cs papers | Gist.Science

Finite Evidence, Infinite Horizons: The Artificial Age Score as a Case Study in Longitudinal AI Measurement

This paper argues that finite longitudinal AI evaluation records, exemplified by the Artificial Age Score (AAS), cannot determine infinite-horizon system properties or convergence without explicit stipulated continuation models, as demonstrated through counterexamples and stress tests showing that bounded observations alone yield only conditional, model-based inferences rather than definitive conclusions.

Seyma Yaman Kayadibi2026-09-03

💻 computer science

MeowID: A Dual-Expert Retrieval System for Individual Cat Identification

This paper introduces MeowID, a dual-expert retrieval system that combines facial and whole-cat recognition with a face-priority framework to achieve robust individual cat identification in unconstrained environments, accompanied by the release of the new Individual Cats in the Wild (ICW) dataset.

Zhangchi Hu, Yi Shang, Haocheng Yang, Qiwei Hu, Yuzheng Li2026-09-03

💻 computer science

Similarity Finds the Fact, Not the Version: A Co-Trained Order Stamp in the Key Resolves Version Conflict in Persistent Model-Internal Memory

This paper demonstrates that adding a co-trained "order stamp" to persistent model-internal memory keys resolves version conflicts by enabling precise retrieval of specific facts based on their temporal sequence, rather than relying on capacity or recency shortcuts.

Maximiliano Rodrigo Speranza2026-09-03

💻 computer science

Modeling Loading Anomalies and Identifying Root Causes in Media Consumption Workflows on Large Social Media Platforms

This paper presents a dynamic service-dependency graph approach for modeling loading anomalies and identifying root causes in large social media platforms, achieving high accuracy in predicting delays and significantly reducing localization time compared to static methods.

Yuewei Yuan, Tianyi Xu, Jiayang Yin, Jialei Huang2026-09-03

💻 computer science

Weakly Supervised Anomaly Detection and Privacy Risk Scoring in Community Message Threads

This paper introduces RiskThread-LF, a weakly supervised and privacy-preserving framework that detects anomalous community message threads by fusing diverse behavioral signals and correcting for reporting bias, achieving superior performance over content-based and graph-based baselines while maintaining robustness under differential privacy.

Yuewei Yuan, Jiayang Yin, Jialei Huang, Tianyi Xu2026-09-03

💻 computer science

Adaptive Edge Intelligence for Low-Latency Assistive Vision under Dynamic Network and Computational Constraints

This paper proposes and evaluates a safety-aware adaptive edge intelligence controller for assistive vision systems that dynamically balances processing modes based on real-time network, computational, and risk conditions to minimize latency and ensure timely feedback, while outlining a framework for future experimental validation.

Md Shahanur Islam Shagor2026-09-03

💻 computer science

Risk-Aware Adaptive Video Processing with Freshness Control for Low-Latency Assistive Vision

This paper proposes a risk-aware adaptive video-processing controller for low-latency assistive vision that dynamically separates frame admission from inference-mode selection based on network, compute, and navigation risk metrics, thereby significantly reducing end-to-end latency and shedding stale non-critical frames while ensuring critical-risk observations receive full-fidelity processing.

Md Shahanur Islam Shagor2026-09-03

💻 computer science

FrameShift-CAD: Executable Coordinate-Frame Interventions for Diagnosing Text-to-CAD Generation

The paper introduces FrameShift-CAD, a diagnostic benchmark that reveals a significant reliability gap in text-to-CAD models, showing they struggle to correctly execute pure rotational coordinate-frame transformations compared to translations, even when explicit mapping instructions are provided.

Shengyao Sun2026-09-03

💻 computer science

MEDPOLY: A Proposed Approach for Cardiovascular Disease Prediction Using Various Ensemble Models

This paper proposes and evaluates two ensemble models for cardiovascular disease prediction, finding that a simpler combination of three classifiers (DNL) outperforms a more complex 24-classifier framework (MEDPOLY) by achieving 98% accuracy while avoiding overfitting and offering better interpretability.

Sreekumari Sreelesh, Rajni Bhalla, Gursharan Singh2026-09-03

💻 computer science

A Global–Local Feature Coupled Invertible Neural Network with Multi-Channel Attention Pooling for Image Steganography

This paper proposes CINN, a convolution-based invertible neural network that integrates global-local feature coupling and multi-channel attention pooling to simultaneously enhance the security, imperceptibility, and recovery fidelity of image steganography, outperforming existing state-of-the-art methods across multiple datasets and steganalysis evaluations.

Yan Zhao, Yang Lingkang2026-09-02

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