Search: “framework”

MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware

arXiv:2605.05945v2 Announce Type: replace-cross Abstract: The recent advancement of Vision Language Action (VLA) models has driven a critical demand for large scale egocentric datasets. However, exist

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Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

DGX agent

arXiv:2602.15451v3 Announce Type: replace-cross Abstract: Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. H

researcharxiv-cs-lg

13 May 2026

Local Ai

MotionGRPO: Overcoming Low Intra-Group Diversity in GRPO-Based Egocentric Motion Recovery

arXiv:2605.05680v2 Announce Type: replace Abstract: This paper studies full-body 3D human motion recovery from head-mounted device signals. Existing diffusion-based methods often rely on global distri

local-aiarxiv-cs-cv

Multimodal Abstractive Summarization of Instructional Videos with Vision-Language Models

arXiv:2605.11959v1 Announce Type: cross Abstract: Multimodal video summarization requires visual features that align semantically with language generation. Traditional approaches rely on CNN features

MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization

model-releasesarxiv-cs-lg

arXiv:2605.11396v1 Announce Type: new Abstract: The Muon optimizer has emerged as a compelling alternative to Adam for training large language models, achieving remarkable computational savings throug

Nautilus: From One Prompt to Plug-and-Play Robot Learning

model-releasesarxiv-cs-ro

arXiv:2605.11665v1 Announce Type: new Abstract: Robot learning research is fragmented across policy families, benchmark suites, and real robots; each implementation is entangled with the others in a c

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

model-releasesarxiv-cs-lg

arXiv:2509.06697v2 Announce Type: replace-cross Abstract: Accurate forecasting of exchange rates remains a persistent challenge, particularly for emerging economies such as Brazil, Russia, India, and

Local Ai

Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale

arXiv:2605.12343v1 Announce Type: new Abstract: Most learned PDE solvers follow a global-surrogate paradigm: a neural operator is trained to map full problem descriptions to full solution fields for a

local-aiarxiv-cs-lg

Neural Statistical Functions

arXiv:2605.11327v1 Announce Type: new Abstract: Classical deep learning typically operates on individual cases. Despite its success, real-world usage often requires repeated inference to estimate stat

NOFE -- Neural Operator Function Embedding

arXiv:2605.11970v1 Announce Type: new Abstract: Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous domain structure inherent to many real-world processe

Offline Policy Evaluation for Manipulation Policies via Discounted Liveness Formulation

arXiv:2605.11479v1 Announce Type: new Abstract: Policy evaluation is a fundamental component of the development and deployment pipeline for robotic policies. In modern manipulation systems, this probl

safetyarxiv-cs-ro

OGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning

arXiv:2605.12400v1 Announce Type: new Abstract: We study {on-policy self-distillation} (OPSD), where a language model improves its reasoning ability by distilling privileged teacher distributions alon

OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation

arXiv:2605.12038v1 Announce Type: new Abstract: Cross-embodiment video generation aims to transfer motions across different humanoid embodiments, such as human-to-robot and robot-to-robot, enabling sc

applicationsarxiv-cs-cv

OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation

arXiv:2605.12480v1 Announce Type: new Abstract: Recent advances in joint audio-video generation have been remarkable, yet real-world applications demand strong per-modality fidelity, cross-modal align

safetyarxiv-cs-cv

On Predicting the Post-training Potential of Pre-trained LLMs

arXiv:2605.11978v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. Howev

One-Step Generative Modeling via Wasserstein Gradient Flows

arXiv:2605.11755v1 Announce Type: cross Abstract: Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it

Online Learning-to-Defer with Varying Experts

arXiv:2605.12340v1 Announce Type: cross Abstract: Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batc

Optimistic Dual Averaging Unifies Modern Optimizers

arXiv:2605.11172v1 Announce Type: new Abstract: We introduce SODA, a generalization of Optimistic Dual Averaging, which provides a common perspective on state-of-the-art optimizers like Muon, Lion, Ad

researcharxiv-cs-lg

ORCE: Order-Aware Alignment of Verbalized Confidence in Large Language Models

arXiv:2605.12446v1 Announce Type: cross Abstract: Large language models (LLMs) often produce answers with high certainty even when they are incorrect, making reliable confidence estimation essential f

OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models

arXiv:2605.11803v1 Announce Type: new Abstract: As Video Large Language Models (Video-LLMs) scale to longer and more complex videos, their inference cost grows rapidly due to the large volume of visua

Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models

arXiv:2605.11459v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLA

OverNaN: NaN-Aware Oversampling for Imbalanced Learning with Meaningful Missingness

arXiv:2605.11525v1 Announce Type: new Abstract: Missing values are routinely treated as defects to be eliminated through deletion or imputation prior to machine learning. In many applied domains, howe

Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion

arXiv:2605.11178v1 Announce Type: new Abstract: Neural Sheaf Diffusion (NSD) generalizes diffusion-based Graph Neural Networks by replacing scalar graph Laplacians with sheaf Laplacians whose learned

