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 4
One Mean Log-Mel Vector per Phoneme: Simple and Fast Text Injection for Speech Recognition
Text injection trains speech recognition models on unpaired text alongside paired audio.Freezing Mechanism and Topology Dependence of Information Flow Structure in Small-Scale Particle Swarm Optimization
The neighborhood topology of particle swarm optimization (PSO) determines information propagation and regulates the exploration-exploitation balance.Optimal Connectivity of Random Boolean Networks for Reservoir Computing
Reservoir computing shifts the focus from training a system's internal dynamics to designing dynamics that expose useful computational capabilities via linear projection.Sensorimotor behaviour from an untrained brain-and-nerve-cord connectome
Connectomes now map the neurons and synapses of an adult fly's brain and nerve cord, but how much of behaviour follows from the wiring is unknown.A Resource-Efficient and Precision-Scalable FPGA Accelerator for Spiking Neural Networks Using Minifloat Arithmetic
Spiking neural networks (SNNs) are well suited to event-driven edge intelligence, but the arithmetic complexity, memory footprint, and power consumption associated with conventional floating-point…Searching with Sparse Histories: Cross-Task Architecture Search from Incomplete Search Logs and Zero-Cost Proxies
Neural architecture search (NAS) is expensive, but cross-task transfer can reduce costs by reusing knowledge from previous tasks.PAFS: Perturbation-Aware Firing Scaffolds for Robust Event-Based SNNs
Event-based spiking neural networks (SNNs) encode information through temporal firing dynamics, but the same stateful computation makes their predictions sensitive to event loss, timestamp jitter…MOSHO: A Native Discrete Shark-Inspired Metaheuristic for Hardware-Aware Neural Architecture Search
Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-specific latency over discrete search spaces.AgenticEvolve: Evolutionary Program Search with Persistent Language Model Agents that Design and Run Experiments
Evolutionary program search with large language models keeps the structure of an evolutionary algorithm and uses the model as its variation operator.First hardware demonstration of a continuous-state probabilistic computer with programmable energy-defined sampling
Probabilistic computing has proved useful for combinatorial optimization, but conventional implementations rely on binary stochastic units and sigmoid-based updates.ENCL: Enabling Efficient Neuromorphic Continual Learning with Local Learning Rule and Sub-Network Freezing Strategy
Continual learning (CL) in neural networks is a promising learning approach for updating the knowledge of deployed systems based on new data encountered at run time, that have not been learned…RobustNCL: Robustifying Neuromorphic Continual Learning under Transient Faults with Lightweight Detect-and-Cure Strategy
In neural networks, continual learning (CL) is a prominent approach for enabling deployed systems to adapt to dynamic operational environments by effectively learning from new unseen data at runtime.Gate Initialisation versus Recurrent Dropout and Zoneout in Learning Long Dependencies: A Controlled Study of LSTM and GRU Networks on Synthetic Tasks
Dropout and zoneout are routinely added to long short-term memory (LSTM) and gated recurrent unit (GRU) networks, but, to our knowledge, their placements have not been crossed with gate…Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models
Neural network models are increasingly constructed from synapse-resolution connectomes, and the same circuit is now available in reconstructions from other animals and pipelines.BrainCodes: A Lightweight EEG Foundation Model over Discrete Neural Codes
Recent electroencephalography (EEG) foundation models increasingly emphasize model scale, potentially limiting their deployment on resource-constrained devices and in everyday environments.Probing Spatial and Spectral Grounding in EEG Foundation Models
EEG foundation models are advancing at an unprecedented pace, yet their evaluation is driven by downstream accuracy, which reveals little about whether their predictions rely on physiologically…Bio-Inspired Auditory Processing: Coupling Parallel Resonate-and-Fire Dynamics with Ternary Recurrent Networks for Speech Recognition
Spiking Neural Networks (SNNs) promise energy-efficient inference through sparse, event-driven computation, yet remain constrained on continuous, context-dependent tasks such as automatic speech…HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning
Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs.Multi-Objective Evolutionary Semi-Supervised Learning for Resource-Aware Smart City Classification
Smart-city applications often combine scarce labeled data with abundant unlabeled observations.Unit-Aware Differentially Private Quantum Machine Learning for Clinical and Wearable Health Data
Clinical registries and wearable sensors support early health-risk assessment, yet models trained on these records may leak sensitive information when released.