Check computer science papers: citations, claims and retractions

Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension Theories

Gonçalves, P. W., Rani, P., Storey, M.-A., Spinellis, D., & Bacchelli, A. (2025) · 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC)

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 4

Context Is King: Large Language Models’ Interpretability in Divergent Knowledge Scenarios

Piñeiro-Martín, A., Santos-Criado, F.-J., García-Mateo, C., Docío-Fernández, L., & López-Pérez, M. D. C. (2025) · Applied Sciences

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 4

Enhancing Software Engineering with AI: Key Insights from ChatGPT

Al-Ahmad, A., Kahtan, H., Tahat, L., & Tahat, T. (2024) · 2024 International Conference on Decision Aid Sciences and Applications (DASA)

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 4

AI-Driven Confidential Computing across Edge-to-Cloud Continuum

Zobaed, S. (2023) · arXiv

Security and Verification in ComputingIoT and Edge/Fog Computing

Cited by 4

Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs

Cheng, K., Yang, J., Jiang, H., Wang, Z., Huang, B., Li, R., Li, S., Li, Z., Gao, Y., Li, X., et al. (2024) · arXiv

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 4

Mitigating Transmission Errors: A Forward Error Correction-Based Framework for Enhancing Objective Video Quality

Imtiaz, M. B., & Kamran, R. (2025) · Sensors

Video Coding and Compression TechnologiesImage and Video Quality Assessment

Cited by 4

Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Feng, X., Wan, Z., Wen, M., McAleer, S. M., Wen, Y., Zhang, W., & Wang, J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning

Wang, X., Zhang, H., Zhao, S., Zou, Y., Chen, H., Ding, Z., Cheng, B., & Lan, Y. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Speech and dialogue systemsTopic Modeling

Cited by 4

KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems

Ye, H., Gao, Z., Ma, M., Wang, Q., Fu, Y., Chung, M.-Y., Lin, Y., Liu, Z., Zhang, J., Zhuo, D., et al. (2025) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 4

Reducing Cost of LLM Agents with Trajectory Reduction

Xiao, Y.-A., Gao, P., Peng, C., & Xiong, Y. (2025) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 4

Mapping the Design Space of Human-AI Interaction in Text Summarization

Cheng, R., Smith-Renner, A., Zhang, K., Tetreault, J. R., & Jaimes, A. (2022) · arXiv

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 4

Do stochastic parrots have feelings too? Improving neural detection of synthetic text via emotion recognition

Cowap, A., Graham, Y., & Foster, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Authorship Attribution and ProfilingDigital Media Forensic Detection

Cited by 4

Human action recognition using wearable sensors and neural networks

Karungaru, S. (2015) · 2015 10th Asian Control Conference (ASCC)

Context-Aware Activity Recognition SystemsHuman Pose and Action Recognition

Cited by 4

Settling the Reward Hypothesis

Bowling, M., Martin, J. D., Abel, D., & Dabney, W. (2022) · arXiv

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 4

Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap

Nageshwaran, V., & Ezekiel, S. (2026) · Electronics

Network Security and Intrusion DetectionIoT and Edge/Fog Computing

Cited by 4

Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data

Abdellatif, A. A., Mhaisen, N., Mohamed, A., Erbad, A., Guizani, M., Dawy, Z., & Nasreddine, W. (2022) · Future Generation Computer Systems

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 4

Problem-Sensitive Response Generation in Human-Robot Dialogs

Gieselmann, P., & Ostendorf, M. (2007) · Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

Speech and dialogue systemsSocial Robot Interaction and HRI

Cited by 4

Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning

Christianos, F., Papoudakis, G., & Albrecht, S. V. (2022) · arXiv

Reinforcement Learning in RoboticsGame Theory and Applications

Cited by 4

CBDC Governance: Programmability, Privacy and Policies

Freiman, O. (2025) · SSRN Electronic Journal

Blockchain Technology Applications and SecurityDigitalization, Law, and Regulation

Cited by 4

WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild

Lin, B. Y., Deng, Y., Chandu, K., Brahman, F., Ravichander, A., Pyatkin, V., Dziri, N., Bras, R. L., & Choi, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

A zero-trust digital twin framework for privacy-preserving multi-dataset intrusion detection in industrial IoT with lightweight blockchain auditing

Mishra, S., Aldafas, T. S. M., & Alshammari, N. S. (2026) · Scientific Reports

Network Security and Intrusion DetectionInternet of Things and AI

Cited by 4

Quantification of Large Language Model Distillation

Lee, S., Zhou, J., Ao, C., Li, K., Du, X., He, S., Wu, H., Liu, T., Liu, J., Alinejad-Rokny, H., et al. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 4

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Yue, X., Zheng, T., Ni, Y., Wang, Y., Zhang, K., Tong, S., Sun, Y., Yu, B., Zhang, G., Sun, H., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 4

Generative Medical Event Models Improve with Scale

Waxler, S., Blazek, P., White, D., Sneider, D., Chung, K., Nagarathnam, M., Williams, P., Voeller, H., Wong, K., Swanhorst, M., et al. (2025) · arXiv

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 4