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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RELAGRAM: Recursive Latent Graph Model for the Capacitated Vehicle Routing Problem
Latent recursion repeatedly processes internal representations through the same neural network.Compute-Optimal Scaling Laws for Spiking Language Models
Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking language models is uncharacterized at scale.The value of information and the emergence of communication in evolved e-puck robots
We study when communication emerges between autonomous agents and what role an innate action selection architecture plays.REMBGO for Evaluation-Limited Optimization: CEC2022 Benchmarking and EAF-STATCOM Generalization
Metaheuristic comparisons are often reported at evaluation budgets far larger than an expensive engineering simulation can afford.A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usually manual.DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness
Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplified by sleep state misperception in paradoxical…Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning
Regularization-based continual learning protects each parameter in proportion to an estimate of its importance, yet these estimates come from finite samples and heuristics and are used as if they…Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating
Solving high-dimensional structural model updating problems requires an algorithm capable of navigating complex, non-convex landscapes with correlated parameters.A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization
Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited.Universal Construction and Exact Self-Reproduction in Ternary McCulloch-Pitts Networks
A fixed network of McCulloch-Pitts threshold units with weights in {-1,0,1} can hold other threshold networks in its state and run them: the state is a ring of banks of records, each record a unit…EliteTable: Self-Improving Program Search for Executable Control Policies
EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fixed-capacity table of high-scoring solutions to guide new proposals, edits of existing…Evaluating the Transfer of Co-Evolved Communication from 2D to 3D Simulation
This work examines the transfer of a co-evolved communication mechanism between two robotic agents from a discrete two-dimensional (2D) simulator to a three-dimensional simulator with real physics…Relocating Nonlinearity: How a Downstream Learner Reshapes What Genetic Programming Must Evolve
Genetic programming was conceived as a way of evolving solutions: the program is the answer, and fitness is the error of its own output.The Modular CMA-ES: A Framework for Modern Evolution Strategies
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continuous black-box…Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction
Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trained.Evolutionary Architecture Search for Chlorophyll-$a$ Prediction in Lakes using Sentinel-2
Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, and the networks applied to them are typically hand-designed.Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks
Spiking neural networks (SNNs) offer sparse and event-driven computation, making them attractive for energy-constrained reinforcement learning (RL) on edge devices.Forecast Accuracy Is Not Trading Profit: Evolving Small Recurrent Networks for Stock Return Prediction
Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolation from the decision it is produced for, and a lower forecast error does not imply a…ReGraph: A Computational Account of Emergent Generalization in the "what" and "where" Dual Visual Streams
Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neuroscience.Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost.