News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 30m ago Can AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models arXiv:2606.28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts. Yet existing… 36 arXiv — Machine Learning research 30m ago On the Necessity of a Liquid Substrate for Mesh Intelligence arXiv:2606.28413v1 Announce Type: new Abstract: A mesh of sovereign agents has no center: no shared clock, no shared model, and no coordinator to gather data or retrain. Its competence rests on each agent folding the projections its peers emit into a single internal state,… 8 arXiv — Machine Learning research 30m ago Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy arXiv:2606.28433v1 Announce Type: new Abstract: One goal in reinforcement learning (RL) research is to understand general-purpose sequential decision-making, using benchmark simulators as a proxy for learning in deployment settings. When running experiments, however, the goal of… 5 arXiv — Machine Learning research 30m ago Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter arXiv:2606.28441v1 Announce Type: new Abstract: Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change-point… 21 arXiv — Machine Learning research 30m ago S-GAI: Spectral Geometry-Aware Initialization for Sigmoidal MLPs -- From Dataset Geometry to Network Weights arXiv:2606.28444v1 Announce Type: new Abstract: Classical universal approximation theorems establish the expressive power of sigmoidal multilayer perceptrons, but they do not prescribe how initial weights should encode the geometry of a data distribution. We propose S-GAI, a… 31 arXiv — Machine Learning research 30m ago scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering arXiv:2606.28459v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) clustering is essential for identifying cell types, but high dimensionality, sparsity, dropout, and technical noise hinder robust expression representation and cell graph construction.… 27 arXiv — Machine Learning research 30m ago Counterfactual Residual Data Augmentation for Regression arXiv:2606.28460v1 Announce Type: new Abstract: Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augmentation in vision and language, we propose a novel… 21 arXiv — Machine Learning research 30m ago Singular Learning and Occam's Razor in Deep Monomial Networks arXiv:2606.28464v1 Announce Type: new Abstract: In the optimization of neural networks, gradient dynamics are influenced by critical points that arise from the model's architecture. These critical points occur where the Jacobian of the model's parametrization is rank-deficient,… 11 arXiv — Machine Learning research 30m ago An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations arXiv:2606.28467v1 Announce Type: new Abstract: Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use. This paper proposes an end-to-end agentic pipeline that combines deep time-series forecasting,… 11 arXiv — Machine Learning research 30m ago Modelling Emotional Memory in Children with Tensor Networks arXiv:2606.28470v1 Announce Type: new Abstract: We demonstrate how emotional valence influences the order-dependent structure of children's recognition memory: correct recall of a sequence of emotionally-valenced toys depended not just on the valence of a given toy itself, but… 7 arXiv — Machine Learning research 30m ago A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications arXiv:2606.28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensionality, memory constraints, and limited training data. These challenges arise… 38 arXiv — Machine Learning research 30m ago A Gravitational Interpretation of Fine-Tuning Reversion arXiv:2606.28525v1 Announce Type: new Abstract: Fine-tuning on harmless data can partially undo behaviors acquired earlier in training. Safety can erode under benign post-alignment updates, unlearned capabilities can re-emerge, latent traits can transfer through apparently… 27 arXiv — Machine Learning research 30m ago NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence arXiv:2606.28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost. However, existing data-driven approaches are limited in their ability to model coupled Earth system dynamics,… 9 arXiv — Machine Learning research 30m ago Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion arXiv:2606.28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures. These structures often represent aggregations (e.g., products to categories to… 29 arXiv — Machine Learning research 30m ago Geometric Measurements of the Axiom of Choice in Neural Proof Embeddings arXiv:2606.28572v1 Announce Type: new Abstract: The axiom of choice has divided the foundations of mathematics for over a century, but the distinction between classical and constructive proofs has remained a philosophical and methodological one. We use Lean 4's kernel-level… 8 arXiv — Machine Learning research 30m ago Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model arXiv:2606.28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions. Such cost functions can have a multitude of local optima and yet, gradient-based optimization appears to converge to… 10 arXiv — Machine Learning research 30m ago What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs arXiv:2606.28615v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rationales are used to justify model