4478 papers
This study utilizes PLS-SEM analysis of data from Ghanaian universities to demonstrate that subjective norms, deterrence, and involvement mechanisms—particularly knowledge sharing—significantly shape contingent employees' attitudes toward information security policies, thereby driving their compliance intentions.
This paper introduces a miniature brain transformer architecture that demonstrates a novel, falsifiable prediction: functional lateralization of hippocampal banks requires the synergistic interaction of a prefrontal working-memory buffer (acting as a symmetry-breaker) and inhibitory callosal coupling, a mechanism that triggers a sharp phase transition in memory performance while a cerebellar fast-path merely accelerates convergence.
VINO is a self-supervised learning framework that overcomes the "co-occurrence trap" in dense video by using a teacher-student distillation approach with structural priors to force representations to focus on foreground objects rather than background context, achieving state-of-the-art unsupervised object discovery performance.
This paper proposes a hybrid Learning-to-Rank recommender system that leverages deep learning and social context embeddings from Stack Overflow to effectively recommend software bug solutions to developers, achieving nearly 78% accuracy in the top 10 results.
This paper introduces BATDiff, an unsupervised single-image super-resolution model that leverages bivariate A-trous wavelet transforms and cross-scale parent-child dependencies to generate sharper, more structurally consistent high-frequency details while minimizing artifacts and dataset-driven hallucinations.
This paper addresses the unexplored challenge of avoiding redundancy in data lakes by defining the Novel Table Search (NTS) problem, proposing an efficient penalization-based approximation algorithm called ANTs, and demonstrating through extensive experiments that it outperforms existing methods in capturing syntactic novelty while maintaining low execution time.
The paper proposes HY-WU, a memory-first adaptation framework that replaces static weight overwriting with a functional neural memory module to synthesize instance-specific weight updates on-the-fly, thereby enabling robust continual learning and personalization without degrading previously learned behaviors.
This paper proposes a soft actor-critic-based reinforcement learning framework for vehicle-to-grid voltage regulation that effectively coordinates single and multi-hub charging systems while prioritizing battery health and fleet availability, demonstrating robust performance comparable to standard droop controllers under both nominal and aggressive overloading conditions.
FabricGen is an end-to-end framework that generates realistic woven fabric materials from text by decoupling macro-scale texture synthesis via fine-tuned diffusion models from micro-scale weaving pattern generation using a specialized LLM-driven procedural geometric model.
This paper introduces PresentBench, a fine-grained, rubric-based benchmark comprising 238 instances and detailed binary checklists that enables more reliable, human-aligned evaluation of automated slide generation, revealing that NotebookLM currently outperforms other methods in this domain.
This paper introduces LEPA, a learned architecture that conditions on geometric augmentations to accurately predict transformed satellite image embeddings, effectively overcoming the limitations of standard interpolation in non-convex geospatial foundation model manifolds and significantly improving geometric adjustment performance.
This paper addresses the challenge of agents with heterogeneous goals deciding when to cooperate or act alone by introducing a hierarchical learning framework that combines imitation and reinforcement learning, demonstrating superior performance over baselines and revealing that modeling teammates is most beneficial when their goals are less observable.
This paper introduces VASR, a multimodal reasoning framework for Context-Aware Visual Speech Recognition (CAVSR) that leverages an Audio-Visual Chain-of-Thought (AV-CoT) to explicitly ground acoustic signals with rich visual context like scenes and on-screen text, thereby overcoming single-modality dominance and achieving state-of-the-art performance.
This paper proposes a kinematics-aware latent world model that integrates vehicle kinematic information and geometry-aware supervision into the Recurrent State-Space Model (RSSM) to enhance spatial representation and long-horizon imagination fidelity, thereby achieving more data-efficient and stable autonomous driving policy learning compared to existing baselines.
This paper provides a comprehensive review of network-aware modeling, optimization, and control methods for Integrated Energy Systems, highlighting the critical role of network constraints in addressing operational challenges and outlining future research directions to achieve scalable, efficient, and low-carbon energy operations.
This paper introduces "trace inversion" models that can reconstruct detailed reasoning traces from only a target model's inputs, answers, and summaries, demonstrating that hiding reasoning chains fails to prevent the theft of reasoning capabilities and enabling significant performance gains for student models fine-tuned on these synthetic traces.
This paper introduces sketch-oriented databases, a categorical framework that unifies various graph-based paradigms and features through finite-limit sketches, while proposing localizers for lazy path inference and stuttering sketches to enable modular composition and scalable model growth.
AutoDataset is a lightweight, automated system that continuously monitors arXiv to detect, extract, and index newly released datasets from research papers, enabling real-time discovery and significantly improving search efficiency by up to 80%.
VisualDeltas is a lightweight, label-free preference-learning framework that leverages systematic visual quality perturbations to generate informative supervision signals, thereby improving multimodal model performance and generalization without relying on human annotations.
This paper establishes a worst-case to average-case reduction for a non-modular Short Integer Solution (SIS) problem over the integers, demonstrating that solving random instances of this problem efficiently allows for the polynomial-time approximation of the Shortest Independent Vectors Problem (SIVP) within a factor of .