cs papers | Gist.Science

Contrast-Induced Class Overlap as a Fairness Bottleneck in Dermatological AI: Evidence from HAM10000

This study identifies that lower lesion-background contrast on darker skin creates a structural class overlap causing AI dermatological models to systematically over-predict malignancy and generate excess referrals for darker-skinned patients, a fairness bottleneck that persists even after correcting for confounding class distributions and is best mitigated by per-tone class balancing rather than tone conditioning.

Aaron Ajit2026-06-25

💻 computer science

Predictive Failure Detection in Data Warehouse Architectures Through Machine Learning

This research proposes a machine learning framework that leverages ensemble methods and explainable AI to predict data warehouse failures up to 15 minutes in advance by analyzing system metrics, thereby reducing downtime through proactive anomaly detection and automated root cause analysis.

Hendrik Robert2026-06-25

💻 computer science

How Class Imbalance Treatments Distort SHAP Attribution Fidelity: A Monte Carlo Investigation of Ranking and Magnitude Errors

Through a comprehensive Monte Carlo investigation, this paper demonstrates that while training sample size is the primary driver of SHAP attribution fidelity, common class imbalance treatments often distort feature rankings and magnitudes, leading the authors to recommend avoiding rebalancing in favor of class weighting when SHAP interpretability is a priority.

Rıdvan Kara2026-06-25

💻 computer science

CLHA: Cross-Layer Hessian Aggregation for Quantizing Weight-Shared Mixture-of-Experts Transformers

This paper introduces CLHA, a cross-layer Hessian aggregation method for post-training quantization of weight-shared Mixture-of-Experts transformers that minimizes total reconstruction error by combining Hessian statistics across shared layers, significantly outperforming existing per-layer calibration approaches while also addressing critical implementation pitfalls in Hessian estimation.

Kunal Dhanda2026-06-25

💻 computer science

AAL: In-Band Source Annotations for Just-In-Time Context Injection in Autonomous Agentic Workflows

This paper introduces the Agentic Annotation Language (AAL), a novel in-band annotation system that enables Just-In-Time context injection for autonomous agents, significantly reducing token consumption and context pollution compared to traditional global rule files while maintaining high compliance with domain-specific constraints.

Jorge O Varona2026-06-25

💻 computer science

Cross-Dataset Generalization in Urdu Fake News Detection: An Empirical Study with XLM-RoBERTa and a Length Confound Analysis

This paper presents the first cross-dataset generalization study for Urdu fake news detection, revealing that a model trained on the Ax-to-Grind dataset fails catastrophically when applied to the Notri-Fact dataset due to a systematic length confound in the training data that induces shortcut learning, thereby highlighting critical flaws in current dataset construction and evaluation practices for low-resource NLP.

Muhammad Abdullah Haroon2026-06-25

💻 computer science

Cascade Progressive Distilled Networks for Heterogeneous Tabular Data Classification

The paper introduces the Cascade Progressive Distilled Network (CPDN), a novel deep learning framework that leverages knowledge distillation from a CatBoost teacher to overcome optimization stagnation and non-differentiability issues, achieving state-of-the-art classification performance on heterogeneous tabular data.

Alexander Viktorov2026-06-25

💻 computer science

Human-Centric Cooperative MARL for Reliable EV Charging Coordination in Smart Cities: A Controlled Multi-Seed Comparison of Centralised and Decentralised Training

This paper demonstrates that a centralized training with decentralized execution (CTDE) approach using QMIX significantly outperforms independent Q-learning (IQL) and non-RL baselines in coordinating electric vehicle charging within smart cities, achieving higher acceptance rates and success probabilities while effectively managing partial observability and traffic dynamics.

Nour-Eddine Moumni2026-06-25

💻 computer science

Selective State Space Models in Medical Imaging: A Systematic Review of Mamba-Based Architectures and Clinical Applications

This systematic review of 445 recent studies (2024–2026) identifies CNN-Mamba hybrids as the most efficient architecture for medical imaging due to their linear computational complexity, while highlighting critical challenges like spatial information loss and memory consumption that must be addressed through native 3D blocks to enable clinical integration.

Ali Emre Gök, Mustafa Yurdakul, Şakir Taşdemir2026-06-25

💻 computer science

Hybrid Intrusion Detection System for IoT and Cloud Environments

This paper proposes and evaluates a hybrid intrusion detection framework that combines supervised learning (Random Forest and XGBoost) with an LSTM Autoencoder-based anomaly detection model on aligned IoT and cloud datasets, demonstrating that while this approach significantly improves detection robustness and recall for unseen attacks, it necessitates a trade-off with increased false positives and reduced precision.

Dipo Dunsin, Raju Molla, Alireza Esfahani2026-06-25

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