outputs. Yet it remains unclear whether these… 31 arXiv — Machine Learning research 30m ago Randomized Exploration for Linear Bandits via Absolute Perturbations arXiv:2606.28616v1 Announce Type: new Abstract: In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson Sampling (TS) is computationally attractive yet… 27 arXiv — Machine Learning research 30m ago Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture arXiv:2606.28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be… 37 arXiv — Machine Learning research 30m ago When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling arXiv:2606.28661v1 Announce Type: new Abstract: People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often a… 22 arXiv — Machine Learning research 30m ago Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks arXiv:2606.28662v1 Announce Type: new Abstract: The flatness hypothesis suggests that flatness of the loss landscape, as measured by the eigenvalues of the loss Hessian, correlates with better neural network generalization. While various algorithms reduce these eigenvalues, most… 18 arXiv — Machine Learning research 30m ago Entropy Regularized Reinforcement Learning for Zero-Sum Stochastic Differential Games in a Regime-Switching Jump-Diffusion Process arXiv:2606.28669v1 Announce Type: new Abstract: To address parameter misspecification and sudden structural environmental changes in conventional stochastic differential game (SDG) frameworks, this paper introduces a distributional control approach that characterizes optimal… 16 arXiv — Machine Learning research 30m ago Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models arXiv:2606.28671v1 Announce Type: new Abstract: Stackelberg differential games (SDGs) provide a powerful framework for hierarchical decision-making in stochastic and continuous-time environments, yet their solution remains computationally challenging due to the complexity of… 13 arXiv — Machine Learning research 30m ago Constrained Tabular Diffusion for Finance arXiv:2606.28674v1 Announce Type: new Abstract: Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular diffusion models cannot provide. To address this… 14 arXiv — Machine Learning research 30m ago A Path-Space Formulation of Prediction in World Models: From a Single Action to Prediction, Planning, and Irreversibility arXiv:2606.28751v1 Announce Type: new Abstract: We propose a path-space formulation of prediction in AI world models. Rather than sequences of one-step conditional distributions, we argue that a world model implicitly defines a probability measure over future trajectories. In… 13 arXiv — Machine Learning research 30m ago Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization arXiv:2606.28764v1 Announce Type: new Abstract: Hierarchical decision-making frameworks are pivotal for addressing complex control tasks, enabling agents to decompose intricate problems into manageable subgoals. Despite their promise, existing hierarchical policies face critical… 20 arXiv — Machine Learning research 30m ago Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions arXiv:2606.28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication. Effectively conveying these expressions through technology is of utmost importance, for example, with virtual reality avatars and in social… 9 arXiv — Machine Learning research 30m ago On design-unbiased algorithmic Machine Learning arXiv:2606.28795v1 Announce Type: new Abstract: Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive performance. However, minimising the MSE or F-score cannot lead to unbiasedness… 24 arXiv — Machine Learning research 30m ago HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression arXiv:2606.28831v1 Announce Type: new Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top-$p$ nucleus sampling) offer superior accuracy by dynamically fluctuating memory budgets, yet modern inference engines (e.g.,… 29 arXiv — Machine Learning research 30m ago Active Quantum Kernel Acquisition for Gaussian Process Regression arXiv:2606.28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions. Recent work on quantum kernel… 36 arXiv — Machine Learning research 30m ago Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning arXiv:2606.28835v1 Announce Type: new Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenarios introduces unique challenges: 1) Capacity conflict and catastrophic forgetting… 7 arXiv — Machine Learning research 30m ago The Contagion Tensor: A Framework for Measuring Output-Distribution Coupling in Multi-Agent LLM Systems -- and Auditing the Claims It Enables arXiv:2606.28839v1 Announce Type: new Abstract: We introduce the Contagion Tensor, a measurement framework for quantifying how large language model (LLM) output distributions couple across modalities, agents, and time steps. From the tensor we derive the Coupling Amplification… 38 arXiv — Machine Learning research 30m ago Analysis of Adam Algorithms for Stochastic Dynamic Systems arXiv:2606.28879v1 Announce Type: new Abstract: The adaptive moment estimation algorithm, known as Adam, is widely used in modern machine learning, owing to its low per-iteration complexity and strong empirical performance. Despite its prevalent use, the theoretical foundation… 11 arXiv — Machine Learning research 30m ago An Integrated Machine Learning and Hierarchical Variance Decomposition Pipeline for