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 9
Evolving Hybrid Quantum-Classical Architectures for Image Classification
Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the choice of quantum…Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift
Deploying a pretrained controller exposes it to conditions that are absent from its training data.The Investment Acceleration Principle Revisited by means of a Neural Network
The investment acceleration principle is a heuristic for modelling investment time series out of consumption time series.Parallel Time-Aligned Spiking Self-Attention for Consistent Integer-Valued Training and Spike-Driven Inference
Integer-valued leaky integrate-and-fire (I-LIF) neurons and spike firing approximation (SFA) reduce temporal training cost by representing spike trains as firing counts and normalized firing rates…FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution
LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing.Distribution Matching Evolutionary Algorithms for Rare Event Sampling
A novel discovery is one which is both useful and surprising: a generative model's output is a useful discovery if it has a low probability of being generated (it's surprising) and a high reward…AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation
Large Language Models (LLMs) are increasingly used for automated algorithm design.Inherited Learning in an Artificial Ecology: How Controls and Update Allocation Shape Benefits
Learning can improve an individual's behavior, yet a population risks losing that experience whenever individuals die and are replaced.LESS: Lightweight Evolutionary Supernet Search in Minutes
Low-cost NAS must both explore high-performing architectures and identify them reliably, yet reducing evaluation cost often weakens the fidelity of candidate comparisons.Controllable Stochastic Quantization Encoding for Adversarially Robust Spiking Neural Networks
Spiking Neural Networks (SNNs) have attracted increasing attention due to their impressive temporal dynamics, energy efficiency, and brain-inspired mechanisms.Continual Reinforcement Learning with Neuroevolution
Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet consistently achieved a good balance between…TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design
Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure.Spiking neural networks for streaming qubit readout
Fast and accurate qubit-state assignment is essential for feedback, calibration, and error correction in quantum processors.SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents
Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorithm discovery, yet existing systems are largely memoryless: each run explores from…Evolutionary Giant Tour for CVRP using NSE and ML Heuristic]{Evolutionary Giant Tour approach for CVRP using Node Shift Encoding and Machine Learning repair heuristic
The Capacitated Vehicle Routing Problem (CVRP) remains a central concern in logistics research, as route optimisation under rigid capacity and fleet restrictions directly affects operational…Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization
Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorithms.T-Router: Learning Thalamic Routing for Reasoning with Parameter-Efficient Reinforcement Learning
Parameter-efficient reinforcement learning aims to improve reasoning with a compact trainable interface to a pretrained model.Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes
How search strategies evolve in finite, depletable landscapes remains a question in foraging theory.Null-model treatment of the sensory-motor boundary changes an evolutionary connectome comparison
Randomised copies of a connectome are the usual baseline for asking whether measured wiring matters, and the answer depends on what the randomisation preserves.An Island-Based Parallel Biased Random-Key Genetic Algorithm for the Three-Dimensional Trailer Loading Problem
The Three-Dimensional Trailer Loading Problem (3D-TLP) involves determining the optimal placement and orientation of heterogeneous items within the confined space of a trailer while maximizing…