The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification
arXiv:2603.20352v1 Announce Type: new Abstract: Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrinsic regression has, in part, been driven by...
CAMA: Exploring Collusive Adversarial Attacks in c-MARL
arXiv:2603.20390v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Nevertheless, many adversarial attacks still exist to threaten various c-MARL systems. At present, the...
SymCircuit: Bayesian Structure Inference for Tractable Probabilistic Circuits via Entropy-Regularized Reinforcement Learning
arXiv:2603.20392v1 Announce Type: new Abstract: Probabilistic circuit (PC) structure learning is hampered by greedy algorithms that make irreversible, locally optimal decisions. We propose SymCircuit, which replaces greedy search with a learned generative policy trained via entropy-regularized reinforcement learning. Instantiating the...
KV Cache Optimization Strategies for Scalable and Efficient LLM Inference
arXiv:2603.20397v1 Announce Type: new Abstract: The key-value (KV) cache is a foundational optimization in Transformer-based large language models (LLMs), eliminating redundant recomputation of past token representations during autoregressive generation. However, its memory footprint scales linearly with context length, imposing critical...
Thinking in Different Spaces: Domain-Specific Latent Geometry Survives Cross-Architecture Translation
arXiv:2603.20406v1 Announce Type: new Abstract: We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited to correct model behavior at inference time without any weight updates. We learn a linear...
SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators
arXiv:2603.20410v1 Announce Type: new Abstract: Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in which future data follow the same distribution as the training set. In practice, new experimental...
Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes
arXiv:2603.20418v1 Announce Type: new Abstract: Unidirectional tapes surface roughness determines the evolution of the degree of intimate contact required for ensuring the thermoplastic molecular diffusion and the associated inter-tapes consolidation during manufacturing of composite structures. However, usual characterization of rough...
Detecting Neurovascular Instability from Multimodal Physiological Signals Using Wearable-Compatible Edge AI: A Responsible Computational Framework
arXiv:2603.20442v1 Announce Type: new Abstract: We propose Melaguard, a multimodal ML framework (Transformer-lite, 1.2M parameters, 4-head self-attention) for detecting neurovascular instability (NVI) from wearable-compatible physiological signals prior to structural stroke pathology. The model fuses heart rate variability (HRV), peripheral perfusion...
SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
arXiv:2603.20452v1 Announce Type: new Abstract: Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learning reliable temporal representations. To address this challenge, we propose SDE-HGNN, a stochastic...
Reinforcement Learning from Multi-Source Imperfect Preferences: Best-of-Both-Regimes Regret
arXiv:2603.20453v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) replaces hard-to-specify rewards with pairwise trajectory preferences, yet regret-oriented theory often assumes that preference labels are generated consistently from a single ground-truth objective. In practical RLHF systems, however, feedback...
From Data to Laws: Neural Discovery of Conservation Laws Without False Positives
arXiv:2603.20474v1 Announce Type: new Abstract: Conservation laws are fundamental to understanding dynamical systems, but discovering them from data remains challenging due to parameter variation, non-polynomial invariants, local minima, and false positives on chaotic systems. We introduce NGCG, a neural-symbolic pipeline...
Spatio-Temporal Grid Intelligence: A Hybrid Graph Neural Network and LSTM Framework for Robust Electricity Theft Detection
arXiv:2603.20488v1 Announce Type: new Abstract: Electricity theft, or non-technical loss (NTL), presents a persistent threat to global power systems, driving significant financial deficits and compromising grid stability. Conventional detection methodologies, predominantly reactive and meter-centric, often fail to capture the complex...
AE-LLM: Adaptive Efficiency Optimization for Large Language Models
arXiv:2603.20492v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse applications, yet their deployment remains challenging due to substantial computational costs, memory requirements, and energy consumption. Recent empirical studies have demonstrated that no single efficiency...
Delightful Distributed Policy Gradient
arXiv:2603.20521v1 Announce Type: new Abstract: Distributed reinforcement learning trains on data from stale, buggy, or mismatched actors, producing actions with high surprisal (negative log-probability) under the learner's policy. The core difficulty is not surprising data per se, but \emph{negative learning...
