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Neural Collapse by Design: Learning Class Prototypes on the Hypersphere

arXiv:2605.20302v1 Announce Type: cross Abstract: Supervised classification has a theoretical optimum, Neural Collapse (NC), yet neither of its two dominant paradigms reaches it in practice. Cross ent

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Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

DGX agent

arXiv:2605.20187v1 Announce Type: new Abstract: Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these mode

researcharxiv-cs-lg

21 May 2026

Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment

arXiv:2605.21437v1 Announce Type: cross Abstract: Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion

NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding

model-releasesarxiv-cs-cl

arXiv:2605.20525v1 Announce Type: cross Abstract: We present NeuroQA, a large-scale benchmark for visual question answering in 3D brain magnetic resonance imaging (MRI), with 56,953 QA pairs from 12,9

Nonlocal operator learning for fMRI encoding and decoding tasks

arXiv:2605.20389v1 Announce Type: new Abstract: Functional MRI data exhibit high-dimensional spatiotemporal structure, making both prediction and decoding challenging. In this work, we investigate neu

Nonparametric Learning and Earning with One-Point Feedback under Nonstationarity

arXiv:2605.21263v1 Announce Type: new Abstract: Firms increasingly rely on dynamic pricing to respond to evolving customer demand, yet in many applications they observe only the revenue generated by a

OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation

arXiv:2605.21343v1 Announce Type: new Abstract: Recent layout-to-image models have achieved remarkable progress in spatial controllability. However, they still struggle with inter-object occlusion. Wh

OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization

arXiv:2605.21226v1 Announce Type: new Abstract: The key-value (KV) cache dominates memory bandwidth and footprint in long-context autoregressive inference. Recent rotation-preconditioned codecs (Turbo

Hardware

OlmoEarth v1.1: A more efficient family of OlmoEarth models

arXiv:2605.20804v1 Announce Type: new Abstract: We present a set of improvements to the OlmoEarth family. These improvements allow us to cut compute costs during training (1.7 imes reduction in GPU ho

hardwarearxiv-cs-cv

OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization

arXiv:2605.20276v1 Announce Type: new Abstract: The global deployment of edge intelligence operates across heterogeneous legal frameworks. While some regions permit centralized learning (CL) via cloud

local-aiarxiv-cs-lg

On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective

arXiv:2605.21260v1 Announce Type: new Abstract: We develop a learning-theoretic framework for understanding Chain of Thought (CoT). We model CoT as the interaction between an answer map and a chain ru

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

model-releasesarxiv-cs-cl

arXiv:2605.20668v1 Announce Type: new Abstract: With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remai

Tutorials

On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures

arXiv:2605.21388v1 Announce Type: new Abstract: Despite the remarkable empirical success of generative models, the available theory on their statistical accuracy in scientific computing remains largel

tutorialsarxiv-cs-lg

On the Suboptimality of GP-UCB under Polynomial Effective Optimism

arXiv:2312.01386v2 Announce Type: replace Abstract: Gaussian process upper confidence bound (GP-UCB) is widely used for sequential optimization of expensive black-box functions. Although many upper bo

One Operator to Rule Them All? On Boundary-Indexed Operator Families in Neural PDE Solvers

arXiv:2603.01406v2 Announce Type: replace Abstract: Neural PDE solvers are often described as learning solution operators that map problem data to PDE solutions. In this work, we argue that this inter

One-Step Distillation of Discrete Diffusion Image Generators via Fixed-Point Iteration

arXiv:2605.21484v1 Announce Type: new Abstract: Discrete diffusion models excel at visual synthesis but rely on slow, iterative decoding. Existing single-step distillation methods attempt to bypass th

Online 3D Multi-Camera Perception through Robust 2D Tracking and Depth-based Late Aggregation

arXiv:2509.09946v2 Announce Type: replace Abstract: Multi-Target Multi-Camera Tracking (MTMC) is an essential computer vision task for automating large-scale surveillance. With camera calibration and

Applications

Online Conformal Prediction with Corrupted Feedback

arXiv:2605.20515v1 Announce Type: new Abstract: Modern artificial intelligence systems require calibrated uncertainty estimates that remain reliable in sequential and non-stationary environments. Onli

applicationsarxiv-cs-lg

OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI

arXiv:2605.20539v1 Announce Type: new Abstract: The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees

arXiv:2410.15761v4 Announce Type: replace Abstract: Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection, particularly in extractive question answeri

researcharxiv-cs-cl

Optimization Hyper-parameter Laws for Large Language Models

arXiv:2409.04777v4 Announce Type: replace Abstract: Large Language Models have driven significant AI advancements, yet their training is resource-intensive and highly sensitive to hyper-parameter sele

Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search

arXiv:2605.21322v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without centralizing data. However, real-world deployments must simultaneously address stat

local-aiarxiv-cs-lg

Oracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising

arXiv:2605.20479v1 Announce Type: new Abstract: Hyperparameter prediction is a critical practical bottleneck for model-based image denoisers, ranging from classical TV/TGV variational solvers to moder

Ordering Matters: Rank-Aware Selective Fusion for Blended Emotion Recognition

arXiv:2605.21417v1 Announce Type: new Abstract: Blended emotion recognition is challenging because emotions are often expressed as mixtures of subtle and overlapping multimodal cues rather than a sing