Student Performance Prediction and Metacognitive Calibration on Multi-Signal Telemetry arXiv:2606.28881v1 Announce Type: new Abstract: Predicting student performance and characterizing metacognitive calibration are essential for personalization in intelligent tutoring systems. Prior research treats performance prediction, calibration error calculation, and… 6 arXiv — Machine Learning research 30m ago MALOQ: Massively Accelerated Learning of Operators for Quantum Transport arXiv:2606.28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously… 19 arXiv — Machine Learning research 30m ago ML-Powered LDAP Reconnaissance Detection using Weak Supervision arXiv:2606.28917v1 Announce Type: new Abstract: Lightweight Directory Access Protocol (LDAP) is a protocol that allows users to query and modify Active Directory (AD) data. By default, all users have read access to all AD data through LDAP, making it a common initial tool for… 14 arXiv — Machine Learning research 30m ago Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks arXiv:2606.28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure. Logging of all cloud computing events enables investigation into any incidents… 24 arXiv — Machine Learning research 30m ago Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation arXiv:2606.28925v1 Announce Type: new Abstract: Tool and agent routing from natural-language prompts is naturally a set-valued prediction problem: a single query may require multiple agents, while over-selection increases execution cost. The benchmark introduced here is derived… 16 arXiv — Machine Learning research 30m ago DLR: Zero-Inference-Cost Latent Residuals for Low-Rank Pre-Training arXiv:2606.28932v1 Announce Type: new Abstract: Large language models have driven recent progress in language and multimodal AI, yet pre-training them at scale is prohibitively expensive. Low-rank pre-training, which factorizes each weight matrix into a rank-r product to reduce… 35 arXiv — Machine Learning research 30m ago ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies arXiv:2606.28939v1 Announce Type: new Abstract: Behavior-cloned diffusion policies are expressive but remain vulnerable to covariate shift: small deviations from demonstrated states can compound into task failure. Existing methods address this either by expanding the training… 10 arXiv — Machine Learning research 30m ago Machine-learnable Sets arXiv:2606.28947v1 Announce Type: new Abstract: In this study we present a formal definition of large discrete sets having, informally, three properties: their elements are easily recognized, easily generated, and the latter tasks are easily learned from examples. The formalism… 19 arXiv — Machine Learning research 30m ago Modification-Considering Value Learning for Reward Hacking Mitigation in RL arXiv:2606.28955v1 Announce Type: new Abstract: Reinforcement learning agents can exploit misspecified reward signals to achieve high apparent returns while failing on the intended objective, a failure mode known as reward hacking. Existing practical defenses typically constrain… 10 arXiv — Machine Learning research 30m ago RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection arXiv:2606.28970v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are often efficient, but they usually rely on a fixed… 32 arXiv — Machine Learning research 30m ago On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks arXiv:2606.28996v1 Announce Type: new Abstract: Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability. Unlike… 29 arXiv — Machine Learning research 30m ago How Far Can Sharpness and Complexity Jointly Explain Generalization? arXiv:2606.29043v1 Announce Type: new Abstract: Sharpness and complexity are two central factors in the generalization analysis of deep neural networks. Existing quantitative evaluations of generalization measures have largely focused on individual scalar measures, leaving the… 13 arXiv — Machine Learning research 30m ago MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment arXiv:2606.29049v1 Announce Type: new Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect semantic depth and a restriction to single-granularity… 37 arXiv — Machine Learning research 30m ago A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest arXiv:2606.29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher discriminant analysis forest (KFDA Forest), which is a tree-based ensemble… 35 arXiv — Machine Learning research 30m ago When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation arXiv:2606.29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by… 4 arXiv — Machine Learning research 30m ago Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models arXiv:2606.29091v1 Announce Type: new Abstract: Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them. We make this statement falsifiable. The \emph{Operational Turing Test} (OTT) constructs pairs of legal and… 32 arXiv — Machine Learning research 30m ago Priced Motion Through Optimal Faces: A Normal-Fan Geometry for Non-Stationary Adversarial MDPs arXiv:2606.29092v1 Announce Type: new Abstract: In a changing decision problem, standard dynamic-regret analyses have often equated the cost of non-stationarity to how far loss moves. However, it is simultaneously possible for a loss sequence to travel far and retain the same… 11 Page 1 of 10 · 500 articles Older →