Does This Gradient Spark Joy?
arXiv:2603.20526v1 Announce Type: new Abstract: Policy gradient computes a backward pass for every sample, even though the backward pass is expensive and most samples carry little learning value. The Delightful Policy Gradient (DG) provides a forward-pass signal of learning value:...
RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization
arXiv:2603.20527v1 Announce Type: new Abstract: Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of the loss landscape . The central challenge in this field lies in balancing preconditioning effectiveness with...
Towards Practical Multimodal Hospital Outbreak Detection
arXiv:2603.20536v1 Announce Type: new Abstract: Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in outbreak investigations, its substantial costs and turnaround times limit its feasibility...
Understanding Behavior Cloning with Action Quantization
arXiv:2603.20538v1 Announce Type: new Abstract: Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and generative models. Autoregressive models like transformer have proven remarkably effective, from large language models (LLMs)...
RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
arXiv:2603.20585v1 Announce Type: new Abstract: Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world...
MKA: Memory-Keyed Attention for Efficient Long-Context Reasoning
arXiv:2603.20586v1 Announce Type: new Abstract: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference. While prior works such as Multi-Query...
Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems
arXiv:2603.20589v1 Announce Type: new Abstract: Generating data from discrete distributions is important for a number of application domains including text, tabular data, and genomic data. Several groups have recently used random $k$-satisfiability ($k$-SAT) as a synthetic benchmark for new generative...
Bayesian Learning in Episodic Zero-Sum Games
arXiv:2603.20604v1 Announce Type: new Abstract: We study Bayesian learning in episodic, finite-horizon zero-sum Markov games with unknown transition and reward models. We investigate a posterior algorithm in which each player maintains a Bayesian posterior over the game model, independently samples...
Beyond Token Eviction: Mixed-Dimension Budget Allocation for Efficient KV Cache Compression
arXiv:2603.20616v1 Announce Type: new Abstract: Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting long-context deployment. Existing token eviction methods reduce memory by discarding less important tokens, which can...
CFNN: Continued Fraction Neural Network
arXiv:2603.20634v1 Announce Type: new Abstract: Accurately characterizing non-linear functional manifolds with singularities is a fundamental challenge in scientific computing. While Multi-Layer Perceptrons (MLPs) dominate, their spectral bias hinders resolving high-curvature features without excessive parameters. We introduce Continued Fraction Neural Networks...
Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity
arXiv:2603.20645v1 Announce Type: new Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated on low-dimensional structures -- remains incomplete. This paper investigates how diffusion models learn such structured...
Exponential Family Discriminant Analysis: Generalizing LDA-Style Generative Classification to Non-Gaussian Models
arXiv:2603.20655v1 Announce Type: new Abstract: We introduce Exponential Family Discriminant Analysis (EFDA), a unified generative framework that extends classical Linear Discriminant Analysis (LDA) beyond the Gaussian setting to any member of the exponential family. Under the assumption that each class-conditional...
Breaking the $O(\sqrt{T})$ Cumulative Constraint Violation Barrier while Achieving $O(\sqrt{T})$ Static Regret in Constrained Online Convex Optimization
arXiv:2603.20671v1 Announce Type: new Abstract: The problem of constrained online convex optimization is considered, where at each round, once a learner commits to an action $x_t \in \mathcal{X} \subset \mathbb{R}^d$, a convex loss function $f_t$ and a convex constraint function...
Centrality-Based Pruning for Efficient Echo State Networks
arXiv:2603.20684v1 Announce Type: new Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, the randomly initialized reservoir often contains redundant nodes, leading to unnecessary computational overhead and reduced efficiency....
OmniPatch: A Universal Adversarial Patch for ViT-CNN Cross-Architecture Transfer in Semantic Segmentation
arXiv:2603.20777v1 Announce Type: new Abstract: Robust semantic segmentation is crucial for safe autonomous driving, yet deployed models remain vulnerable to black-box adversarial attacks when target weights are unknown. Most existing approaches either craft image-wide perturbations or optimize patches for a...
Neural Autoregressive Flows for Markov Boundary Learning
arXiv:2603.20791v1 Announce Type: new Abstract: Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial in many applications. While recent advances improve upon traditional constraint-based techniques by scoring local causal...