cs papers | Gist.Science

Divergent research orientations of affective and emotion concepts in artificial intelligence education through a bibliometric analysis

Through a bibliometric analysis of 401 documents from 2020 to 2025, this study reveals that while AI-driven affective and emotion research has matured into a coherent ecosystem with a significant growth inflection in 2023, the two concepts exhibit divergent orientations—where "affective" research focuses on educational practice and "emotion" research on technological development—necessitating a proposed Dual Knowledge Production Model to guide cross-disciplinary collaboration.

Hsiang Ni Tsai, Sheng Yi Wu2026-06-26

💻 computer science

Robust Graph Neural Networks via Community-Guided Label Refinement and Progressive Contrastive Learning

This paper proposes CG-GNN, a robust graph neural network framework that mitigates label noise by integrating community-guided label refinement with edge pruning and a progressive contrastive learning scheme to outperform state-of-the-art methods in noisy and data-scarce scenarios.

Zhigang Sun, Haoran Xu, Jinke Xu, Lie Wang2026-06-26

💻 computer science

Accountability Risks of SHAP Based Managerial Recommendations in Explainable Artificial Intelligence

This study demonstrates that converting SHAP-based feature importance scores into direct managerial actions without accounting for intervention costs, causal validity, and downside risks creates significant accountability hazards, revealing that explainability alone is insufficient for responsible AI-driven decision-making.

Oussama Chahed2026-06-26

💻 computer science

A Survey on Poisoning Attacks, Defenses, andProvable Defense in the Era of LLMs

This survey systematically reviews poisoning attacks and defenses in the era of Large Language Models by proposing a comprehensive taxonomy that categorizes threats into weight and context poisoning, analyzing representative techniques and defense strategies, and outlining future research directions for securing compound AI systems.

Yuni Lai, Xinqi Lyu, Dong Wang, Yihao Liu, Kai Zhou, Bin Xiao2026-06-26

💻 computer science

FedDAAW: Dynamic Client Selection and Accuracy- Adaptive Aggregation for Cross-Task Federated Learning under Heterogeneity

FedDAAW is a lightweight, privacy-preserving framework for heterogeneous federated learning that employs dynamic client selection and accuracy-adaptive aggregation to significantly improve convergence efficiency and performance across diverse tasks (classification, sentiment analysis, and regression) under non-IID settings without requiring gradient sharing or auxiliary data.

Jianqing Tang2026-06-26

💻 computer science

Bibliometric Analysis of Global Research Trends in Robotic Sports: Mapping Two Decades of Innovation (2000–2024)

This bibliometric analysis of 1,225 publications from 2000 to 2024 reveals the evolution of robotic sports research into a mature, globally collaborative field driven by key institutions and a thematic shift from foundational robot soccer to advanced AI applications like deep learning and human–robot interaction.

DEBDAS MONDAL2026-06-25

💻 computer science

CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning

This paper introduces the Composite Tasks Challenge (CTC), a new benchmark suite designed to rigorously evaluate division of labor and cooperation in multi-agent reinforcement learning, revealing that current state-of-the-art methods fail to solve these tasks while demonstrating that a solvable yet challenging testbed is essential for advancing the field.

Yurui Li, Yuxuan Chen, Xiaoli Yang, Shijian Li, Gang Pan2026-06-25

💻 computer science

SousCoder: Cross-Application Confusion Detection for Proactive Developer Assistance

SousCoder is a local-first, proactive developer assistant that monitors cross-application behavioral signals across VS Code, Chrome, and the terminal to detect developer confusion and intervene with context-aware suggestions, achieving low latency and high relevance in field evaluations while extending prior work with persistent memory and multi-source perception.

Vrund Thakkar2026-06-25

💻 computer science

Validating Large Language Model Extraction of Actuarial Variables from Unstructured Claims Documents

This study evaluates a large language model pipeline for extracting 14 actuarial variables from workers' compensation claims, revealing moderate overall agreement with human reviewers (quadratic weighted kappa of 0.53) and significant variability across dimensions, which underscores the need for calibration and phased deployment before formal validation.

Robert Lieberthal, Vietbao Phan, Jawand Singh, Elizabeth Sottung, Richard Tran2026-06-25

💻 computer science

Comparative Analysis of Machine Learning Models for Predicting Grid Stability Using Tabular and Transformer Architectures

This study demonstrates that a transformer-based deep learning model outperforms classical machine learning algorithms in predicting electrical grid stability, achieving high accuracy in both binary classification and regression tasks while supporting global sustainability goals for resilient smart grids.

ARUNKUMAR E, Lakshmi Priya M2026-06-25

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