Search: “framework”

DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT

arXiv:2506.17501v3 Announce Type: replace-cross Abstract: Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience

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Dual-Temporal LSTM with Hybrid Attention for Airline Passenger Load Factor Forecasting: Integrating Intra-Flight and Inter-Flight Booking Dynamics

DGX agent

arXiv:2605.11569v1 Announce Type: cross Abstract: Accurate short-term demand forecasting is crucial to airline revenue management, yet most existing systems fail to meet this need because current mode

applicationsarxiv-cs-lg

13 May 2026

Agents

Dynamic Full-body Motion Agent with Object Interaction via Blending Pre-trained Modular Controllers

arXiv:2605.11369v1 Announce Type: new Abstract: Generating physically plausible dynamic motions of human-object interaction (HOI) remains challenging, mainly due to existing HOI datasets limited to st

agentsarxiv-cs-cv

ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models

arXiv:2605.10993v1 Announce Type: new Abstract: Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks. Existing mem

applicationsarxiv-cs-ro

ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting

arXiv:2605.12196v1 Announce Type: new Abstract: Accurate ultra-short-term wind power forecasting is critical for grid dispatch and reserve management, yet remains challenging due to the non-stationary

EDGER: EDge-Guided with HEatmap Refinement for Generalizable Image Forgery Localization

arXiv:2605.12002v1 Announce Type: new Abstract: Text-guided inpainting has made image forgery increasingly realistic, challenging both SID and IFL. However, existing methods often struggle to point ou

Efficient and provably convergent end-to-end training of deep neural networks with linear constraints

arXiv:2605.11526v1 Announce Type: cross Abstract: Training a deep neural network with the outputs of selected layers satisfying linear constraints is required in many contemporary data-driven applicat

applicationsarxiv-cs-lg

Efficient Bayesian Inference from Noisy Pairwise Comparisons

arXiv:2510.09333v2 Announce Type: replace-cross Abstract: Evaluating generative models is challenging because standard metrics often fail to reflect human preferences. Human evaluations are more relia

Efficient LLM Reasoning via Variational Posterior Guidance with Efficiency Awareness

arXiv:2605.11019v1 Announce Type: new Abstract: Although large language models rely on chain-of-thought for complex reasoning, the overthinking phenomenon severely degrades inference efficiency. Exist

EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras

model-releasesarxiv-cs-cv

arXiv:2605.12297v1 Announce Type: new Abstract: Egocentric 3D hand pose estimation and gesture recognition are essential for immersive augmented/virtual reality, human-computer interaction, and roboti

EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera

arXiv:2605.12498v1 Announce Type: new Abstract: Reconstructing the absolute 3D pose and shape of the hands from the user's viewpoint using a single head-mounted camera is crucial for practical egocent

EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records

arXiv:2605.12335v1 Announce Type: cross Abstract: Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effect

EndoVGGT: GNN-Enhanced Depth Estimation for Surgical 3D Reconstruction

arXiv:2603.24577v2 Announce Type: replace Abstract: Accurate 3D reconstruction of deformable soft tissues is essential for surgical robotic perception. However, low-texture surfaces, specular highligh

Enhancing Domain Generalization in 3D Human Pose Estimation through Controllable Generative Augmentation

arXiv:2605.12198v1 Announce Type: new Abstract: Pedestrian motion, due to its causal nature, is strongly influenced by domain gaps arising from discrepancies between training and testing data distribu

applicationsarxiv-cs-cv

Enhancing Multilingual Counterfactual Generation through Alignment-as-Preference Optimization

arXiv:2605.11632v1 Announce Type: new Abstract: Self-generated counterfactual explanations (SCEs) are minimally modified inputs (minimality) generated by large language models (LLMs) that flip their o

safetyarxiv-cs-cl

Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control

arXiv:2605.11775v1 Announce Type: cross Abstract: Policy entropy has emerged as a fundamental measure for understanding and controlling exploration in reinforcement learning with verifiable rewards (R

safetyarxiv-cs-cl

Environment-Adaptive Preference Optimization for Wildfire Prediction

arXiv:2605.12435v1 Announce Type: new Abstract: Predicting rare extreme events such as wildfires from meteorological data requires models that remain reliable under evolving environmental conditions.

applicationsarxiv-cs-lg

EPIC: Efficient Predicate-Guided Inference-Time Control for Compositional Text-to-Image Generation

arXiv:2605.11722v1 Announce Type: new Abstract: Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objects, counts,

Evolutionary Task Discovery: Advancing Reasoning Frontiers via Skill Composition and Complexity Scaling

arXiv:2605.11666v1 Announce Type: new Abstract: The reasoning frontier of Large Language Models (LLMs) has advanced significantly through modern post-training paradigms (e.g., Reinforcement Learning f

EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models

arXiv:2605.11859v1 Announce Type: new Abstract: Robot navigation is a crucial task with applications to social robots in dynamic human environments. While Reinforcement Learning (RL) has shown great p

safetyarxiv-cs-ro

Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning

arXiv:2605.11815v1 Announce Type: new Abstract: Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for join

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

arXiv:2605.11752v1 Announce Type: new Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods ass

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

arXiv:2602.23638v2 Announce Type: replace Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a dis

local-aiarxiv-cs-lg

FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity

arXiv:2605.11869v1 Announce Type: new Abstract: While the overall inference latency of Video Diffusion Transformers (DiTs) can be substantially reduced through model distillation, per-step inference l

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning

arXiv:2511.14715v3 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who

Focusable Monocular Depth Estimation

model-releasesarxiv-cs-cv

arXiv:2605.11756v1 Announce Type: new Abstract: Monocular depth foundation models generalize well across scenes, yet they are typically optimized with uniform pixel-wise objectives that do not disting

Agents

Focusing Influence Mechanism for Multi-Agent Reinforcement Learning

arXiv:2506.19417v2 Announce Type: replace Abstract: Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrat

agentsarxiv-cs-lg

Fractal Graph Contrastive Learning

arXiv:2505.11356v4 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) relies on semantically consistent graph augmentations, but common local perturbations provide limited control over

applicationsarxiv-cs-lg

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training

model-releasesarxiv-cs-cl

arXiv:2605.11416v1 Announce Type: new Abstract: Selective layer-wise updates are essential for low-cost continued pre-training of Large Language Models (LLMs), yet determining which layers to freeze o

From Image Hashing to Scene Change Detection

arXiv:2605.12259v1 Announce Type: new Abstract: Image hashing provides compact representations for efficient storage and retrieval but is inherently limited to global comparison and cannot reason abou

From Message-Passing to Linearized Graph Sequence Models

arXiv:2605.12358v1 Announce Type: new Abstract: Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of moder

From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD

arXiv:2605.11176v1 Announce Type: cross Abstract: We present a neural-network-based data processing pipeline for Baikal-GVD, designed to improve event reconstruction quality and accelerate neutrino ca

Agents

From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation

arXiv:2605.11951v1 Announce Type: new Abstract: Although robotic manipulation has made significant progress, reliable execution remains challenging because task failures are inevitable in dynamic and

agentsarxiv-cs-ro

Fully AI-Generated Image Detection: Definition, Recent Advances and Challenges

arXiv:2502.19716v2 Announce Type: replace Abstract: Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source

FuTCR: Future-Targeted Contrast and Repulsion for Continual Panoptic Segmentation

arXiv:2605.12451v1 Announce Type: new Abstract: Continual Panoptic Segmentation (CPS) requires methods that can quickly adapt to new categories over time. The nature of this dense prediction task mean

G^2TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

arXiv:2605.12309v1 Announce Type: new Abstract: The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. I

GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization

arXiv:2605.12431v1 Announce Type: new Abstract: Conventional gait de-identification methods often encounter an inherent trade-off: they either provide insufficient identity suppression or introduce sp

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation

arXiv:2605.11853v1 Announce Type: cross Abstract: Reinforcement learning has become a widely used post-training approach for LLM agents, where training commonly relies on outcome-level rewards that pr

safetyarxiv-cs-cl

Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer

arXiv:2605.11414v1 Announce Type: new Abstract: While traditional time-series classifiers assume full sequences at inference, practical constraints (latency and cost) often limit inputs to partial pre

GeneZip: Region-Aware Compression for Long Context DNA Modeling

arXiv:2602.17739v3 Announce Type: replace-cross Abstract: Long-context DNA models are limited by token-mixing cost and by how compression allocates representational budget across the genome. Existing

GeomHerd: A Forward-looking Herding Quantification via Ricci Flow Geometry on Agent Interactive Simulations

arXiv:2605.11645v1 Announce Type: cross Abstract: Herding -- where agents align their behaviors and act collectively -- is a central driver of market fragility and systemic risk. Existing approaches t

GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction

arXiv:2605.12399v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent paradigm for 3D reconstruction and novel view synthesis. However, it remains vulnerable to sever

Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters

arXiv:2605.11838v1 Announce Type: new Abstract: Gradient clipping is a standard safeguard for training neural networks under noisy, heavy-tailed stochastic gradients; yet, most clipping rules treat al

Gradient-Free Noise Optimization for Reward Alignment in Generative Models

arXiv:2605.11347v1 Announce Type: cross Abstract: Existing reward alignment methods for diffusion and flow models rely on multi-step stochastic trajectories, making them difficult to extend to determi

safetyarxiv-cs-cv

GRAFT: Graph-Tokenized LLMs for Tool Planning

arXiv:2605.11706v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to complete complex tasks by selecting and coordinating external tools across multiple steps. This re

Grounding by Remembering: Cross-Scene and In-Scene Memory for 3D Functional Affordances

arXiv:2605.11616v1 Announce Type: new Abstract: Functional affordance grounding requires more than recognizing an object: an agent must localize the specific region that supports an interaction, such

Tutorials

GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

arXiv:2605.12369v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Exist

tutorialsarxiv-cs-ro

H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows

arXiv:2605.12252v1 Announce Type: new Abstract: Metal artifacts in computed tomography (CT) severely degrade image quality, compromising diagnostic accuracy and radiotherapy planning, especially in ca