Search: “framework”

A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding

arXiv:2606.26078v1 Announce Type: new Abstract: Supervised deep learning has been widely used for weld penetration state classification; however, its performance often degrades significantly under dom

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Research

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks

DGX agent

arXiv:2606.26059v1 Announce Type: new Abstract: The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. A

researcharxiv-cs-cv

25 Jun 2026

Tutorials

Adapt Only When It Pays: Budgeted Decision-Loss Priority for Delayed Online Time-Series Adaptation

arXiv:2606.25068v1 Announce Type: new Abstract: Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute. We study when an

tutorialsarxiv-cs-lg

Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction

arXiv:2606.25003v1 Announce Type: new Abstract: Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange cr

Applications

ADM-Fusion: Adaptive Deep Multi-Sensor Fusion for Robust Ego-Motion Estimation in Diverse Conditions

arXiv:2606.25111v1 Announce Type: cross Abstract: Robust multi-sensor fusion is essential for reliable autonomy in diverse and degraded environments, where sensor reliability can fluctuate rapidly. Be

applicationsarxiv-cs-cv

AeroCast: Probabilistic 3D Trajectory Prediction for Non-Cooperative Aerial Obstacles via Transformer-MDN Architecture

arXiv:2606.25122v1 Announce Type: cross Abstract: Autonomous aerial vehicles operating in shared airspace must predict the future positions of non-cooperative obstacles to plan evasive maneuvers befor

agentsarxiv-cs-lg

Agentic evolution of physically constrained foundation models

arXiv:2606.25532v1 Announce Type: cross Abstract: Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hal

Agentic Knowledge Tracing: A Multi-Agent LLM Architecture for Stealth Assessment of Financial Literacy in Serious Games

arXiv:2606.25358v1 Announce Type: new Abstract: Assessing financial literacy during gameplay without disrupting the learning experience remains a key challenge in serious games for education. We prese

agentsarxiv-cs-ai

AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents

arXiv:2606.24893v1 Announce Type: new Abstract: For agents to learn continuously from interaction with the world at test time, they must be able to explore effectively, acquire new world knowledge and

agentsarxiv-cs-cl

AI Coaching for Accelerating Human Skill Development with Reinforcement Learning

arXiv:2606.25337v1 Announce Type: new Abstract: AI copilots can substantially boost human performance through shared control, but excessive assistance can induce over-reliance and skill atrophy. This

agentsarxiv-cs-ro

AI Snitches Get Glitches: Towards Evading Agentic Surveillance

arXiv:2606.25836v1 Announce Type: new Abstract: To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs. Many employer

agentsarxiv-cs-ai

AIChilles: Automatically Uncovering Hidden Weaknesses in AI-Evolved Systems

model-releasesarxiv-cs-ai

arXiv:2606.15834v2 Announce Type: replace Abstract: The computer systems community has recently seen growing interest in AI-driven system evolution, where AI agents iteratively rewrite systems. Framew

AISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior

arXiv:2606.25503v1 Announce Type: cross Abstract: Reliable depth perception is critical for robotic manipulation, especially for non-Lambertian objects such as transparent or highly specular surfaces,

AMVICC: A Novel Benchmark for Cross-Modal Failure Mode Profiling for VLMs and IGMs

arXiv:2601.17037v2 Announce Type: replace Abstract: We investigate visual reasoning limitations of both multimodal large language models (MLLMs) and image generation models (IGMs) by creating a novel

Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

arXiv:2606.25390v1 Announce Type: new Abstract: Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Ana

Applications

Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints

arXiv:2603.11606v2 Announce Type: replace Abstract: Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view c

applicationsarxiv-cs-cv

BCoughBench: Benchmarking Respiratory Acoustic Foundation Models Under Body-Coupled Wearable Sensor Conditions

arXiv:2606.25116v1 Announce Type: cross Abstract: Respiratory acoustic foundation models (FMs) are benchmarked exclusively on smartphone recordings, yet clinical deployment increasingly targets body-c

researcharxiv-cs-ai

Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset

arXiv:2510.11047v2 Announce Type: replace Abstract: Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Larynge

Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors

arXiv:2606.25527v1 Announce Type: new Abstract: Online reinforcement learning (RL) agents increasingly depend on knowledge acquired offline to achieve practical efficiency. Originally studied in offli

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF

arXiv:2505.12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often

BitNet Text Embeddings

arXiv:2606.25674v1 Announce Type: new Abstract: LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone

researcharxiv-cs-cl

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511.11421v2 Announce Type: replace Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge. Vision-language models s

Bridging Spherical Black-Box Optimizers

arXiv:2606.25761v1 Announce Type: new Abstract: When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative. While Evolution Strategies (ES), Consens

researcharxiv-cs-lg

Local Ai

C2RM-Seg: Causal Counterfactual Reasoning with Structural-Semantic Priors for Weakly Supervised Histopathological Tissue Segmentation

arXiv:2606.25508v1 Announce Type: new Abstract: Histopathological tissue segmentation is essential for computer-aided diagnosis, yet weakly supervised methods often suffer from noisy pseudo-labels gen

local-aiarxiv-cs-cv

Cage-based Texture Transfer with Geometric Filtering

arXiv:2606.25220v1 Announce Type: new Abstract: Real-time texture transfer expands the creative horizon for interactive applications, enabling seamless detail projection in scenarios that range from d

