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Non-intrusive Body Composition Assessment from Full-body mmWave Scans

arXiv:2605.08306v1 Announce Type: cross Abstract: Body composition assessment (BCA) provides detailed information about the distribution of different tissue types in the body, enabling more precise ch

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Non-Parametric Rehearsal Learning via Conditional Mean Embeddings

DGX agent

arXiv:2605.08999v1 Announce Type: new Abstract: In machine learning, a critical class of decision-related problems concerns preventing predicted undesirable outcomes, referred to as the extit{avoiding

researcharxiv-cs-lg

12 May 2026

Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials

arXiv:2605.09058v1 Announce Type: cross Abstract: We introduce Nonlinear GENERIC Informed Neural Networks (N-GINNs), a deep learning framework for discovering evolution equations of systems governed b

Not Blind but Silenced: Rebalancing Vision and Language via Adversarial Counter-Commonsense Equilibrium

arXiv:2605.10676v1 Announce Type: new Abstract: During MLLM decoding, attention often abnormally concentrates on irrelevant image tokens. While existing research dismisses this as invalid noise and fo

Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear

arXiv:2605.10061v1 Announce Type: cross Abstract: Futrell and Mahowald (2025) frame the success of neural language models (LMs) as supporting gradient, usage-based linguistic theories. I argue that LM

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Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning

arXiv:2605.09160v1 Announce Type: new Abstract: Learned representations are often invariant to rotational transformations, leaving individual dimensions non-identifiable and interchangeable. We study

OCP-GN: A Scalable Second-order Optimizer for Stochastic Optimization

arXiv:2512.24552v2 Announce Type: replace Abstract: This paper proposes a novel second-order optimization algorithm based on the Optimal Control Principle (OCP), applicable to large-scale optimization

On Characterizing Learnability for Adversarial Noisy Bandits

arXiv:2605.09200v1 Announce Type: new Abstract: We study adversarial noisy bandits given a known function class F. In each round, the adversary selects a function f in F, the learner chooses an arm, a

On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints

arXiv:2605.10722v1 Announce Type: new Abstract: Molecular Graph Neural Networks (GNNs) are increasingly common in drug discovery, particularly for Quantitative Structure-Activity Relationship (QSAR) s

On periodic distributed representations using Fourier embeddings

arXiv:2605.10818v1 Announce Type: new Abstract: Periodic signals are critical for representing physical and perceptual phenomena. Scalar, real angular measures, e.g., radians and degrees, result in di

On the Convergence of Muon and Beyond

arXiv:2509.15816v5 Announce Type: replace Abstract: The Muon optimizer has demonstrated remarkable empirical success in handling matrix-structured parameters for training neural networks. However, a s

On the Convergence Rate of LoRA Gradient Descent

arXiv:2512.18248v3 Announce Type: replace Abstract: The low-rank adaptation (LoRA) algorithm for fine-tuning large models has grown popular in recent years due to its remarkable performance and low co

On the global convergence of gradient descent for wide shallow models with bounded nonlinearities

arXiv:2605.10775v1 Announce Type: cross Abstract: A surprising phenomenon in the training of neural networks is the ability of gradient descent to find global minimizers of the training loss despite i

One-step Latent-free Image Generation with Pixel Mean Flows

arXiv:2601.22158v3 Announce Type: replace Abstract: Modern diffusion/flow-based models for image generation typically exhibit two core characteristics: (i) using multi-step sampling, and (ii) operatin

One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory

arXiv:2505.23617v3 Announce Type: replace-cross Abstract: Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time p

Online Set Learning from Precision and Recall Feedback

arXiv:2605.09565v1 Announce Type: new Abstract: We consider the problem of learning an unknown subset N_ext{target} of a domain in an online setting. In each round t, the learner predicts a set of ite

Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection

arXiv:2605.10040v1 Announce Type: new Abstract: The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalating the risks of identity fraud and the d

Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions

arXiv:2511.01292v2 Announce Type: replace-cross Abstract: Pretrained Transformers can perform in-context learning (ICL) from a few demonstrations, but this ability can fail sharply when the test distr

Optimal Regret for Single Index Bandits

arXiv:2605.09454v1 Announce Type: cross Abstract: We study the extit{single-index bandit} problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an

Optimizer-Induced Mode Connectivity: From AdamW to Muon

arXiv:2605.09991v1 Announce Type: new Abstract: Mode connectivity has been widely studied, yet the role of the optimizer remains underexplored. We revisit it through optimizer-induced implicit regular

Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks

arXiv:2605.09813v1 Announce Type: cross Abstract: We investigate the control and optimization of vertical federated learning (VFL), a class of distributed machine learning (ML) methods in which edge/f

