4998 papers
This paper proposes a discrete mathematical framework using coarse-grained partitions and categorical unification to quantify information loss in explainable AI, demonstrating that zero loss is an exceptional case and providing a method to optimize the trade-off between interpretability and informational fidelity.
This paper introduces Texts as Time Series (TaTS), a novel framework that leverages the periodic alignment between paired texts and time series data to enhance multimodal forecasting and imputation performance in existing numerical-only models without requiring architectural changes.
The paper proposes LaVCa, a novel data-driven approach that leverages large language models to generate natural-language captions for images, thereby providing more accurate and detailed interpretations of human visual cortex voxel selectivity and revealing fine-grained functional differentiation within the visual cortex compared to existing deep neural network-based methods.
This paper proposes a Clustering-On-Difficulty (COD) framework that groups tasks by their difficulty scaling features to predict downstream LLM performance with high accuracy (1.55% error), effectively addressing challenges like emergent capabilities and inconsistent scaling patterns.
This study proposes a high-accuracy glioma subclass classification framework that fuses 2D and 3D UNET-segmented multimodal MRI images using weighted averaging and classifies them via a pre-trained ResNet50 model, achieving a 99.25% accuracy rate.
This paper proposes a deep learning-based approach utilizing UNET, Inception, and ResNet architectures to achieve automatic 2D and 3D glioma segmentation on BraTS datasets, demonstrating that a ResNet model effectively balances computational efficiency and spatial accuracy to significantly improve diagnosis with high accuracy and Dice scores.
This paper proposes Leave-One-Out PPO (LOOP), a novel reinforcement learning method that combines variance reduction techniques from REINFORCE with the robustness of PPO to achieve a superior balance between sample efficiency and performance in fine-tuning text-to-image diffusion models.
This paper proposes a novel dual-thread framework that enables concurrent planning and acting with interruptible execution for LLM-based multi-agent systems, overcoming the limitations of serialized paradigms to enhance real-time responsiveness and adaptability in dynamic environments like Minecraft.
This paper introduces GlucoLens, an explainable machine learning system that integrates wearable sensor data, diet, and activity logs to accurately predict postprandial hyperglycemia and recommend personalized behavioral interventions for managing blood glucose levels.
This paper proposes a novel transformer-based geometric deep learning model that tokenizes tetrahedral meshes with anatomical landmarks to accurately classify Alzheimer's disease and predict brain amyloid positivity in medium-risk individuals, offering a robust alternative to costly and invasive PET scans.
This study of 36 Kazakhstani scholars using the CGScholar platform reveals that while familiarity with AI tools correlates with openness to feedback, participants still highly value peer input for methodological guidance, suggesting that integrating AI with traditional peer feedback effectively enhances academic research writing skills.
ViLAM is a novel method that distills vision-language reasoning from large Vision-Language Models into spatial attention maps to guide socially compliant robot navigation, achieving significant improvements in success rates through real-world validation.
The paper proposes IMPACT, a novel motion planning framework that leverages Vision-Language Models to infer environment semantics and generate anisotropic cost maps, enabling a contact-aware A* planner to safely navigate cluttered environments by distinguishing between acceptable and dangerous object contacts.
The paper introduces iProg, an interactive tool that leverages a structured communication protocol between humans and large language models to decompose scientific data analysis tasks into declarative Data Flow Diagrams and generate corresponding code, thereby achieving significantly faster development, higher code quality, and better performance than traditional Low Code/No Code alternatives.
This paper reveals that while Large Language Models overrepresent female characters due to fine-tuning, they paradoxically still reinforce traditional occupational gender stereotypes more than real-world labor data, highlighting the need for nuanced bias mitigation strategies.
This paper proposes a unified framework for occlusion-robust two-hand reconstruction that combines a fusion-alignment encoder to implicitly integrate heterogeneous 2D structural priors from vision foundation models with a penetration-free diffusion model that guides 3D pose generation toward collision-free, kinematically coherent interactions.
The paper introduces EDU-PRM, an entropy-driven process reward model that automatically identifies reasoning step boundaries using predictive entropy to eliminate manual annotations, achieving state-of-the-art performance with only 1.5% of the training data while significantly improving accuracy and reducing token usage.
This paper introduces MediTools, an AI-powered prototype application that leverages large language models to revolutionize medical education through interactive dermatology case simulations, enhanced literature analysis, and automated medical news summaries, while validating its potential through a survey of medical professionals and students.
This paper proposes MetaBoost, a novel hybrid framework combining SMOTE, ADASYN, and CTGAN to optimize data balancing for enhanced Metabolic Syndrome prediction, while utilizing counterfactual analysis to identify blood glucose and triglycerides as the most critical modifiable risk factors.
This paper presents a comprehensive review that consolidates fragmented evaluation efforts into a unified taxonomy of approximately 60 benchmarks, surveys AI-agent frameworks and collaboration protocols, and explores real-world applications and future research directions for autonomous AI agents.