News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 2d ago STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data arXiv:2608.10249v1 Announce Type: new Abstract: We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives. Variable-length trajectories are encoded with a custom BERT-based… 29 arXiv — Machine Learning research 2d ago CRHT: A Continuous Regression Hybrid Transformer for Vessel Trajectory Prediction with Online Cluster Sampling arXiv:2608.10256v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and navigational realism. We propose the Continuous Regression Hybrid Transformer… 27 arXiv — Machine Learning research 2d ago Toward Human Rights Benchmarking for LLMs: A Pilot Methodology arXiv:2608.10268v1 Announce Type: new Abstract: Large language models (LLMs) increasingly mediate legal determinations over what human rights are realized, and how. Yet, no evaluation benchmark exists to assess whether they can reason correctly about human rights law. To this… 14 arXiv — NLP / Computation & Language research 2d ago Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference arXiv:2608.10288v1 Announce Type: cross Abstract: The Large Language Model from Power Law Decoder Representations (PLDR-LLM) and its attention, Power Law Graph Attention (PLGA), replace the fixed bilinear form of scaled dot-product attention (SDPA) with a learned,… 28 arXiv — Machine Learning research 2d ago MERA: Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale arXiv:2608.10333v1 Announce Type: new Abstract: LLM agents execute heterogeneous sequences of model calls within a single task: some invocations require careful reasoning, while others are structured steps such as formatting or tool-argument construction. Prior routing methods… 34 arXiv — Machine Learning research 2d ago Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network arXiv:2608.10351v1 Announce Type: new Abstract: In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN). This optimization procedure introduces contextual features into the first layer of a DNN. The… 4 arXiv — Machine Learning research 2d ago Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks arXiv:2608.10357v1 Announce Type: new Abstract: Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcement learning (RL) is a natural fit for this setting, but multi-turn on-policy… 20 arXiv — Machine Learning research 2d ago Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration arXiv:2608.10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining. An ideal calibrator should correct nonlinear miscalibration, scale gracefully to large label spaces, and preserve the… 25 arXiv — Machine Learning research 2d ago Fisher8: Stabilizing Neural Heteroscedastic Regression via Output-Layer Fisher Geometry arXiv:2608.10374v1 Announce Type: new Abstract: Training neural networks to jointly predict mean and uncertainty estimates from noisy observations can be unstable, prompting a series of independent stabilization efforts. We argue that these interventions highlight a common… 16 arXiv — Machine Learning research 2d ago Generator-Guided Inverse Sampling for L\'evy-Driven Generative Models arXiv:2608.10384v1 Announce Type: new Abstract: This paper studies inverse sampling for L\'evy-driven generative models from the perspective of Markov generators. Unlike conventional diffusion models, L\'evy-driven dynamics involve infinite jump activities, which makes their… 19 arXiv — Machine Learning research 2d ago Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving arXiv:2608.10386v1 Announce Type: new Abstract: Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world models reduce the reliance on costly environment interactions, policy optimization… 37 arXiv — NLP / Computation & Language research 2d ago Share First, Route What Remains: A Unified Framework for Token-Adaptive MoE Computation arXiv:2608.10392v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts. Shared-expert designs preserve reusable knowledge, fine-grained methods vary computation within experts, and dynamic routers… 13 arXiv — Machine Learning research 2d ago ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation arXiv:2608.10398v1 Announce Type: new Abstract: Variational autoencoders generate samples from probabilistic latent representations but do not distinguish uncertainty about the latent location from variability around it. We formulate ELVAE, an evidential learning-based VAE in… 13 arXiv — Machine Learning research 2d ago Do Judges Behave Like Algorithms? arXiv:2608.10400v1 Announce Type: new Abstract: What if judges already behave like algorithms? As artificial intelligence and algorithms are deployed in many settings, including the judicial system, many have debated whether judges should be allowed to rely on them. Instead, we… 4 arXiv — Machine Learning research 2d ago TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling arXiv:2608.10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times.