arXiv:2511.07901v1 Announce Type: new Abstract: Negative sampling (NS) strategies play a crucial role in knowledge graph representation. In order to overcome the limitations of existing negative sampling strategies, such as vulnerability to false negatives, limited generalization, and lack of control over sample hardness, we propose DANS-KGC (Diffusion-based Adaptive Negative Sampling for Knowledge Graph Completion). DANS-KGC […]
ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
arXiv:2511.08046v1 Announce Type: cross Abstract: Automated medical image segmentation suffers from high inter-observer variability, particularly in tasks such as lung nodule delineation, where experts often disagree. Existing approaches either collapse this variability into a consensus mask or rely on separate model branches for each annotator. We introduce ProSona, a two-stage framework that learns a continuous […]
A robust methodology for long-term sustainability evaluation of Machine Learning models
arXiv:2511.08120v1 Announce Type: cross Abstract: Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, yet existing regulatory and reporting practices lack standardized, model-agnostic evaluation protocols. Current assessments often measure only short-term experimental resource usage and disproportionately emphasize batch learning settings, failing to reflect real-world, long-term AI lifecycles. In […]
An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models
arXiv:2511.08077v1 Announce Type: cross Abstract: The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalability issues […]
Neurophysiological Characteristics of Adaptive Reasoning for Creative Problem-Solving Strategy
arXiv:2511.07912v1 Announce Type: new Abstract: Adaptive reasoning enables humans to flexibly adjust inference strategies when environmental rules or contexts change, yet its underlying neural dynamics remain unclear. This study investigated the neurophysiological mechanisms of adaptive reasoning using a card-sorting paradigm combined with electroencephalography and compared human performance with that of a multimodal large language model. […]
2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time
arXiv:2511.08224v1 Announce Type: cross Abstract: We introduce 2Dto3D-SR, a versatile framework for real-time single-view 3D super-resolution that eliminates the need for high-resolution RGB guidance. Our framework encodes 3D data from a single viewpoint into a structured 2D representation, enabling the direct application of existing 2D image super-resolution architectures. We utilize the Projected Normalized Coordinate Code […]
REFLEX: Reference-Free Evaluation of Log Summarization via Large Language Model Judgment
arXiv:2511.07458v1 Announce Type: cross Abstract: Evaluating log summarization systems is challenging due to the lack of high-quality reference summaries and the limitations of existing metrics like ROUGE and BLEU, which depend on surface-level lexical overlap. We introduce REFLEX, a reference-free evaluation metric for log summarization based on large language model (LLM) judgment. REFLEX uses LLMs […]
It Takes Two: A Dual Stage Approach for Terminology-Aware Translation
arXiv:2511.07461v1 Announce Type: cross Abstract: This paper introduces DuTerm, a novel two-stage architecture for terminology-constrained machine translation. Our system combines a terminology-aware NMT model, adapted via fine-tuning on large-scale synthetic data, with a prompt-based LLM for post-editing. The LLM stage refines NMT output and enforces terminology adherence. We evaluate DuTerm on English-to German, English-to-Spanish, and […]
Dual-Kernel Graph Community Contrastive Learning
arXiv:2511.08287v1 Announce Type: cross Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful paradigm for training Graph Neural Networks (GNNs) in the absence of task-specific labels. However, its scalability on large-scale graphs is hindered by the intensive message passing mechanism of GNN and the quadratic computational complexity of contrastive loss over positive and negative […]
Motif 2 12.7B technical report
arXiv:2511.07464v1 Announce Type: cross Abstract: We introduce Motif-2-12.7B, a new open-weight foundation model that pushes the efficiency frontier of large language models by combining architectural innovation with system-level optimization. Designed for scalable language understanding and robust instruction generalization under constrained compute budgets, Motif-2-12.7B builds upon Motif-2.6B with the integration of Grouped Differential Attention (GDA), which […]
Computational Blueprints: Generating Isomorphic Mathematics Problems with Large Language Models
arXiv:2511.07932v1 Announce Type: new Abstract: Personalized mathematics education is growing rapidly, creating a strong demand for large sets of similar practice problems. Yet existing studies on mathematics problem generation have focused on data augmentation for training neural language models rather than on direct educational deployment. To bridge this gap, we define a new task, Isomorphic […]
Modulo Video Recovery via Selective Spatiotemporal Vision Transformer
arXiv:2511.07479v1 Announce Type: cross Abstract: Conventional image sensors have limited dynamic range, causing saturation in high-dynamic-range (HDR) scenes. Modulo cameras address this by folding incident irradiance into a bounded range, yet require specialized unwrapping algorithms to reconstruct the underlying signal. Unlike HDR recovery, which extends dynamic range from conventional sampling, modulo recovery restores actual values […]