Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP
arXiv:2604.01653v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states evolve in real time and quantifying the energy required for such transitions remains a major challenge. The...
Coupled Query-Key Dynamics for Attention
arXiv:2604.01683v1 Announce Type: new Abstract: Standard scaled dot-product attention computes scores from static, independent projections of the input. We show that evolving queries and keys \emph{jointly} through shared learned dynamics before scoring - which we call \textbf{coupled QK dynamics} -...
Volume 40, Issue 4
COMPLETE VOLUME 40, ISSUE 4 Complete Issue FRONT MATTER Front Matter ARTICLES Foreword: AI Governance at the Crossroads by Emily Rehmet & Yasameen Joulaee An American’s Guide to the EU AI Act by Margot E. Kaminski & Andrew D. Selbst...
Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error
arXiv:2604.01613v1 Announce Type: new Abstract: In reinforcement learning (RL), temporal difference (TD) errors are widely adopted for optimizing value and policy functions. However, since the TD error is defined by a bootstrap method, its computation tends to be noisy and...
Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
arXiv:2604.01730v1 Announce Type: new Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed to generate training data and validate the controllers. A meta-heuristic extended dynamic mode decomposition is...
Beyond Symbolic Solving: Multi Chain-of-Thought Voting for Geometric Reasoning in Large Language Models
arXiv:2604.00890v1 Announce Type: new Abstract: Geometric Problem Solving (GPS) remains at the heart of enhancing mathematical reasoning in large language models because it requires the combination of diagrammatic understanding, symbolic manipulation and logical inference. In existing literature, researchers have chiefly...
From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM
arXiv:2603.23520v1 Announce Type: new Abstract: Medicine is an empirical discipline refined through long-term observation and the messy, high-variance reality of clinical practice. Physicians build diagnostic and therapeutic competence through repeated cycles of application, reflection, and improvement, forming individualized methodologies. Yet...
Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
arXiv:2603.23624v1 Announce Type: new Abstract: Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless...
Perturbation: A simple and efficient adversarial tracer for representation learning in language models
arXiv:2603.23821v1 Announce Type: new Abstract: Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. However, finding representations in LMs remains an unsolved problem, in part due to a dilemma...
From AI Assistant to AI Scientist: Autonomous Discovery of LLM-RL Algorithms with LLM Agents
arXiv:2603.23951v1 Announce Type: new Abstract: Discovering improved policy optimization algorithms for language models remains a costly manual process requiring repeated mechanism-level modification and validation. Unlike simple combinatorial code search, this problem requires searching over algorithmic mechanisms tightly coupled with training...
Thinking with Tables: Enhancing Multi-Modal Tabular Understanding via Neuro-Symbolic Reasoning
arXiv:2603.24004v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a critical real-world modality, remains relatively underexplored in multimodal learning. In this paper,...
AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
arXiv:2603.23566v1 Announce Type: new Abstract: AscendC (Ascend C) operator optimization on Huawei Ascend neural processing units (NPUs) faces a two-fold knowledge bottleneck: unlike the CUDA ecosystem, there are few public reference implementations to learn from, and performance hinges on a...
Dual-Criterion Curriculum Learning: Application to Temporal Data
arXiv:2603.23573v1 Announce Type: new Abstract: Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progression. Defining meaningful difficulty assessment measures is crucial and...
Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
arXiv:2603.23578v1 Announce Type: new Abstract: Efficient thermal management and precise field prediction are critical for the design of advanced energy systems, including electrohydrodynamic transport, microfluidic energy harvesters, and electrically driven thermal regulators. However, the steady-state simulation of these electrothermal coupled...
LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
arXiv:2603.23584v1 Announce Type: new Abstract: Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed...
Steering Code LLMs with Activation Directions for Language and Library Control
arXiv:2603.23629v1 Announce Type: new Abstract: Code LLMs often default to particular programming languages and libraries under neutral prompts. We investigate whether these preferences are encoded as approximately linear directions in activation space that can be manipulated at inference time. Using...
Latent Algorithmic Structure Precedes Grokking: A Mechanistic Study of ReLU MLPs on Modular Arithmetic
arXiv:2603.23784v1 Announce Type: new Abstract: Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has been memorized-has been characterized in previous works as producing sinusoidal input weight distributions in transformers...
Resolving gradient pathology in physics-informed epidemiological models
arXiv:2603.23799v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental models, such as the susceptible-exposed-infected-removed (SEIR) model. However, training these hybrid networks is often unstable...
Deep Neural Regression Collapse
arXiv:2603.23805v1 Announce Type: new Abstract: Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the...
Unveiling Hidden Convexity in Deep Learning: a Sparse Signal Processing Perspective
arXiv:2603.23831v1 Announce Type: new Abstract: Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine learning tasks, including image recognition, audio processing, and language modeling. Despite this success, the non-convex...
Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
arXiv:2603.23854v1 Announce Type: new Abstract: Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability and scalable learning. Classical symbolic regression methods yield explicit analytic expressions but rely on combinatorial...
Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules
arXiv:2603.23862v1 Announce Type: new Abstract: Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In...
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
arXiv:2603.23867v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper, we study covariate shifts in which the...
Kirchhoff-Inspired Neural Networks for Evolving High-Order Perception
arXiv:2603.23977v1 Announce Type: new Abstract: Deep learning architectures are fundamentally inspired by neuroscience, particularly the structure of the brain's sensory pathways, and have achieved remarkable success in learning informative data representations. Although these architectures mimic the communication mechanisms of biological...
Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
arXiv:2603.23984v1 Announce Type: new Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions,...
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
arXiv:2603.24025v1 Announce Type: new Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only a few features, called the influential features, meaningfully define the clusters. Recovering these influential features...
Whether, Not Which: Mechanistic Interpretability Reveals Dissociable Affect Reception and Emotion Categorization in LLMs
arXiv:2603.22295v1 Announce Type: new Abstract: Large language models appear to develop internal representations of emotion -- "emotion circuits," "emotion neurons," and structured emotional manifolds have been reported across multiple model families. But every study making these claims uses stimuli signalled...
Computational Arbitrage in AI Model Markets
arXiv:2603.22404v1 Announce Type: new Abstract: Consider a market of competing model providers selling query access to models with varying costs and capabilities. Customers submit problem instances and are willing to pay up to a budget for a verifiable solution. An...
Reliable Classroom AI via Neuro-Symbolic Multimodal Reasoning
arXiv:2603.22793v1 Announce Type: new Abstract: Classroom AI is rapidly expanding from low-level perception toward higher-level judgments about engagement, confusion, collaboration, and instructional quality. Yet classrooms are among the hardest real-world settings for multimodal vision: they are multi-party, noisy, privacy-sensitive, pedagogically...