arXiv:2603.21987v1 Announce Type: cross Abstract: Autonomous vehicles face major perception and navigation challenges in adverse weather such as rain, fog, and snow, which degrade the performance of LiDAR, RADAR, and RGB camera sensors. While each sensor type offers unique strengths, such as RADAR robustness in poor visibility and LiDAR precision in clear conditions, they also […]
Exploring Multi-Objective Trade-offs in Reference Compound Selection for Validation Studies of Toxicity Assays
arXiv:2505.07140v3 Announce Type: replace Abstract: In chemical safety assessment, validation studies rely on reference compound lists to evaluate the applicability of alternative methods prior to regulatory acceptance. These lists are expected to cover multiple aspects, including chemical structure, physicochemical properties, and toxicity profiles. In practice, however, trade-offs among these aspects are typically addressed implicitly through […]
S5-SHB Agent: Society 5.0 enabled Multi-model Agentic Blockchain Framework for Smart Home
arXiv:2603.05027v2 Announce Type: replace Abstract: The smart home is a key application domain within the Society 5.0 vision for a human-centered society. As smart home ecosystems expand with heterogeneous IoT protocols, diverse devices, and evolving threats, autonomous systems must manage comfort, security, energy, and safety for residents. Such autonomous decision-making requires a trust anchor, making […]
UASTrack: A Unified Adaptive Selection Framework with Modality-Customization in Single Object Tracking
arXiv:2502.18220v2 Announce Type: replace-cross Abstract: Multi-modal tracking is essential in single-object tracking (SOT), as different sensor types contribute unique capabilities to overcome challenges caused by variations in object appearance. However, existing unified RGB-X trackers (X represents depth, event, or thermal modality) either rely on the task-specific training strategy for individual RGB-X image pairs or fail […]
Long Chain-of-Thought Reasoning Across Languages
arXiv:2508.14828v3 Announce Type: replace-cross Abstract: While large reasoning models have shown remarkable ability to generate long chains-of-thought (CoTs) in English, we still lack understanding of how these long-form reasoning abilities transfer to the vast majority of the world’s languages. In this work, we systematically investigate four key stages of model development–scaling, pretraining, post-training, and inference–to […]
Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization
arXiv:2511.22169v2 Announce Type: replace-cross Abstract: Accurate long horizon forecasting of particulate matter (PM) concentration fields is essential for operational public health decisions. However, achieving reliable forecasts remains challenging in regions with complex terrain and strong atmospheric dynamics such as East Asia. While foundation models such as Aurora offer global generality, they often miss region-specific dynamics […]
Fast-WAM: Do World Action Models Need Test-time Future Imagination?
arXiv:2603.16666v2 Announce Type: replace-cross Abstract: World Action Models (WAMs) have emerged as a promising alternative to Vision-Language-Action (VLA) models for embodied control because they explicitly model how visual observations may evolve under action. Most existing WAMs follow an imagine-then-execute paradigm, incurring substantial test-time latency from iterative video denoising, yet it remains unclear whether explicit future […]
Nuanced Emotion Recognition Based on a Segment-based MLLM Framework Leveraging Qwen3-Omni for AH Detection
arXiv:2603.13406v2 Announce Type: replace-cross Abstract: Emotion recognition in videos is a pivotal task in affective computing, where identifying subtle psychological states such as Ambivalence and Hesitancy holds significant value for behavioral intervention and digital health. Ambivalence and Hesitancy states often manifest through cross-modal inconsistencies such as discrepancies between facial expressions, vocal tones, and textual semantics, […]
Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences
arXiv:2603.21508v1 Announce Type: cross Abstract: Machine learning models are widely integrated into modern mobile apps to analyze user behaviors and deliver personalized services. Ensuring low-latency on-device model execution is critical for maintaining high-quality user experiences. While prior research has primarily focused on accelerating model inference with given input features, we identify an overlooked bottleneck in […]
DiT-Flow: Speech Enhancement Robust to Multiple Distortions based on Flow Matching in Latent Space and Diffusion Transformers
arXiv:2603.21608v1 Announce Type: cross Abstract: Recent advances in generative models, such as diffusion and flow matching, have shown strong performance in audio tasks. However, speech enhancement (SE) models are typically trained on limited datasets and evaluated under narrow conditions, limiting real-world applicability. To address this, we propose DiT-Flow, a flow matching-based SE framework built on […]
Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration
arXiv:2603.21760v1 Announce Type: cross Abstract: Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subjects. While recent deep learning-based approaches have significantly improved computational efficiency, many existing methods remain limited in capturing long-range anatomical correspondence and maintaining deformation consistency. In this work, we present a […]
SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation
arXiv:2603.21904v1 Announce Type: cross Abstract: Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffering from semantically unaware feature alignment that results in poor distributional fidelity and from pseudo-label validation that disregards global anatomical constraints, thus failing to prevent the formation of globally implausible […]