AI & Machine Learning · Computer Science & AI
Neural & Evolutionary Computing
The newest papers in Neural & Evolutionary Computing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
324 papers, from 4 September 2026 to 5 November 2026: 324 arXiv preprints.
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Newest papers, page 6
Design and Analysis of Temporal Representations in Neural Networks
The ability to represent temporal information is often hypothesized to underlie the efficiency and learning capabilities of biological neural networks.Controlling the latency in temporal coded spiking models for neuromorphic datasets
Exploiting temporal information in datasets with high temporal dependencies (such as spoken keyword recognition) by spiking models is still an open problem.When the implementation matters more than the algorithm: NSGA-II and other multi-objective optimizers in engineering design tools
Engineering design tools increasingly embed optimizers as ready-made components that users select by algorithm name, while operator settings and bound, integer and duplicate handling stay fixed…A Time-Variant Spiking Neuron from a Unified State-Dynamics Perspective
Spiking neural networks (SNNs) have increasingly incorporated rich neuronal dynamics to enhance temporal processing.Collective Benefits and Efficiency in Coupled Echo State Networks
Simple aggregation of independent responses ("wisdom of crowds") generally improves collective performance through statistical facilitation, but interaction among members can sometimes hinder group…Neural Dimensional Analysis: Foundations of Dimensionless and Scale-Consistent Computation
Modern Transformers rely on many scale-control mechanisms, including normalization, residual scaling, attention temperatures, loss weighting, and adaptive updates.Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially.Same Function, Different Trajectory: No Standard Optimizer Is Equivariant to Transformer Reparameterization
We show that two Transformers representing the same function can follow different training trajectories.Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine
The Continuous Thought Machine (CTM) is a recurrent network that attends to inputs and makes predictions based on the synchronization between its neurons.Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science
We argue that tacit knowing is better modelled as an operating regime of one memory substrate than as a kind of content.CIPHEUR: LLM-Guided Structural Search Control for Conflict-Graph Optimization
Conflict-graph search can stagnate despite effective operators when interventions fail to address the conflicts blocking progress.naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation
We present an overview of R package called \texttt{naspaclust}, a tool for spatial fuzzy clustering improvement by hybridising the Fuzzy Geographically Weighted Clustering (FGWC) with…DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidates.Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning
Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misaligned with neural events.FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT
Although spiking neural networks (SNNs) show promise in energy-efficient computing through event-driven computation and stateful temporal processing, their recurrent membrane dynamics render their…FENAS: A Few-Shot Meta-Ensembling Framework for Efficient Neural Architecture Search with Synergized Zero-Cost Proxies
Efficient neural architecture search (NAS) requires accurate performance estimation under limited evaluation budgets.CoFlu-Brain: An fMRI Connectivity Foundation Model with Cofluctuation-Based Pretraining
Functional magnetic resonance imaging (fMRI) commonly characterizes large-scale brain organization through functional connectivity (FC).REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming
Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety training.How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility
When several predictions use the same internal representation, information about some targets can remain easier to recover than information about others.PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation
Generating an image with a diffusion or flow-matching model typically involves integrating a velocity field for hundreds of sequential steps.