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.
Get the few papers worth your time in Neural & Evolutionary Computing every morning.
Follow Neural & Evolutionary Computing and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 12
Modeling quantum neural network gradient with reinforcement learning
Training quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the…Purin: A Biology-inspired Mechanism for Artificial Neural Networks
Artificial neural networks (ANNs) usually represent neural transmission with fixed trainable weights during a training batch, which omits short-term changes in synaptic efficacy.Common-Mode Collapse and Recovery in Direct Feedback Alignment
Direct feedback alignment (DFA) trains hidden layers through fixed random projections of output error.SNIP++: Fine-Grained Symbolic-Numerical Alignment for Symbolic Regression
Mathematical expressions and the numerical behavior they produce are two views of the same underlying function, and connecting them is central to scientific discovery.A Voltage-controlled MTJ-CMOS Neuron Emulating Tunable Izhikevich-Inspired Dynamics
Biological neurons exhibit diverse firing dynamics that enable adaptive and stimulus-dependent signalling, yet reproducing these dynamics in hardware has remained an enduring challenge.Distance-Residual Physics-Informed Neural Networks: A Deep Learning Framework for Differential and Partial Differential Inclusions
We introduce Distance-Residual Physics-Informed Neural Networks (DR-PINNs), a physics-informed learning framework for approximating solutions of ordinary and partial differential inclusions (DIs)…EvoTreeNAD: Genealogy-Guided Evolution for LLM-Driven Neural Architecture Discovery
AI-driven scientific discovery accelerates research by autonomously developing solutions and designs.Online Task Adaptation via Self-Organisation
Neural networks are typically adapted by computing gradients and updating model parameters.Dynamical Diversity for Reservoir Computing in Reconfigurable Nanomechanics
Physical reservoir computing uses nonlinear dynamics and a trained linear readout to process information.On Growth and Form, and Function: Reusable Regulatory Handles Control Phenotypic Variation
How phenotypic transformations are implemented by changes in underlying regulatory dynamics remains a central question in developmental biology.Boolean threshold functions, neuron capacity, and memory retrieval
How much information can a single neuron remember?A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics
Understanding the neurobiological mechanisms underlying self-referential cognition and baseline self-consciousness remains a fundamental challenge in computational neuroscience.Activation-Flexible ANN-to-SNN Conversion with Finite-State Markov Neurons
Most ANN-to-SNN conversion methods rely on a specific correspondence between the source activation and the spiking neuron dynamics.Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis
Modern deep neural networks treat parameters as static floating-point matrices stored in physical memory, incurring Von Neumann memory bottlenecks and representation collapse.Combining LLMs and Genetic Search for ARC-AGI-2
LLMs can generate programs for ARC-AGI-2 tasks, but the provided compute only allows a small number of attempts to generate, debug and validate solutions.An Unbounded Archive-based Transfer Strategy for Dynamic Multi-Objective Optimization with a Changing Number of Objectives
Dynamic multi-objective optimization with a variable number of objectives is difficult because objective-dimensional variations may significantly change the Pareto front and degrade algorithm…Spiking Neural Network Predicting Sequence of the External Worlds States in Model-Based Reinforcement Learning
This paper presents a spiking neural network (SNN) designed to predict the sequence of the external world states starting from the current world state.Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond
Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs.Scenario-Driven Neuroevolution: Using Models to Guide Test Generation for Games
Automatically generating test inputs for games is challenging, as test generators must master the game to reach advanced program states while also ensuring robustness against the heavy program…Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly generated atomic vector symbols and fractional…