… 16 arXiv — Machine Learning research 2d ago Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique arXiv:2608.10430v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed as AI agents frequently exhibit user specification-grounding failures, executing hallucinated, undesired actions to force a resolution rather than expressing uncertainty. Existing detection… 14 arXiv — Machine Learning research 2d ago Do Time-Series Forecasters Use the Right History: Recoverability, Recovery, and Functional Use of Temporal Delays arXiv:2608.10433v1 Announce Type: new Abstract: Forecast accuracy does not tell us which past inputs produced a prediction. We separate three questions for time-series models with known delay structure: can the true delay be recovered from the observed data, does the model… 37 arXiv — NLP / Computation & Language research 2d ago Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agents arXiv:2608.10441v1 Announce Type: cross Abstract: Many pipelines can pay a per-example cost to acquire an auxiliary, model-derived observation -- an LLM's structured reasoning, a slow oracle, an expensive measurement -- and then must decide when the acquired signal is worth… 30 arXiv — Machine Learning research 2d ago A Joint-Distribution Route to Fair Representations with Continuous Sensitive Attributes arXiv:2608.10470v1 Announce Type: new Abstract: Fair representation learning with a continuous sensitive attribute $S$ requires a representation $Z$ that is statistically independent of $S$. Existing criteria, including generalized demographic parity, the expectation of integral… 37 arXiv — Machine Learning research 2d ago Critic-Free Pretraining for Efficient Online Reinforcement Learning Fine-Tuning arXiv:2608.10473v1 Announce Type: new Abstract: Offline-to-online (O2O) reinforcement learning aims to leverage policies pretrained on static datasets while improving them through online interaction. However, directly reusing an offline-trained critic can hinder online… 19 arXiv — Machine Learning research 2d ago Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation arXiv:2608.10499v1 Announce Type: new Abstract: Personalized Federated Reinforcement Learning (PFRL) takes a decentralized approach to storing and accessing information based on past experiences while keeping each client's data private during the learning of each client's… 8 arXiv — Machine Learning research 2d ago Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits arXiv:2608.10526v1 Announce Type: new Abstract: Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown. We consider three information structures: (A)~unobserved actions with… 31 arXiv — Machine Learning research 2d ago Robust Multi-Agent Bandits with Heavy-Tailed Rewards and Information Asymmetry arXiv:2608.10529v1 Announce Type: new Abstract: The multi-armed bandit problem is a central framework in sequential decision-making, extensively studied under sub-Gaussian reward assumptions. However, real-world applications often involve heavy-tailed reward distributions and… 6 arXiv — Machine Learning research 2d ago Retrieval-Corrected Conformal Prediction for Time Series arXiv:2608.10553v1 Announce Type: new Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and… 12 arXiv — Machine Learning research 2d ago MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction arXiv:2608.10562v1 Announce Type: new Abstract: Not all clicks are equal. Industrial ads ranking decouples conversion probability into click-through rate (CTR) and post-click conversion rate (CVR), yet treats every click as the same event. In reality, users provide a free,… 33 arXiv — Machine Learning research 2d ago BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis arXiv:2608.10587v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings. Among these approaches, AI-based statistical process monitoring (SPM) is widely used,… 20 arXiv — Machine Learning research 2d ago $\beta$-VAEs as Effective Theories: Tolerance-Dependent Dimension arXiv:2608.10599v1 Announce Type: new Abstract: In a $\beta$-VAE, increasing the regularization strength acts as a spectral cutoff by collapsing low-utility latent coordinates. In the linear Gaussian VAE, the collapse order matches the ranking of reconstruction utilities… 11 arXiv — Machine Learning research 2d ago Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts arXiv:2608.10605v1 Announce Type: new Abstract: In large-scale pretraining, the algorithm, architecture, and systems decisions are conventionally made in disconnected stages. A scaling law stage selects an architecture and training recipe, optimizing loss under compute… 38 arXiv — Machine Learning research 2d ago Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment arXiv:2608.10619v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) often struggle when task-relevant information is distributed across distant regions of a graph, since local propagation must compress remote signals through limited structural interfaces.… 34 arXiv — Machine Learning research 2d ago ProbGuard: Calibrated Safety Risk Estimation from LLM Output Distributions arXiv:2608.10621v1 Announce Type: new Abstract: Recent research on Large Language Model (LLM) safety has widely adopted guardrails to identify unsafe LLM outputs. Existing guardrails typically formulate safety assessment as a deterministic classification task, mapping a discrete… 37 arXiv — Machine Learning research 2d ago IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning arXiv:2608.10634v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making. Numerous methods have been developed to improve dynamics… 13 arXiv — NLP / Computation & Language research 2d ago Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization arXiv:2608.10694v1 Announce Type: cross Abstract: Evolutionary optimization of LLM prompts and agentic programs (e.g., GEPA) is dominated by fitness evaluation: scoring each candidate runs an answering LLM over a validation set, so the evaluator's price tier dictates total… 25 arXiv — Machine Learning research 2d ago ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes arXiv:2608.10699v1 Announce Type: new Abstract: Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network… 24 arXiv — NLP / Computation & Language research 2d ago Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control arXiv:2608.10703v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM personality studies largely rely on self-report… 18 arXiv — Machine Learning research 2d ago SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features arXiv:2608.10709v1 Announce Type: new Abstract: Quantization-Aware Training (QAT) enables the deployment of quantized models with minimal accuracy degradation. However, in practical scenarios, training labels are often unavailable due to privacy, copyright, or cost constraints.