News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 3h ago LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining arXiv:2608.12419v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit… 29 arXiv — Machine Learning research 3h ago Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods arXiv:2608.12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under… 14 arXiv — Machine Learning research 3h ago MARCH: Scaling Recurrent Memory with Content-Routed State Anchors arXiv:2608.12435v1 Announce Type: new Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length. This flexibility, however, incurs a quadratic computation complexity during training and a key--value… 15 arXiv — Machine Learning research 3h ago Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach arXiv:2608.12436v1 Announce Type: new Abstract: Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean… 17 arXiv — Machine Learning research 3h ago Unifying Generative Models with Path Integrals arXiv:2608.12438v1 Announce Type: new Abstract: We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single master action. Its… 36 arXiv — Machine Learning research 3h ago Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection arXiv:2608.12441v1 Announce Type: new Abstract: Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason. This opacity is untenable in the cooperative,… 31 arXiv — Machine Learning research 3h ago Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts arXiv:2608.12446v1 Announce Type: new Abstract: Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models against a single reference hypnogram despite known inter-scorer variability. This study… 31 arXiv — NLP / Computation & Language research 3h ago Geometric and Behavioral Stratification in Transformer Residual Streams arXiv:2608.12447v1 Announce Type: cross Abstract: Trained transformer models develop privileged bases: coordinate axes whose statistics differ from the rest of the residual stream. But what kind of direction does such a basis select? We investigate the prediction direction, the… 6 arXiv — Machine Learning research 3h ago Exemplar-based objective classification of gust-induced loads across multiple flight conditions arXiv:2608.12448v1 Announce Type: new Abstract: Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such… 13 arXiv — Machine Learning research 3h ago Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication arXiv:2608.12477v1 Announce Type: new Abstract: Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient. This assumption fails when treatment decisions make the clinically relevant outcome permanently unobservable. As… 22 arXiv — Machine Learning research 3h ago When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide arXiv:2608.12489v1 Announce Type: new Abstract: Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it. Off-policy evaluation promises this from logged data, but the deployable rule is a deterministic top-k… 18 arXiv — Machine Learning research 3h ago Exploring Oversmoothing with Householder Matrices arXiv:2608.12514v1 Announce Type: new Abstract: Deep graph neural networks(GNNs) suffer from oversmoothing- a progressive collapse of node representation towards a low information subspace as network depth increases because the normalized graph propagation operator is repeatedly… 25 arXiv — Machine Learning research 3h ago GENADA: efficient generative time series adversarial attack framework arXiv:2608.12535v1 Announce Type: new Abstract: Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring. However, these models remain vulnerable to adversarial attacks, where small input… 17 arXiv — Machine Learning research 3h ago Scaling Automatic Research Agents via World Models arXiv:2608.12564v1 Announce Type: new Abstract: Automating empirical research is a long-standing direction of AI. Recent automatic research (AutoResearch) agents bring this goal within reach, as modern LLMs show the capability to independently implement solutions and learn from… 11 arXiv — Machine Learning research 3h ago Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection arXiv:2608.12573v1 Announce Type: new Abstract: Top-k selection is a fundamental computational primitive with applications spanning databases, information retrieval, signal processing, and modern machine learning workloads, including sparse activations and attention pruning. As… 18 arXiv — Machine Learning research 3h ago Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness arXiv:2608.12592v1 Announce Type: new Abstract: Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient. Conditional generation offers a remedy: an… 6 arXiv — Machine Learning research 3h ago Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks arXiv:2608.12597v1 Announce Type: new Abstract: Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen random map. This raises a practical question: how… 7 arXiv — Machine Learning research 3h ago The Boolean Power of ReLU arXiv:2608.12617v1 Announce Type: new Abstract: We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in $\Sigma$-MPLang, for any collection $\Sigma$ of eventually constant activation functions and with… 38 arXiv — Machine Learning research 3h ago