‑dependent gradient attenuation in deep continuous‑time recurrent networks. Experiments on RQFs, S5, and ORGaNICs show that prospective variants match or surpass non‑prospective controls, achieving high accuracy on raw‑audio Speech Commands and the Path‑X task with few parameters.
By Shivang Rawat, Mirko Morello, Flaviano Morone, David J. Heeger
arXiv:2605. 06384v3 Announce Type: replace-cross Abstract: We introduce MinMax Recurrent Neural Cascades (MinMax RNCs), a class of recurrent neural networks built from a novel form of recurrence over the MinMax algebra.
By Alessandro Ronca
arXiv:2609.38598v1 Announce Type: new
Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from o...
By Sathya Kamesh Bhethanabhotla, Efstratios Gavves, Andr\'e Biedenkapp
Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizons, and active overwriting of stale ones. In our diagnostic suite, the strongest efficient baselines tend to solve only one side well.
arXiv:2609.36653v1 Announce Type: new
Abstract: Recurrent reasoning models have attracted growing attention for scaling test-time computation, typically by iteratively refining latent states with sha...
By Boyuan Wang, Chengyao Yu, Jiaxi Ren, Hongxin Wei, Bingyi Jing, Yuxin Tao
arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.
By Yang Meng, Zhenya Liu, Zhuokai Zhao, Yuxin Chen
arXiv:2602. 01196v2 Announce Type: replace Abstract: Recurrent neural policies are widely used in partially observable control and meta-RL tasks.
By Jin Li, Yue Wu, Mengsha Huang, Yuhao Sun, Hao He, Xianyuan Zhan
arXiv:2608. 15854v1 Announce Type: new Abstract: Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning new tasks.
By Maksim A. Kazanskii