Calousel: Extrinsic Calibration of Non-overlapping Multi-camera Systems from Pure Rotation

arXiv:2606.25646v1 Announce Type: cross Abstract: Extrinsic calibration of multi-camera systems with non-overlapping FOVs has been a challenging problem in the robotics literature. Conventional target

Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models

arXiv:2606.25473v1 Announce Type: new Abstract: Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-co

safetyarxiv-cs-cv

CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG

model-releasesarxiv-cs-ai

arXiv:2602.05143v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a

Chorus II: Cross-Request Sparsity Reuse for Efficient Image-to-Video Generation

arXiv:2606.25040v1 Announce Type: new Abstract: Serving diffusion models for image-to-video generation is computationally expensive, posing significant challenges for large-scale deployment. Real I2V

CKM-Driven Communication-Aware UAV Intelligent Trajectory Optimization for Urban Inspection

arXiv:2606.24979v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) are increasingly employed in urban inspection tasks, where reliable communication is critical but challenging due to the

researcharxiv-cs-lg

Closed-Loop Graph Algorithm Execution with Small Language Models: Step Accuracy and Rollout Reliability

arXiv:2606.24980v1 Announce Type: new Abstract: Small language models offer an efficient alternative to large-scale systems, but their ability to execute structured algorithms over multiple dependent

agentsarxiv-cs-lg

Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks

arXiv:2606.25858v1 Announce Type: cross Abstract: Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance. In t

Communicability-Inspired Positional Encoding (CIPE)

arXiv:2606.25293v1 Announce Type: new Abstract: Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings shou

Consistent Zero-Shot Imitation with Contrastive Goal Inference

arXiv:2510.17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction o

Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model

arXiv:2507.09052v3 Announce Type: replace Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes. Such an

safetyarxiv-cs-cv

Applications

Cross-Modal Robustness Transfer (CMRT): Training Robust Speech Translation Models Using Adversarial Text

arXiv:2602.11933v2 Announce Type: replace Abstract: End-to-End Speech Translation (E2E-ST) has seen significant advancements, yet current models are primarily benchmarked on curated, 'clean' datasets.

applicationsarxiv-cs-cl

Tutorials

Cross-Modality Structural Guidance in 3D Latent Diffusion for Robust FLAIR Super-Resolution

arXiv:2606.25255v1 Announce Type: new Abstract: High-resolution (HR) MRI acquisition is often hampered by scan time constraints, resulting in anisotropic or low-resolution scans (e.g., thick-slice FLA

tutorialsarxiv-cs-cv

CrossAccent-TTS: Cross-Lingual Accent-Intensity Controllable Text-to-Speech via Disentangled Speaker and Accent Representations

arXiv:2606.25403v1 Announce Type: cross Abstract: Accent conversion and controllability remain fundamental challenges in cross-lingual text-to-speech (TTS), particularly for low-resource and phonetica

researcharxiv-cs-ai

Curvature-Guided Mixing for MLLM Adaptation

arXiv:2606.24963v1 Announce Type: new Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) on specialized tasks often leads to catastrophic forgetting of their general capabilities. Existing

daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization

arXiv:2606.16497v2 Announce Type: replace-cross Abstract: GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present da

safetyarxiv-cs-cl

Deep Neural Networks with Ordinal Loss for Medical Applications

arXiv:2606.25769v1 Announce Type: new Abstract: In many prediction problems in medical applications, target labels exhibit an inherent ordinal structure, where class ordering reflects clinically meani

Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm

arXiv:2606.26048v1 Announce Type: new Abstract: Soft robots are challenging to control due to their nonlinear and time-varying dynamics. Data-enabled predictive control (DeePC) offers a model-free alt

safetyarxiv-cs-ro

DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning

arXiv:2606.25939v1 Announce Type: new Abstract: Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation du

safetyarxiv-cs-ro

Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning

model-releasesarxiv-cs-cl

arXiv:2606.26036v1 Announce Type: new Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization,

Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography

arXiv:2606.25546v1 Announce Type: new Abstract: Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet e

Distill on a Diet: Efficient Knowledge Distillation via Learnable Data Pruning

arXiv:2606.25488v1 Announce Type: new Abstract: Knowledge Distillation (KD) is widely used to obtain compact models for efficient inference in resource-constrained environments. Yet the computational

Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation

arXiv:2606.25254v1 Announce Type: cross Abstract: Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the

safetyarxiv-cs-cv

Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs

arXiv:2606.25758v1 Announce Type: new Abstract: While test-time adaptation (TTA) empowers vision-language models to adapt without costly retraining, it remains highly vulnerable to out-of-distribution