9916 papers
This paper proposes DSA-SRGS, the first super-resolution Gaussian splatting framework for dynamic sparse-view DSA reconstruction, which integrates a Multi-Fidelity Texture Learning Module with confidence-aware supervision and Radiative Sub-Pixel Densification to recover fine-grained vascular details while avoiding blurring and hallucination artifacts.
The paper proposes MADCrowner, a margin-aware framework that combines a template deformation network (CrownDeformR) with a novel margin segmentation network (CrownSegger) to automatically generate high-precision, clinically feasible dental crowns by addressing limitations in spatial resolution and surface overextension found in existing learning-based methods.
TSEmbed is a universal multimodal embedding framework that addresses task conflict by synergizing Mixture-of-Experts with Low-Rank Adaptation and introducing Expert-Aware Negative Sampling to achieve state-of-the-art performance on both benchmark and industrial datasets.
This paper introduces RLSTA, a reinforcement learning framework that leverages single-turn capabilities as stable anchors to overcome "Contextual Inertia," thereby enabling large language models to effectively integrate new information and maintain robust reasoning in multi-turn interactions.
The paper introduces Timer-S1, an 8.3B-parameter Mixture-of-Experts time series foundation model that achieves state-of-the-art forecasting performance through a novel "Serial Scaling" strategy across architecture, dataset, and training pipeline, featuring a specialized Serial-Token Prediction objective and a curated trillion-point dataset called TimeBench.
This paper introduces "LAW & ORDER," a dual-adapter framework that employs Learnable Adaptive Weighting to stabilize diffusion-based medical image synthesis and Optimal Region Detection to enhance efficient segmentation, collectively addressing spatial imbalance to significantly improve generative quality and segmentation accuracy while maintaining a lightweight model architecture.
This review paper evaluates traditional and deep learning methods for brain glioma segmentation and classification, concluding that convolutional neural network architectures outperform traditional techniques in post-MRI analysis to enhance treatment planning and patient outcomes.
This paper proposes Clustering-Sampling-Voting (CSV), a novel framework that significantly reduces the linear latency and token costs of semantic filtering in large language models by embedding tuples into semantic clusters, sampling subsets for evaluation, and inferring cluster-level labels through voting strategies, thereby achieving sublinear complexity with strong error guarantees.
This paper proposes Diffusion Contrastive Reconstruction (DCR), a method that injects contrastive signals derived from reconstructed images into the diffusion process to resolve gradient conflicts and jointly optimize both discriminative and detail-perceptive abilities, thereby overcoming the limitations of CLIP's visual encoder for balanced visual representation.
This paper introduces the Attention Gravitational Field (AGF) concept, which decouples positional encodings from semantic embeddings to optimize Large Language Model architecture, demonstrating that positional correlations follow a power-law distribution consistent with Newton's Law of Universal Gravitation and yielding superior accuracy and interpretability.
The paper presents Meta-D, a metadata-aware architecture that leverages categorical scanner information to dynamically modulate feature extraction for improved 2D brain tumor detection and to serve as a robust anchor for cross-attention mechanisms in 3D missing-modality segmentation, achieving significant performance gains and parameter reduction.
EchoGuard is an agentic AI framework that utilizes a Knowledge Graph-based memory system to track longitudinal dialogue, detect psychologically grounded manipulative patterns, and guide users toward self-discovery through targeted Socratic prompts.
This paper introduces AIS-TGNN, a framework that combines Temporal Graph Attention Networks with structured Large Language Models to predict port congestion using AIS data while generating operationally interpretable, evidence-grounded natural language explanations that maintain high predictive accuracy and directional consistency.
This paper investigates the generalization capabilities of a multimodal foundation model fine-tuned on diverse synthetic interactive data for the novel task of Open-Set Corrective Assistance, demonstrating that effective open-set assistive intelligence requires datasets encompassing multimodal grounding, defect inference, and exposure to varied scenarios.
The paper proposes VISA, a closed-loop framework that utilizes Group Relative Policy Optimization to inject fine-grained human values into Large Language Models while preserving semantic integrity and mitigating the alignment tax typically caused by standard fine-tuning.
This paper introduces a multilevel training framework for Kolmogorov-Arnold Networks (KANs) that leverages their structural equivalence to multichannel MLPs and the properties of spline basis functions to create a properly nested hierarchy of models, resulting in orders-of-magnitude improvements in training accuracy and speed, particularly for physics-informed neural networks.
SCoUT is a scalable multi-agent reinforcement learning framework that improves coordination by dynamically grouping agents into latent clusters for efficient value estimation and employing counterfactual advantages to precisely guide decentralized communication decisions on when and whom to message.
This paper introduces the Dynamic Behavioral Constraint (DBC) benchmark, a model-agnostic, inference-time governance framework that demonstrates a 36.8% relative reduction in risk exposure and enhanced EU AI Act compliance across multiple LLM families compared to standard safety prompts, validated through a rigorous, taxonomy-driven red-teaming protocol.
This paper presents a novel framework for the simultaneous acquisition of real-time MRI, EEG, and surface EMG to capture brain, muscle, and articulatory activity during speech, featuring a specialized artifact suppression pipeline to overcome technical challenges and enable unprecedented insights into speech neuroscience.
This paper introduces a training-free, non-parametric approach to multi-step theorem prediction that overcomes the scalability limitations of vanilla in-context learning by leveraging Theorem Precedence Graphs to encode temporal dependencies and impose topological constraints, achieving state-of-the-art accuracy on the FormalGeo7k benchmark without gradient-based optimization.