arXiv:2608. 05416v1 Announce Type: new Abstract: Can nonlinear dynamical systems be learned through a compact linear state-space representation, without directly solving a non-convex system-identification problem?
By Liane Galanti, Devan Shah, Shlomo Fortgang, Elad Hazan
The paper introduces recirculation, an inference‑time architectural enhancement for foundation models that reduces perplexity and improves accuracy on generation and reasoning tasks without adding significant latency. Recirculation adds a specific form of recurrence, enabling the model to function as a dynamical system that tracks belief states, and is distinct from chain‑of‑thought or depth‑recurrence methods. An adaptive variant requires minimal hyperparameter tuning and achieves notable gains on the Gemma3 family, including a 23% perplexity drop and a 21% accuracy increase on GSM8k.
By Michael C. Mozer, Shoaib Ahmed Siddiqui, Danny Sawyer, Sunny Sanyal, Rosanne Liu
arXiv:2605. 11287v2 Announce Type: replace-cross Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers.
By Jevon Twitty, Vinh Pham, Nitiwith Rotchanarak, Viresh Pati, Yubin Kim, Shihao Yang, Jiecheng Lu
The paper introduces a framework for aligning the inductive bias of linear time‑invariant State Space Models (SSMs) with task‑specific spectral characteristics. By formalizing the bias through an SSM‑induced kernel and showing its spectrum is governed by the model’s frequency response, the authors propose Task‑Dependent Initialization (TDI), a fast power‑spectrum matching method. Experiments on synthetic data, one‑layer SSMs, and deep SSMs across real‑world benchmarks demonstrate that TDI improves data‑efficient generalization when the task’s spectral structure differs from the default SSM bias.
By Qiyu Chen, Guozhang Chen
arXiv:2602. 09530v2 Announce Type: replace-cross Abstract: We introduce AutoSpec, a neural network framework for discovering iterative spectral algorithms for large-scale numerical linear algebra and numerical optimization.
By Zihang Liu, Oleg Balabanov, Yaoqing Yang, Michael W. Mahoney
arXiv:2606. 24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states.
By Klaus Schertler, Xiomara Runge, Andrea Ceni, David Kappel, Claudio Gallicchio