838 papers
This paper presents the first production-scale study of AI-assisted course design by mining a privacy-preserving dataset from CourseFactory to extract four workflow surfaces, demonstrating that structural-quality triage and item-to-assignment routing models significantly improve review efficiency and accuracy while confirming that withheld prompt text adds no measurable signal.
This study demonstrates that while successful and failed trajectories in a disrupted world model exhibit distinct recovery dynamics—such as lower path entropy and fewer structural excursions—these full-trajectory patterns cannot be reliably predicted from early prefixes, thereby distinguishing the challenges of regime detection, recovery monitoring, and individual rollout prediction.
This paper introduces H. Dilpriya's Momentum (HDM), a novel multi-strategy optimizer that combines adaptive per-parameter corrections with cosine-annealed scheduling to achieve rigorous convergence guarantees and state-of-the-art gradient alignment, demonstrating superior performance on both ill-conditioned synthetic problems and deep learning benchmarks like MNIST and CIFAR-10.
This paper proposes a multi-tenant edge-cloud hybrid LLM serving architecture that combines W4A16-quantized speculative decoding with asynchronous communication and a multi-tenant verification orchestrator to simultaneously address privacy, latency, and resource utilization challenges.
This paper introduces JPAL-HA, a safe learning model for safety-critical environments that leverages human action justifications to generate hypothetical queries, thereby significantly improving safety and sample efficiency while reducing human training burden.
This study demonstrates that using explainable machine learning to analyze item-level safety climate data reveals nonlinear, threshold-dependent behavioural risk patterns driven by management responsiveness, training, and experience, challenging traditional linear aggregation models and offering a more precise basis for proactive accident prevention.
This paper proposes a sparse ℓ0-regularized quadratic surface support vector machine (QSVM) to address overfitting and interpretability issues in kernel-free nonlinear classification, introducing a penalty decomposition algorithm with provable optimality and convergence guarantees that demonstrates competitive performance and sparsity on both benchmark and real-world credit datasets.
This paper presents a PRISMA-based systematic review revealing the limitations of current auditory-centric solutions for deaf learners and proposes an AI-driven multimodal communication framework integrating speech recognition, natural language processing, and sign language generation to enable real-time visual support and achieve meaningful inclusion in classrooms.
This paper reveals that standard scaffold-based evaluation methods introduce a hidden allocation bias by incorrectly treating chemically heterogeneous "no-scaffold" molecules as a single unit, which distorts generalization metrics across common benchmarks, and proposes an "Empty-Scaffold-Aware" repair to restore balanced representation and accurate performance assessment.
This paper introduces C3Q-MARL, a connectivity-aware framework that integrates graph-theoretic topology control with a QAOA-inspired optimization module into multi-agent reinforcement learning to enhance UAV swarm coordination, collision avoidance, and connectivity preservation under partial observability and communication constraints.