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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From DNA Design to DNA Slimming: Auditable Agentic Discovery of a Deletion-Only Designer
Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted activity.Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling
Spiking neural networks (SNNs) offer low-energy sequence modeling through sparse, event-driven computation.Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers.Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning
End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers of a neural network.Toward Controlling Biology with Language:Offline Learning of Prompt-Conditioned Interventions for Cells, Organoids, and Biobots
Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated tasks by describing what they want rather than…The Quantum Sphere: A Physically Realizable Optimization Benchmark with Provable Linear Convergence in White- and Black-Box Settings
We revisit an established Quantum Control objective, characterize its exact local metric geometry, and reposition it as a rigorous benchmark for Search and Optimization.NeuroDyn-EEG: An Interpretable Pre-trained Model for EEG Based on Neural Dynamics
Clinical scalp electroencephalography (EEG) offers a noninvasive window into neural dynamics of neuropsychiatric disorders.Where Does Randomness Matter in Neural Cellular Automata?
Stochastic cell updates are often used throughout the life of a neural cellular automaton (NCA), from backpropagation through time to final rollout.Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks
We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies.Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes
Evolutionary program search driven by large language models (LLMs) has produced record-breaking constructions for open problems in combinatorics and beyond.Adaptive Rotation for iSOMA: Geometry, Benchmarking, and Noise Robustness in Variational Quantum Objectives
We study whether the coordinate dependence of the improved Self-Organizing Migrating Algorithm (iSOMA) can be reduced while retaining its inexpensive leader-directed migration mechanism.Hybrid Joint-Selective Optimization: Reduced-Space Levenberg-Marquardt Refinement of Low-Dimensional Parameters of Interest
This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable quantities is of primary interest.A neural network that maintains and retrieves memories based on context
Every day, people continuously infer situational context and adjust the way they understand and remember the world.Kolmogorov-Arnold Classifier Systems as Universal Approximators
As the input dimension $n$ grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), faces a fundamental scalability bottleneck for function approximation: both rule count and…Brain-SAD: A Brain-Inspired Safe Autonomous Driving Control Framework with Dynamic Fear-Oriented Constraint on Dual-Policy
Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to maximize the expected reward while keeping the…Simulating Synchrony Loop Networks in the Open Source RISP Neuroprocessor
Neuromorphic spiking neural networks (SNNs) offer a promising alternative to conventional deep neural networks for tasks with computational resource or data constraints.Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars
Hyperdimensional computing and vector symbolic architectures often represent quantized scalar levels by dense binary codebooks whose level-to-level similarity is intended to follow a prescribed…Behavioral Persistence and Incomplete Functional Transfer of Co-evolved Communication in Evolutionary Robotics
This work evaluates the direct transfer of a co-evolved communication protocol from a 2D simulation to a 3D physical environment, without retraining the network weights.How much of fly walking is written in the wiring?
Connectome models of the fly nerve cord generate walking-like motor rhythms, but oscillation alone does not show that the specific wiring matters.Hidden Activations are not Enough I: Knowledge Matrices as Higher Representations
We study the knowledge matrix of a trained feedforward network as a higher representation of its inputs.