PairDropGS: Paired Dropout-Induced Consistency Regularization for Sparse-View Gaussian Splatting

arXiv:2605.12072v1 Announce Type: new Abstract: Dropout-based sparse-view 3D Gaussian Splatting (3DGS) methods alleviate overfitting by randomly suppressing Gaussian primitives during training. Existi

PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming

arXiv:2605.11427v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must downlo

Physics-Informed Graph Neural Networks for Frequency-Aware Optical Aberration Correction

arXiv:2512.05683v2 Announce Type: replace Abstract: Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples. These aberrations arise from d

safetyarxiv-cs-cv

Agents

PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations

arXiv:2605.11594v1 Announce Type: new Abstract: High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable f

agentsarxiv-cs-cv

PoseBridge: Bridging the Skeletonization Gap for Zero-Shot Skeleton-Based Action Recognition

arXiv:2605.11497v1 Announce Type: new Abstract: Zero-shot skeleton-based action recognition (ZSSAR) is typically treated as a skeleton-text alignment problem: encode joint-coordinate sequences, align

Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling

arXiv:2605.11299v1 Announce Type: cross Abstract: Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, of

Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

arXiv:2605.10984v1 Announce Type: new Abstract: Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as

Principled Latent Diffusion for Graphs via Laplacian Autoencoders

arXiv:2601.13780v3 Announce Type: replace Abstract: Graph diffusion models achieve state-of-the-art performance in graph generation but suffer from quadratic complexity in the number of nodes -- and m

researcharxiv-cs-lg

PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents

model-releasesarxiv-cs-cl

arXiv:2605.12260v1 Announce Type: new Abstract: Long-horizon language agents accumulate conversation history far faster than any fixed context window can hold, making memory management critical to bot

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

model-releasesarxiv-cs-lg

arXiv:2605.12194v1 Announce Type: cross Abstract: Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access t

Quantifying the Reconstructability of Astrophysical Methods with Large Language Models and Information Theory: A Case Study in Spectral Reconstruction

arXiv:2605.11154v1 Announce Type: cross Abstract: Modern astrophysical studies rely heavily on complex data analysis pipelines; however, published descriptions often lack the detail required for compu

Agents

Random-Set Graph Neural Networks

arXiv:2605.11987v1 Announce Type: cross Abstract: Uncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite

agentsarxiv-cs-lg

READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling

model-releasesarxiv-cs-cl

arXiv:2312.06950v3 Announce Type: replace-cross Abstract: Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal

ReAD: Reinforcement-Guided Capability Distillation for Large Language Models

arXiv:2605.11290v1 Announce Type: new Abstract: Capability distillation applies knowledge distillation to selected model capabilities, aiming to compress a large language model (LLM) into a smaller on

Real-Time Whole-Body Teleoperation of a Humanoid Robot Using IMU-Based Motion Capture with Sim2Sim and Sim2Real Validation

arXiv:2605.12347v1 Announce Type: new Abstract: Stable, low-latency whole-body teleoperation of humanoid robots is an open research challenge, complicated by kinematic mismatches between human and rob

researcharxiv-cs-ro

RealDiffusion: Physics-informed Attention for Multi-character Storybook Generation

arXiv:2605.11927v1 Announce Type: new Abstract: While modern diffusion models excel at generating diverse single images, extending this to sequential generation reveals a fundamental challenge: balanc

Red-Teaming Text-to-Image Models via In-Context Experience Replay and Semantic-Preserving Prompt Rewriting

arXiv:2411.16769v3 Announce Type: replace-cross Abstract: Understanding the capabilities of text-to-image (T2I) models in harmful content generation is essential to safety and compliance. However, hum

Resilient Vision-Tabular Multimodal Learning under Modality Missingness

arXiv:2605.12031v1 Announce Type: cross Abstract: Multimodal deep learning has shown strong potential in medical applications by integrating heterogeneous data sources such as medical images and struc

applicationsarxiv-cs-cv

RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition

arXiv:2605.11818v1 Announce Type: new Abstract: Recent diffusion-based approaches have made substantial progress in image layer decomposition. However, accurately decomposing complex natural images re

Tutorials

Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction

arXiv:2605.12494v1 Announce Type: new Abstract: Surface reconstruction with differentiable rendering has achieved impressive performance in recent years, yet the pervasive photometric ambiguities have

tutorialsarxiv-cs-cv

Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning

arXiv:2605.11659v1 Announce Type: new Abstract: Cross-Domain Few-Shot Learning (CDFSL) aims to adapt large-scale pretrained models to specialized target domains with limited samples, yet the few-shot

RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning

arXiv:2605.11564v1 Announce Type: new Abstract: Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to sho

safetyarxiv-cs-ro

RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction

arXiv:2605.11622v1 Announce Type: new Abstract: Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular archit

Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations

arXiv:2605.11502v1 Announce Type: new Abstract: Accurately and consistently indexing biomedical literature by publication type and study design is essential for supporting evidence synthesis and knowl

Robust Policy Optimization to Prevent Catastrophic Forgetting

arXiv:2602.08813v2 Announce Type: replace Abstract: Large language models are commonly trained through multi-stage post-training: first via RLHF, then fine-tuned for other downstream objectives. Yet e