← Back to all news
arXiv Machine Learning

Learning infinite context windows in recurrent architectures via spatial neural computing

The paper proposes a second‑order recurrent neural network that replaces traditional neuron‑to‑neuron communication with a spatially evolving field governed by discretized partial differential equations. This design creates a structured infinite‑order RNN where each state depends on its entire history, providing an unbounded receptive field while keeping the number of parameters fixed. The authors derive conditions for marginal stability that prevent vanishing or exploding gradients, and demonstrate that the architecture outperforms other recurrent models on long‑horizon benchmarks with fewer parameters.

Related stories

arXiv AI
Sep 25

ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks

The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.

By Laura Kriener, Kristin V\"olk, Ben von H\"unerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
More like this →
arXiv Machine Learning
Jun 25

Frequency Domain Reservoir Computing

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
llmsragbenchmarks
More like this →
arXiv AI
Oct 7

Continuous Memory Machines

The paper introduces the Continuous Memory Machine (CMM), a recurrent neural network that uses separate matrix-valued short‑term and long‑term memory states. Short‑term memory tracks recent neural activity with neuron‑level models for rapid computation, while long‑term memory stores information for later use; both are jointly updated by a Transformer that allows bidirectional read‑write operations. Experiments on algorithmic, in‑context learning, and recurrent reasoning tasks show that CMM outperforms many baselines and generalizes better than previous memory‑augmented networks, while maintaining interpretable attention patterns from the Continuous Thought Machine.

By Ciaran Regan, Kai Arulkumaran, Luke Darlow, Stefania Druga, Sebastian Risi, Llion Jones
llms
More like this →
arXiv AI
Jun 9

MinMax Recurrent Neural Cascades

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
More like this →
arXiv Machine Learning
Aug 19

Dynamic Compression in Recurrent Networks

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
More like this →