ORICF -- Open Robotics Inference and Control Framework

arXiv:2605.09656v1 Announce Type: new Abstract: Recent advances in artificial intelligence (AI) have enabled effective perception and language models for robots, but their deployment remains computati

researcharxiv-cs-ro

OsteoFlow: Lyapunov-Guided Flow Distillation for Predicting Bone Remodeling after Mandibular Reconstruction

arXiv:2603.22421v2 Announce Type: replace Abstract: Predicting long-term bone remodeling after mandibular reconstruction would be of great clinical benefit, yet standard generative models struggle to

OUIDecay: Adaptive Layer-wise Weight Decay for CNNs Using Online Activation Patterns

arXiv:2605.10161v1 Announce Type: new Abstract: Weight decay remains one of the most widely used regularization mechanisms for training convolutional neural networks, yet it is still commonly applied

Outlier detection for patient monitoring and alerting

arXiv:2605.08955v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electro

Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner

arXiv:2506.01301v2 Announce Type: replace Abstract: Theory-of-Mind (ToM) enables humans to infer mental states-such as beliefs, desires, and intentions-forming the foundation of social cognition. Howe

OZ-TAL: Online Zero-Shot Temporal Action Localization

arXiv:2605.09976v1 Announce Type: new Abstract: Online Temporal Action Localization (On-TAL) aims to detect the occurrence time and category of actions in untrimmed streaming videos immediately upon t

P-Flow: Proxy-gradient Flows for Linear Inverse Problems

arXiv:2605.08328v1 Announce Type: cross Abstract: Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster samplin

PaceVGGT: Pre-Alternating-Attention Token Pruning for Visual Geometry Transformers

arXiv:2605.08371v1 Announce Type: new Abstract: Visual Geometry Transformer (VGGT) is a strong feed-forward model for multiple 3D tasks, but its Alternating-Attention (AA) stack scales quadratically i

PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation

arXiv:2605.09036v1 Announce Type: new Abstract: Accurate and efficient storm-surge emulation is essential for coastal hazard assessment, yet high-fidelity hydrodynamic models remain too expensive for

PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding

arXiv:2605.08632v1 Announce Type: cross Abstract: Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verifi

parHSOM: A novel parallel Hierarchical Self-Organizing Map implementation

arXiv:2605.08164v1 Announce Type: cross Abstract: The digital age has completely transformed the way that information is processed and stored, which makes cybersecurity a crucial field of research. Cy

PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering

arXiv:2605.10791v1 Announce Type: new Abstract: Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent KGQA methods mainly follow the

Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

arXiv:2605.09339v1 Announce Type: cross Abstract: Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured

Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification

arXiv:2605.08121v1 Announce Type: cross Abstract: Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Rec

PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection

arXiv:2605.09319v1 Announce Type: new Abstract: With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigatin

Phase Transitions in Affective Meaning Divergence: The Hidden Drift Before the Break

arXiv:2605.09043v1 Announce Type: cross Abstract: One partner says 'Fine' meaning resolution; the other hears surrender. The word is shared; the affective uptake is not. We formalize this as affective

Phases of Muon: When Muon Eclipses SignSGD

arXiv:2605.09552v1 Announce Type: cross Abstract: Recently, Muon and related spectral optimizers have demonstrated strong empirical performance as scalable stochastic methods, often outperforming Adam

Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow

arXiv:2605.08915v1 Announce Type: new Abstract: Deep learning paradigms, such as PINNs and neural operators, have significantly advanced the solving of PDEs. However, they often struggle to capture th

Physics-Modeled Neural Networks

arXiv:2605.08176v1 Announce Type: new Abstract: We introduce Dynamical Physics-Modeled Neural Networks (DynPMNNs), a continuous-time deep learning architecture in which each hidden layer is defined as

PIDNet: Progressive Implicit Decouple Network for Multimodal Action Quality Assessment

arXiv:2605.08945v1 Announce Type: new Abstract: Action quality assessment (AQA) aims to automatically quantify the execution quality of human actions in videos and is valuable for applications such as

Pixal3D: Pixel-Aligned 3D Generation from Images

arXiv:2605.10922v1 Announce Type: new Abstract: Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appe

PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams

arXiv:2605.08388v1 Announce Type: new Abstract: Human-AI teams play a pivotal role in improving overall system performance when neither the human nor the model can achieve such performance on their ow

PoDAR: Power-Disentangled Audio Representation for Generative Modeling

arXiv:2605.10084v1 Announce Type: cross Abstract: The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space.

Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable

arXiv:2605.10404v1 Announce Type: new Abstract: With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becomin

Post-hoc Selective Classification for Reliable Synthetic Image Detection

arXiv:2605.08574v1 Announce Type: new Abstract: As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite s

PowerStep: Memory-Efficient Adaptive Optimization via ell_p-Norm Steepest Descent

arXiv:2605.10335v1 Announce Type: cross Abstract: Adaptive optimizers, most notably Adam, have become the default standard for training large-scale neural networks such as Transformers. These methods