cs papers | Gist.Science

A Governed Cognitive Enterprise Architecture Integrating Enterprise Intelligence Knowledge Memory Decision Intelligence Agentic AI and AI Assurance

This paper proposes the Governed Cognitive Enterprise Architecture (GCEA), a unified reference model that integrates strategic intelligence, knowledge memory, decision-making, agentic AI, and AI assurance to help organizations transition from fragmented AI initiatives to trusted, accountable, and continuously evolving cognitive enterprises.

Rakesh Kumar Agrawal, Wasim Mohammed Amin Tambe2026-09-23

💻 computer science

Reproducible Hybrid Ensemble Learning for Drought Forecasting

This study presents a reproducible hybrid ensemble learning framework that integrates classical machine learning and deep learning models with domain-driven feature engineering and satellite data to achieve robust, adaptive drought forecasting in Zambia, prioritizing methodological transparency and operational scalability over marginal performance gains over individual algorithms like Gradient Boosting.

Moses Chilanga, Josephat Kalezhi, Nchimunya Chaamwe2026-09-23

💻 computer science

Arabic Clinical Decision Support under a Limited GPU Budget: LoRA versus Full Fine-Tuning for Medical Question Answering

This study demonstrates that while full fine-tuning initially outperforms default low-rank adaptation (LoRA) for Arabic clinical question answering, a well-configured LoRA strategy—particularly 4-bit QLoRA—can effectively match full fine-tuning performance under limited GPU budgets, rendering it an adequate and efficient adaptation choice.

Ibrahim Abaker Hashem, Omar Elgendy, Ali Bou Nassif, Ismail Shahin, Ashraf Elnagar, Ayad Turky, Imad Afyouni2026-09-23

💻 computer science

Mechanism-oriented tendency-integration operator networks for PDE foundation models

This paper introduces mechanism-oriented operator networks that explicitly integrate physical mechanisms via learned tendency pathways, demonstrating through intervention and equivalence tests that such organization enhances PDE foundation models' generalization, accuracy, and interpretability across diverse physical systems and regimes.

Jeongsu Lee2026-09-23

💻 computer science

AAMIL-Net: Prototype-Calibrated Activity Guided Attention Fusion for Multi-Instance Learning

This paper introduces AAMIL-Net, a novel Multiple Instance Learning framework that resolves attention-confidence misalignment through prototype-calibrated activity scoring, adaptive ambiguity margins, and contrastive instance regularization, achieving state-of-the-art performance across diverse benchmarks without external pretraining.

Chen Rui, Jie Li, Fang Fang2026-09-23

💻 computer science

An Optimized Stacking Ensemble Model for Forecasting Student Dropout

This study presents an optimized stacking ensemble model integrating K-Nearest Neighbors and Bayesian Network classifiers, enhanced by hyperparameter tuning and SMOTE-based balancing, which achieves 85.9% accuracy and outperforms existing methods to provide an effective early-warning tool for forecasting student dropout.

Jackson Kisuule, Anthony Bua2026-09-23

💻 computer science

Transition-Aware Short-Term Traffic Congestion Prediction: Efficient Gradient Boosting versus Spatio-Temporal Graph Neural Networks

This paper challenges the reliance on standard deep spatio-temporal graph neural networks and aggregate accuracy metrics for short-term traffic congestion prediction by introducing a transition-aware evaluation framework (TRSP) and demonstrating that efficient, interpretable feature-engineered gradient boosting models can match or surpass deep learning baselines in detecting state transitions while significantly reducing computational costs.

George S. Theodoropoulos, Yannis Theodoridis2026-09-23

💻 computer science

YOLO-RCD: A Lightweight Pavement Damage Detector Validated by Controlled Multi-Seed Reproduction and Measured Edge Deployment

This paper introduces YOLO-RCD, a lightweight pavement damage detector that achieves superior accuracy and energy efficiency on edge hardware compared to state-of-the-art baselines, validated through a rigorous multi-seed controlled protocol and enhanced by test-time augmentation to mitigate zero-shot transfer costs.

Aihemaitijiang Tuerhong, Shuo Wang, Aximu Yuemaier, Xiaopeng Gu, Jie Liu, Naman Maimaiti2026-09-23

💻 computer science

Do Option-Elimination Logs Improve Student Models? A Preregistered Negative Result on EdNet-KT4

This preregistered study on the EdNet-KT4 dataset concludes that adding option-elimination logs to student models yields no statistically significant improvement in predicting correctness or ranking lecture consumption, thereby arguing against their inclusion in the evaluated models.

Brahim ES-SABERY, Abdelkader Moumane, Mohamed Ouhda, Mohamed Baslam2026-09-23

💻 computer science

An Inter-variable Relationship-Aware Mixture-of-Experts Model for Stock Closing-Price Prediction

The paper proposes IR-MoE, an Inter-variable Relationship-Aware Mixture-of-Experts Transformer that explicitly models dynamic dependencies among trading variables and employs sparse routing with a stock-wise shuffled training strategy to achieve superior forecasting accuracy and zero-shot generalization across diverse global stock markets.

Zhenjiang Chen, Bin Liu, Pei-Gen Ye, Yang Lv, Jun Zheng2026-09-23

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