… 5 arXiv — Machine Learning research 2d ago Long-Time Trajectory Approximation via SA-NODEs: Model Predictive and Floquet Strategies arXiv:2608.10738v1 Announce Type: new Abstract: We study the approximation of dynamical systems by semi-autonomous neural ordinary differential equations (SA-NODEs) over long time horizons. For a single network trained on the whole horizon, the available error bound deteriorates… 27 arXiv — Machine Learning research 2d ago Path Integral Value Matching for Linear Quadratic Stochastic Optimal Control arXiv:2608.10777v1 Announce Type: new Abstract: Linear Quadratic Stochastic Optimal Control (LQ-SOC) establishes a fundamental framework for steering noisy dynamical systems and has recently gained renewed interest in the machine learning community. However, current… 26 arXiv — Machine Learning research 2d ago MoE Proxy Models for Low-Cost Failure Reproduction and Diagnosis in LLM RL Post-Training arXiv:2608.10823v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training of large language models (LLMs) is computationally intensive and involves complex system pipelines with substantial debugging overhead. In practice, factors such as framework adaptation,… 16 arXiv — Machine Learning research 2d ago TACTICL: Task-Aware Compression of Tabular ICL Models arXiv:2608.10837v1 Announce Type: new Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs. Distilling models into task-specific architectures reduces model size and computational demands but also sacrifices in-context… 5 arXiv — Machine Learning research 2d ago Diffract: Spectral View of LLM Domain Adaptation arXiv:2608.10850v1 Announce Type: new Abstract: We study continual pre-training (CPT) as a mechanism for adapting general-purpose large language models to specialized domains: mathematics, instruction, code, and natural text. Using singular value decomposition of weight… 6 arXiv — Machine Learning research 2d ago FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data arXiv:2608.10857v1 Announce Type: new Abstract: Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning. This is particularly challenging in multi-modal settings when trying to learn disentangled… 10 arXiv — Machine Learning research 2d ago Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets? arXiv:2608.10867v1 Announce Type: new Abstract: Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly. We ask whether structured search can identify a strong… 28 arXiv — Machine Learning research 2d ago Optimistic Rates for Multiclass PAC Learning arXiv:2608.10869v1 Announce Type: new Abstract: Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself. For a class of… 5 arXiv — Machine Learning research 2d ago Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting arXiv:2608.10891v1 Announce Type: new Abstract: Time series forecasting in privacy-sensitive domains often requires training models on released data rather than original observations. Synthetic time series generation has been developed primarily for data augmentation, where… 6 arXiv — Machine Learning research 2d ago Partially Observable Learning for Multi-Platform Dispatch Optimization arXiv:2608.10897v1 Announce Type: new Abstract: Instant delivery platforms have become a critical component of urban logistics, increasingly relying on crowdsourced couriers to fulfill highly dynamic orders. In real-world systems, couriers are not exclusive to a single platform… 33 arXiv — Machine Learning research 2d ago ReOrder-OPD:Reliability-Aware Prompt Ordering for On-Policy Distillation arXiv:2608.10905v1 Announce Type: new Abstract: On-policy distillation (OPD) applies token-level teacher supervision to student-generated trajectories, but this supervision is not always reliable. Existing methods use local confidence or teacher-student agreement to weight,… 7 arXiv — Machine Learning research 2d ago Physics-informed Diffusion Generative Model for Time-Series Data Synthesis in Dynamic Systems arXiv:2608.10941v1 Announce Type: new Abstract: Industrial time-series signals, such as turbine temperature and rotational speed in aero-engines, are essential for monitoring the health and operational status of complex dynamical systems. However, collecting such data is often… 23 arXiv — Machine Learning research 2d ago GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care arXiv:2608.10969v1 Announce Type: new Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are… 27 arXiv — Machine Learning research 2d ago DEFT: Data-Efficient Frequency-domain Top-k Sampling via Inverse Discrete Fourier Transform for Spatiotemporal Dynamical Systems Modeling arXiv:2608.11019v1 Announce Type: new Abstract: Modeling spatiotemporal dynamical systems governed by partial differential equations (PDEs) poses two major challenges: it either requires expensive physics-based simulators that entail iterative numerical solving at high… 37 arXiv — Machine Learning research 2d ago Derivative Computation in PINNs: Automatic Differentiation, Finite Differences and Beyond arXiv:2608.11020v1 Announce Type: new Abstract: We systematically investigate finite-difference (FD) derivative computation in Physics-Informed Neural Networks (PINNs) as an alternative to automatic differentiation (AD). On three benchmark PDEs we show that, with a properly… 31 Page 9 of 10 · 500 articles ← Newer Older →
