cs papers | Gist.Science

Goal-Aware Adaptive Regulation Across Modalities: Evolutionary Architecture Search for Transformer Language Models on a Consumer AMD GPU

This paper demonstrates that the GaaR evolutionary architecture search framework, originally validated on convolutional networks, successfully transfers to autoregressive transformer language models on a consumer AMD GPU via DirectML, achieving competitive performance and revealing that regulated exploitation outperforms random search on structured landscapes while favoring wide-shallow architectures under fixed step budgets.

Leon Sandler2026-07-17

💻 computer science

A Distributed CatBoost Approach with Weighted Aggregation for STI/HIV Risk Prediction

This paper proposes a privacy-preserving, distributed CatBoost framework with weighted aggregation that achieves high accuracy in predicting STI and HIV risks across eight countries without requiring centralized data, thereby offering a robust alternative to traditional centralized machine learning models.

Zhaohui Tang, Yan Li, Abdulkadir Sengur, U. Rajendra Acharya2026-07-17

💻 computer science

NAE-VC: Neural Arithmetic Encoding as a Learned Replacement for CABAC in Modern Video Codecs

NAE-VC is a modular, learned entropy coding framework that replaces the standard CABAC engine in H.264, H.265, and H.266 codecs with a neural architecture combining context aggregation, Gaussian mixture modeling, and hyperpriors to achieve significant bitrate savings while maintaining full compatibility with existing video coding standards.

Reka Sandaruwan Gallena Watthage, Anil Fernando2026-07-17

💻 computer science

MUNNA: A Multi-strategy, Uncertainty-driven, Nested Neuro-ensemble Algorithm for Disease Diagnosis with Closed-loop Explainability Feedback

This paper introduces MUNNA, a novel three-stage hybrid framework that integrates a GWO-SA metaheuristic for feature selection, an uncertainty-weighted stacking ensemble, and a closed-loop SHAP feedback mechanism to achieve state-of-the-art disease diagnosis performance on the Breast Cancer Wisconsin dataset while addressing limitations in local optima stagnation and static ensemble weighting.

Md. Maruf Sharker Munna2026-07-17

💻 computer science

DAÏO: A High-Specificity Real-TimeVision-Based Fall Detection SystemUsing Advanced Kinematic Tracking andComputer Vision

The paper presents DAÏO, a novel edge-capable vision-based fall detection system that combines a lightweight pose estimator, an adaptive Extended Kalman Filter, and a Triple-Check Logic Engine to achieve high specificity (96.4%) and significantly reduce false positives from daily activities and religious prostrations while maintaining real-time performance on consumer hardware.

Alamin Abubakar Nataala2026-07-17

💻 computer science

STAC-MV: Spatio-Temporal Adaptive Context for Neural Multiview Video Compression

STAC-MV is the first transformer-based neural multiview video compression framework that leverages geometry-aware adaptive context selection and cross-view attention to achieve significant BD-rate improvements over conventional standards across diverse camera configurations while substantially reducing encoding time.

Reka Sandaruwan Gallena Watthage, Anil Fernando2026-07-17

💻 computer science

Multi-Stage Alignment of Large Language Models for Popularity Bias Mitigation in Generative Movie Recommendation

This paper proposes a multi-stage alignment pipeline combining preference extraction, supervised fine-tuning, and Direct Preference Optimization to effectively mitigate popularity bias in LLM-based movie recommenders, achieving improved novelty and catalog coverage while maintaining competitive accuracy.

Subham Raj, Krishnakant Chourey, Sriparna Saha, Brijraj Singh, Niranjan Pedanekar2026-07-17

💻 computer science

Towards Generalizable Face Forgery Detection via Multi-Granularity Fusion

To address the generalization gap in face forgery detection caused by the conflict between transferable high-level semantics and sparse local artifacts, this paper proposes SemFusion, a framework that combines Semantic Consistency Regularization to preserve CLIP's transferable structure with Multi-level Patch Evidence learning to effectively capture and fuse localized forgery cues.

Mingjie Zhao, Yiu-ming Cheung, Guilin Pang2026-07-17

💻 computer science

An Agent-Based Concept Generation ApproachUsing Concept Bottleneck Models for ChestRadiograph Classification

This study demonstrates that clinically grounded concept construction enhances concept bottleneck models for chest radiograph classification, with supervised approaches achieving strong performance comparable to non-bottleneck baselines while label-free models offer a promising alternative when concept annotations are unavailable.

Mehmet Varan, Fatih Soygazi, Damla Oguz2026-07-17

💻 computer science

BioGraph-SentiCOVID: Calibrated Adaptive Context Selection for Graph Neural Sentiment Analysis of COVID-19 Tweets

This paper introduces BioGraph-SentiCOVID, a calibrated adaptive-context graph neural framework that optimizes conversational context selection and hyperparameters via a genetic algorithm to achieve state-of-the-art sentiment classification of noisy COVID-19 tweets on Twitter.

Moustafa MAASKRI, Mohamed GOISMI, Djamal SEGHIER, Mohamed DEBBAB2026-07-17

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