OSGNet with MLLM Reranking @ Ego4D Episodic Memory Challenge 2026

arXiv:2605.20818v1 Announce Type: new Abstract: In this report, we present our champion solutions for the Natural Language Queries and GoalStep tracks of the Ego4D Episodic Memory Challenge at CVPR 20

Tutorials

PACD-Net: Pseudo-Augmented Contrastive Distillation for Glycemic Control Estimation from SMBG

arXiv:2605.20751v1 Announce Type: new Abstract: Effective diabetes management requires continuous monitoring of glycemic levels. Clinically, glycemic control is assessed using metrics such as Time in

tutorialsarxiv-cs-lg

PaintCopilot: Modeling Painting as Autonomous Artistic Continuation

arXiv:2605.20941v1 Announce Type: new Abstract: We present PaintCopilot, a co-creative neural painting assistant that models painting as an open-ended autoregressive artistic behavior conditioned on e

Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction

arXiv:2605.20194v1 Announce Type: new Abstract: Large language models (LLMs) have been increasingly used to analyze text. However, they are often plagued with contextual reasoning limitations when ana

safetyarxiv-cs-cl

Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing

model-releasesarxiv-cs-cv

arXiv:2602.06862v2 Announce Type: replace Abstract: Adapting pre-trained vision models using parameter-efficient fine-tuning (PEFT) remains challenging, as it aims to achieve performance comparable to

Agents

ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning

arXiv:2605.20342v1 Announce Type: new Abstract: Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promisin

agentsarxiv-cs-cv

Pareto-Enhanced Portrait Generation: Vision-Aligned Text Supervision for Alignment, Realism, and Aesthetics

arXiv:2605.20640v1 Announce Type: new Abstract: Text-to-image diffusion models often face a severe trilemma in human portrait generation: text-image alignment, photorealism, and human-perceived aesthe

Applications

Perception of Social Robots as Communication Partners in Healthcare for Older Adults

arXiv:2605.21053v1 Announce Type: new Abstract: Addressing the global caregiver shortage through socially assistive robots necessitates a deep understanding of their psychological and physiological im

applicationsarxiv-cs-ro

PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation

arXiv:2602.04876v2 Announce Type: replace Abstract: We introduce PerpetualWonder, a hybrid generative simulator that enables long-horizon, action-conditioned 4D scene generation from a single image. C

Applications

Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence

arXiv:2409.08700v2 Announce Type: replace Abstract: Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores th

applicationsarxiv-cs-lg

PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection

model-releasesarxiv-cs-cv

arXiv:2605.21207v1 Announce Type: new Abstract: The rapid evolution of generative AI, from GANs to modern diffusion models, has resulted in increasingly subtle discriminative clues. These fine-grained

Physics-informed convolutional neural networks for fluid flow through porous media

arXiv:2605.20250v1 Announce Type: new Abstract: Accurate simulation of fluid flow in porous media is challenging due to complex pore-space geometries and the computational cost of solving the Navier-S

Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures

arXiv:2605.20459v1 Announce Type: new Abstract: In recent years, there has been a notable increase in the level of attention that is given to algorithms based on deep learning in the context of medica

PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models

arXiv:2605.20873v1 Announce Type: cross Abstract: Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, reso

Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry

arXiv:2605.20496v1 Announce Type: cross Abstract: The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively

Playing Devil's Advocate: Off-the-Shelf Persona Vectors Rival Targeted Steering for Sycophancy

arXiv:2605.21006v1 Announce Type: cross Abstract: We study the effect of different persona on extbf{sycophancy}: model's agreement with users even when the user is incorrect. The standard mitigation,

researcharxiv-cs-cl

Hardware

PlexRL: Cluster-Level Orchestration of Serviceized LLM Execution for RLVR

arXiv:2605.20863v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently unlocked strong reasoning capabilities in large language models (LLMs), triggering

hardwarearxiv-cs-lg

Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers

arXiv:2605.20289v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical, training-free route to spiking large language models. However, current pipelines primarily focus on spike-driv

Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators

arXiv:2605.20978v1 Announce Type: new Abstract: Graph Network Simulators (GNSs) have emerged as powerful surrogates for complex physics-based simulation, offering inherent differentiability and orders

PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction

arXiv:2605.21414v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation by leveraging large pretrained vision-languag

Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals

arXiv:2605.21428v1 Announce Type: new Abstract: We study the task of agnostic learning of multiclass linear classifiers under the Gaussian distribution. Given labeled examples (x, y) from a distributi

Post-Hoc Understanding of Metaphor Processing in Decoder-Only Language Models via Conditional Scale Entropy

model-releasesarxiv-cs-cl

arXiv:2605.21391v1 Announce Type: new Abstract: Metaphor requires a language model to resolve a token whose contextual meaning diverges from its basic literal sense. Understanding how transformer mode

Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis

arXiv:2603.23890v2 Announce Type: replace-cross Abstract: As the modern microservice architecture for cloud applications grows in popularity, cloud services are becoming increasingly complex and more

PREF: Phasorial Embedding Fields for Compact Neural Representations

arXiv:2205.13524v4 Announce Type: replace Abstract: We present an efficient frequency-based neural representation termed PREF: a shallow MLP augmented with a phasor volume that covers significant bord