Structure-preserving uncertainty quantification for GENERIC dynamics arXiv:2608.12624v1 Announce Type: new Abstract: Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limited because standard UQ methods may violate the… 18 arXiv — Machine Learning research 3h ago CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution arXiv:2608.12629v1 Announce Type: new Abstract: GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce. Agents usually treat the compiler as a fixed black box and receive only errors, correctness outcomes, and… 11 arXiv — Machine Learning research 3h ago Interpretable Causal Discovery via Causal-Effect Constraints arXiv:2608.12640v1 Announce Type: new Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but also to be able to interpret and explain either… 16 arXiv — Machine Learning research 3h ago Training Under Challenge: Executable Certificates and Challenge-Closed Optimality for Neural Networks arXiv:2608.12655v1 Announce Type: new Abstract: A flat training curve does not reveal whether a neural network has reached a global optimum, is locally trapped, is representation-limited, or is mismatched to its trainer. We introduce Training Under Challenge, an… 32 arXiv — Machine Learning research 3h ago Demand Transfer Estimation at Scale via Restricted Logit Modeling arXiv:2608.12680v1 Announce Type: new Abstract: Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice model and using this model to determine the value of an objective function (i.e.… 35 arXiv — Machine Learning research 3h ago Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization arXiv:2608.12687v1 Announce Type: new Abstract: Bayesian optimization (BO) is a sample-efficient framework for analog circuit topology search, where evaluating each candidate topology can require costly simulation. However, representation-based BO methods typically treat circuit… 33 arXiv — Machine Learning research 3h ago The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning arXiv:2608.12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It remains unclear whether these choices sacrifice information needed for rhythm… 20 arXiv — Machine Learning research 3h ago A Contract-Grade Verifier for LLM-Generated GPU Kernels, and a Native Blackwell Backward for the Gated-Linear-Recurrence Family arXiv:2608.12700v1 Announce Type: new Abstract: Systems that generate GPU kernels with language models report high correctness rates. Those rates come from a single loose test: run the kernel on a few random inputs at one fixed shape and accept it if the output is close to a… 20 arXiv — Machine Learning research 3h ago Federated Compositional Muon Optimizer for Matrix-Wise Models arXiv:2608.12710v1 Announce Type: new Abstract: Muon, a more recently developed optimizer, is useful for matrix-wise models in AI areas. Although many works have studied Muon and its variants, these methods are still not particularly well-suited for hierarchical structured… 32 arXiv — NLP / Computation & Language research 3h ago Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia arXiv:2608.12717v1 Announce Type: cross Abstract: Mechanistic interpretability of large language models lacks spatially resolved, falsifiable tools for testing whether internal components are specialized for distinct cognitive operations. We adapt subtraction analysis, the… 36 arXiv — Machine Learning research 3h ago MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning arXiv:2608.12724v1 Announce Type: new Abstract: Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance is highly sensitive to the quality and coverage of the selected… 27 arXiv — Machine Learning research 3h ago A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings arXiv:2608.12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarce, compute is limited, and clinical decision-making… 19 arXiv — Machine Learning research 3h ago Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry arXiv:2608.12753v1 Announce Type: new Abstract: We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions with common rewards, (B) observed actions with… 18 arXiv — Machine Learning research 3h ago Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents arXiv:2608.12764v1 Announce Type: new Abstract: Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sparse for effective credit assignment. On-policy… 19 arXiv — Machine Learning research 3h ago CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility arXiv:2608.12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the… 38 arXiv — Machine Learning research 3h ago HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models arXiv:2608.12821v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt tuning typically rely on a single global prompt or externally selected prompt… 5 arXiv — Machine Learning research 3h ago Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing arXiv:2608.12831v1 Announce Type: new Abstract: Online platforms increasingly compare many adaptive decision policies---ranking systems, recommendation algorithms, pricing rules, and language-model agents---while each reward-bearing interaction can be costly or risky. A direct… 25 arXiv — Machine Learning research 3h ago A Compositional Theory of Curvature in Probabilistic Circuits arXiv:2608.12869v1 Announce Type: new Abstract: Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: the trace of the Hessian of the log-likelihood.