AI & Machine Learning · Computer Science & AI
Neural & Evolutionary Computing
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324 papers, from 4 September 2026 to 5 November 2026: 324 arXiv preprints.
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Newest papers, page 5
Peer-Relative Trust-Guided Update Screening for Robust Semi-Decentralized Federated Learning under Label-Flipping Attacks
Trust and reputation mechanisms can reduce the influence of malicious clients in federated learning (FL), but strict update filtering is difficult under non-independent and identically distributed…Sector-Ring Linear Reservoirs: Sector-Wise Decay-Timescale Allocation With State Reuse Across Candidate Settings
In reservoir computing, performance depends on whether the fixed dynamics of the reservoir suit the task, and the appropriate memory length and frequency response differ across tasks.A Spiking Ring-Attractor Architecture for Representation and Control of Revolute Robot Joints
Robotic systems with revolute joints evolve on circular state spaces, yet most control architectures represent these states using Euclidean variables that ignore this underlying geometry.Hybrid metaheuristics for architectural design optimization under small evaluation and wall-clock budgets
Architectural and structural design optimization in parametric software affords about a thousand evaluations of an expensive model for problems with tens of variables, a regime for which most…Migrate Computation, Not Data: Fault Recovery in Neuromorphic Many-Core Systems
As neuromorphic systems scale to thousands of near-memory cores, aging-induced permanent faults in neuron-update datapaths become an important obstacle to long-term deployment.Length-Independent Stability and $L_2$-Gain Certificates for Chains of Delayed Neural Modules
Networks of repeated neural modules with delayed neighbour coupling need stability guarantees for every number of modules, but delay-dependent criteria certify one network at a time and grow with…Small-scale neural simulation of Caenorhabditis elegans on Loihi-2 using NeuroML2Loihi
Neural simulation is an essential methodology for studying biological nervous systems, but it remains limited by the computational costs of repeatedly updating neural and synaptic states over many…Let Them Sleep: Recursive Self-Pruning Memory for Long-Horizon LLM Agents
Agents that run for a long time have a memory problem, and it is not the one most systems optimize for.SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synaptic weights and…Wavelength Diversity Expands the Expressive Power of Optical Computing
Universal function approximation underpins neural networks' capacity to represent nonlinear relationships, but realizing this capability in optical hardware requires engineering the computational…Algorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence
Can algorithms be learned and evolved as models, rather than repeatedly redesigned as programs?QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents.First Mathematical Runtime Analyses of Evolutionary Multitask Multi-Objective Optimization
To facilitate the solving of complex problems, evolutionary multitask optimization is regarded as a promising technique due to its capacity for cross-task knowledge transfer.Approaching Backpropagation Accuracy with Weight Perturbation on MNIST
Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change in loss, without backpropagating derivatives.Beyond the Hamming Cliff: Compact Spatial Encodings Preserving Semantic Distance in Spiking Neural Networks
By pre-processing input signals with Gray, Thermometer, Johnson and hybrid encodings before passing them to spiking neural networks running on SpiNNaker hardware, Davies et al.(2025) address the…Attention over noisy examples: generalization and memorization in the exponential Hopfield network
In Hebbian learning from examples, a Hopfield network retrieves archetypes seen only through noisy examples.A restart rule for genetic algorithms based on Schnabel census
A new adaptive restart rule for Genetic Algorithms (GAs) is considered.Upper bounds on the number of causal pieces in spiking neural networks
The input space of single-spike neural networks decomposes into causal pieces, and the number of causal pieces has been shown to provide a lower bound on the approximation error for some spiking…IWOO: Wave Optics Optimizer Improved with Hybrid Strategies for Numerical Optimization and Engineering Applications
The Wave Optics Optimizer (WOO) incorporates optical diffraction and interference into numerical optimization and has shown competitive performance in benchmarks and engineering applications.How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity
Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler.