Search: “framework”

arXiv:2605.23569v1 Announce Type: new Abstract: Dynamic Programming (DP) and Constraint Programming (CP) are well-established paradigms for solving combinatorial optimization problems. Usually, these

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Cross-attention-based bipartite graph neural network for coupled nodal and elemental field prediction in large-deformation sheet material forming

DGX agent

arXiv:2605.22845v1 Announce Type: cross Abstract: Finite element simulations of large-deformation sheet material forming involve node-element coupling between nodal kinematics and element-level deform

researcharxiv-cs-lg

25 May 2026

CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception

arXiv:2605.23655v1 Announce Type: cross Abstract: High-resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising s

Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Energy-Constrained Mobile Robots

arXiv:2501.08222v2 Announce Type: replace Abstract: We consider the spatial classification problem for monitoring using data collected by a coordinated team of mobile robots. Such classification probl

researcharxiv-cs-ro

Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models

arXiv:2605.23797v1 Announce Type: cross Abstract: Aiming at identifying unexpected inputs from unknown classes, out-of-distribution (OOD) detection has emerged as a pivotal approach to enhancing the r

safetyarxiv-cs-cv

Decomposing and Measuring Evaluation Awareness

arXiv:2605.23055v1 Announce Type: cross Abstract: Frontier language models sometimes recognize that they are being evaluated and adjust their behavior, undermining validity of benchmark results. Yet t

Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization

model-releasesarxiv-cs-cv

arXiv:2605.23355v1 Announce Type: new Abstract: Temporal Action Localization (TAL) has been extensively studied in generic video understanding, while fine-grained sports scenarios, such as professiona

Hardware

DFSAttn: Dynamic Fine-grained Sparse Attention for Efficient Video Generation

arXiv:2605.23445v1 Announce Type: new Abstract: Diffusion transformers have achieved remarkable success in high-quality video generation, yet their reliance on spatiotemporal 3D full attention incurs

hardwarearxiv-cs-cv

Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos

arXiv:2605.23762v1 Announce Type: new Abstract: Imitation Learning from monocular video demonstrations provides a scalable approach for teaching complex skills to humanoid robots. However, translating

safetyarxiv-cs-ro

Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks

arXiv:2605.23635v1 Announce Type: cross Abstract: Traditional neural networks provide deterministic predictions without inherent uncertainty estimates. While Bayesian Neural Networks (BNNs) offer a pr

DrawVideo: Generating Long Video from Storyboard Keyframe Sketches

arXiv:2605.23508v1 Announce Type: cross Abstract: Long video generation requires high-fidelity synthesis, coherent narrative structure, and user control over extended time spans. Existing text-to-vide

researcharxiv-cs-ai

Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise

arXiv:2510.09450v2 Announce Type: replace Abstract: Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast infere

safetyarxiv-cs-cv

Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries

model-releasesarxiv-cs-lg

arXiv:2404.05108v3 Announce Type: replace-cross Abstract: Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and lea

Efficient Learned Image Compression without Entropy Coding

arXiv:2605.23323v1 Announce Type: cross Abstract: Entropy coding is widely used in typical learned image compression (LIC) that converts latents into a compact bitstream. However, entropy coding is ty

researcharxiv-cs-cv

Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention

model-releasesarxiv-cs-cv

arXiv:2605.23451v1 Announce Type: new Abstract: Real-world image super-resolution aims to recover high-quality images from complex and unknown real-world degradations. However, existing generative Rea

Empirical Bayes Conformal Prediction for Vision and Language Models

arXiv:2605.23189v1 Announce Type: new Abstract: Conformal prediction (CP) gives distribution-free coverage for modern vision and language models, but it is often forced to make a ranking decision from

Encrypted Neural Networks without Overflows

arXiv:2605.23096v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computa

Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery

arXiv:2512.22597v2 Announce Type: replace Abstract: Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generat

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems

arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th

safetyarxiv-cs-ai

Enhancing Deep Neural Network Reliability with Refinement and Calibration

arXiv:2605.23249v1 Announce Type: cross Abstract: Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user

researcharxiv-cs-ai

Entropy-Aware On-Policy Distillation of Language Models

arXiv:2603.07079v2 Announce Type: replace-cross Abstract: On-policy distillation is a promising approach for transferring knowledge between language models, where a student learns from dense token-lev

safetyarxiv-cs-cl

Evaluating Counterfactual Strategic Reasoning in Large Language Models

arXiv:2603.19167v2 Announce Type: replace Abstract: We evaluate Large Language Models (LLMs) in repeated game-theoretic settings to assess whether strategic performance reflects genuine reasoning or r

researcharxiv-cs-cl

EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation

arXiv:2605.23271v1 Announce Type: cross Abstract: The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such deman

agentsarxiv-cs-ai

Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking

arXiv:2605.23118v1 Announce Type: cross Abstract: Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-of

Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving

arXiv:2605.23163v1 Announce Type: new Abstract: End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficie

safetyarxiv-cs-cl

Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks

arXiv:2602.00567v2 Announce Type: replace Abstract: The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearn

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning

arXiv:2509.12958v2 Announce Type: replace Abstract: Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to acc

researcharxiv-cs-ai

From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

arXiv:2605.23899v1 Announce Type: new Abstract: Language agents increasingly improve by reusing skills -- structured procedural artifacts distilled from past experience. In particular, domain-level an

agentsarxiv-cs-ai

From Residuals to Reasons: LLM-Guided Mechanism Inference from Tabular Data

arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st

agentsarxiv-cs-lg

FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

model-releasesarxiv-cs-lg

arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral st

FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence

arXiv:2605.22868v1 Announce Type: new Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, late

agentsarxiv-cs-lg

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs

arXiv:2605.23078v1 Announce Type: cross Abstract: Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters.

researcharxiv-cs-cl

Geo-Align: Video Generation Alignment via Metric Geometry Reward

arXiv:2605.23903v1 Announce Type: new Abstract: Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rel

safetyarxiv-cs-cv

GFSR: Geometric Fidelity and Spatial Refinement for Reliable Lane Detection

arXiv:2605.23327v1 Announce Type: new Abstract: Lane detection stands as a crucial perception task in autonomous driving and advanced driver assistance systems. However, existing methods still degrade

agentsarxiv-cs-cv

Tutorials

GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals

arXiv:2504.09846v2 Announce Type: replace-cross Abstract: Frequent and long-term exposure to hyperglycemia increases the risk of chronic complications, including neuropathy, nephropathy, and cardiovas

tutorialsarxiv-cs-ai

Tutorials

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences

arXiv:2605.23183v1 Announce Type: cross Abstract: Contemporary glioma diagnosis integrates molecular features with histopathology to guide clinical decision-making. However, in clinical settings, dive

tutorialsarxiv-cs-cv

Tutorials

Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers

arXiv:2605.23892v1 Announce Type: cross Abstract: Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes

tutorialsarxiv-cs-ai

GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents

arXiv:2602.00979v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as educational agents for automatic short answer grading (ASAG) in real-world education

agentsarxiv-cs-ai

Hardware

Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization

arXiv:2508.10651v3 Announce Type: replace Abstract: We present a novel approach for graph classification based on tabularizing graph data via new variants of the Weisfeiler-Leman algorithm and then ap

hardwarearxiv-cs-lg

HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval

arXiv:2605.23572v1 Announce Type: cross Abstract: In the competitive landscape of sponsored search, balancing retrieval quality with production latency is a critical challenge. While large retrieval m

Applications

Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models

arXiv:2605.23422v1 Announce Type: new Abstract: Learning high-quality oblique decision trees remains a significant challenge due to the discrete and non-convex nature of split optimization. We present

applicationsarxiv-cs-lg

Applications

How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks

arXiv:2605.23583v1 Announce Type: cross Abstract: Inverse Kinematics (IK) plays a critical role in robotic motion planning and control. The IK solutions of a robot manipulator could be done by convent

applicationsarxiv-cs-lg

Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

arXiv:2605.23320v1 Announce Type: new Abstract: Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and

safetyarxiv-cs-ai

ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization

arXiv:2605.22885v1 Announce Type: new Abstract: Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data

Inconsistency-aware Multimodal Schrodinger Bridge for Deepfake Localization

arXiv:2605.23113v1 Announce Type: new Abstract: Audio-visual deepfake localization demands interval-level outputs that serve as temporal evidence. Despite recent progress, symmetric fusion under singl

researcharxiv-cs-cv

InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion

arXiv:2505.13893v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have intensified efforts to fuse heterogeneous open-source models into a unified system that inherit

researcharxiv-cs-cl

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

arXiv:2605.23146v1 Announce Type: cross Abstract: Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy. This assum

safetyarxiv-cs-ai

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking

model-releasesarxiv-cs-lg

arXiv:2603.24226v3 Announce Type: replace-cross Abstract: Scaling studies for industrial search, advertising, and recommendation have largely emphasized enlarging model capacity or refining architectu