… 21 arXiv — Machine Learning research 3h ago Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning arXiv:2608.12874v1 Announce Type: new Abstract: Plasticity loss has emerged as a critical challenge in continual learning that significantly hinders the acquisition of sequential tasks. While optimizing activation designs offers a potential solution, current fixed-form functions… 23 arXiv — Machine Learning research 3h ago Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection arXiv:2608.12879v1 Announce Type: new Abstract: Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency measurement noise and the derivative orders are… 27 arXiv — Machine Learning research 3h ago Adaptive $k$ Nearest Neighbors Classifier via Granular Ball Computing arXiv:2608.12903v1 Announce Type: new Abstract: The $k$-Nearest Neighbor~(KNN) algorithm is widely used across various tasks. The selection of the $k$ value is a key issue because it significantly impacts performance. In this paper, an adaptive and efficient KNN approach via… 25 arXiv — Machine Learning research 3h ago EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction arXiv:2608.12906v1 Announce Type: new Abstract: RNA-Protein Interactions (RPIs) are critical for regulating cellular functions. While traditional wet-lab experiments for RPI detection are costly and time-consuming, Deep Learning (DL) methods provide an efficient computational… 38 arXiv — Machine Learning research 3h ago Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection arXiv:2608.12912v1 Announce Type: new Abstract: This paper considers the overestimation bias problem of Q-learning in the setting of a large action space, for the purpose of relieving the bottleneck of existing methods. We find that the large action space increases the… 9 arXiv — Machine Learning research 3h ago Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling arXiv:2608.12917v1 Announce Type: new Abstract: Developing effective robot navigation methods in crowded environments is essential for real-world applications. Although recent deep reinforcement learning (DRL) methods have improved navigation performance in crowded environments,… 31 arXiv — Machine Learning research 3h ago Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization arXiv:2608.12925v1 Announce Type: new Abstract: Momentum-based optimizers are widely used in modern deep learning, yet the relations among momentum recursion, update geometry, and acceleration remain only partially understood. We develop an $\textbf{A}$DMM-$\textbf{I}$nspired… 14 arXiv — Machine Learning research 3h ago H-VAEP and H-xT: Valuing Offensive On-the-Ball Actions in Handball by Estimating Probabilities arXiv:2608.12926v1 Announce Type: new Abstract: Traditional player evaluation in professional handball relies on basic box-score metrics or heuristic indices, which fail to credit the multi-player build-up chain. While football (soccer) analytics has adopted Expected Threat (xT)… 31 arXiv — Machine Learning research 3h ago Multi-perspective Imbalance-Conscious 6G Beamforming Optimization and Performance arXiv:2608.12929v1 Announce Type: new Abstract: The study presents a systematic machine learning (ML) study of 6G-IoT beamforming optimization (6GBO) using supervised and unsupervised approaches. We compared the predictive power of network, environmental, device, and vision… 38 arXiv — Machine Learning research 3h ago Diagnosing JEPA World Models with Action-Conditioned Predictive Consistency arXiv:2608.12939v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn world models that predict in a compact latent space rather than in pixels, reducing the pressure to model nuisance appearance. Yet this provides no guarantee against visual… 15 arXiv — Machine Learning research 3h ago CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation arXiv:2608.12944v1 Announce Type: new Abstract: Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the… 23 arXiv — NLP / Computation & Language research 3h ago I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization arXiv:2608.12957v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) learns from reward differences within a rollout group, but receives no useful relative signal when every sampled response is incorrect. Privileged self-distillation can fill this gap with… 33 arXiv — Machine Learning research 3h ago The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use arXiv:2608.12959v1 Announce Type: new Abstract: Latent world models are judged by how well they predict, so when planning fails at long horizons the natural reading is that the predictor degrades. On a reproduction of LeWorldModel on TwoRoom we show the binding constraint is the… 25 arXiv — Machine Learning research 3h ago Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice arXiv:2608.12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this setting, the initiator can withhold information about the learning task, while other… 36 Page 1 of 10 · 500 articles Older →
