On the Use of Survival Selection Methods for Evolutionary Diversity Optimisation

MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC

Combining Trained Models in Reinforcement Learning

Training Non-Differentiable Networks via Optimal Transport

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance

Benchmarking local Hebbian learning rules for memory storage and prototype extraction

Robust volatility updates for Hierarchical Gaussian Filtering

Spiking Sequence Machines and Transformers

Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts

Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation

Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks

Attractor FCM

Physical Foundation Models: Fixed hardware implementations of large-scale neural networks

When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry

Learning to Forget: Continual Learning with Adaptive Weight Decay

Causal Learning with Neural Assemblies

NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning

Text-Utilization for Encoder-dominated Speech Recognition Models

EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming

Analysis and Explainability of LLMs Via Evolutionary Methods

Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI

SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution

Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs

MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks

Learn&Drop: Fast Learning of CNNs based on Layer Dropping

Architecture-Induced Recoverability Bias in Differentiable Symbolic Regression

Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen

Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction

HubRouter: A Pluggable Sub-Quadratic Routing Primitive for Hybrid Sequence Models

A Co-Evolutionary Theory of Human-AI Coexistence: Mutualism, Governance, and Dynamics in Complex Societies

LTBs-KAN: Linear-Time B-splines Kolmogorov-Arnold Networks

Multi-Task Optimization over Networks of Tasks

Geometric Monomial (GEM): a family of rational 2N-differentiable activation functions

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning

Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling

Distributional Value Estimation Without Target Networks for Robust Quality-Diversity

EvoJail: Evolutionary Diverse Jailbreak Prompt Generation for Large Language Models

Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification

Large Language Models Exhibit Normative Conformity

Prototype-Grounded Concept Models for Verifiable Concept Alignment

ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset

What Makes a Bacterial Model a Good Reservoir Computer? Predicting Performance from Separability and Similarity

Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks

Why Fine-Tuning Encourages Hallucinations and How to Fix It

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning

Structure as Computation: Developmental Generation of Minimal Neural Circuits

NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning

Neural architectures for resolving references in program code

Diffusion Language Models for Speech Recognition

A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot

On the Use of Evolutionary Optimization for the Dynamic Chance Constrained Open-Pit Mine Scheduling Problem

Analog Optical Inference on Million-Record Mortgage Data

Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation

Does Dimensionality Reduction via Random Projections Preserve Landscape Features?

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models

Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems

Beyond LLMs, Sparse Distributed Memory, and Neuromorphics <A Hyper-Dimensional SRAM-CAM "VaCoAl" for Ultra-High Speed, Ultra-Low Power, and Low Cost>

SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression

Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training

Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds

Retinal Cyst Detection from Optical Coherence Tomography Images

TurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution

Universal statistical signatures of evolution in artificial intelligence architectures

Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks

Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit

Evolutionary Token-Level Prompt Optimization for Diffusion Models

Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis

A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need

Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization

OpenCLAW-P2P v7.0-P2PCLAW: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review v7.0 -- Mathematical Corrections & Ecosystem Developments Edition

An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing

Selectivity and Shape in the Design of Forward-Forward Goodness Functions

Efficient Disruption of Criminal Networks through Multi-Objective Genetic Algorithms

DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis

EvoForest: A Novel Machine-Learning Paradigm via Open-Ended Evolution of Computational Graphs

Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation

Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization

Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces

QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models

Optimized Architectures for Kolmogorov-Arnold Networks

AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging

Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems

Efficient Vector Symbolic Architectures from Histogram Recovery

Language Models Learn Universal Representations of Numbers and Here's Why You Should Care

A Practitioner's Guide to Kolmogorov-Arnold Networks

Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles

PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems

HiPreNets: High-Precision Neural Networks through Progressive Training

Machine Learning as Iterated Belief Change a la Darwiche and Pearl

Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning

Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents

Learning Evolution via Optimization Knowledge Adaptation

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers

P1-KAN: an effective Kolmogorov-Arnold network with application to hydraulic valley optimization

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