Check computer science papers: citations, claims and retractions

Active Inference for Robotic Manipulation

Schneider, T., Belousov, B., Abdulsamad, H., & Peters, J. (2022) · arXiv

Reinforcement Learning in RoboticsEmbodied and Extended Cognition

Cited by 3

Quantization Robustness to Input Degradations for Object Detection

Karimov, T., Imani, H., & Kazakov, A. (2025) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 3

Executing Shell Scripts in the Wrong Order, Correctly

Liargkovas, G., Kallas, K., Greenberg, M., & Vasilakis, N. (2023) · Proceedings of the 19th Workshop on Hot Topics in Operating Systems

Parallel Computing and Optimization TechniquesLogic, programming, and type systems

Cited by 3

Adaptive Program Management Strategies for AI-Based Cyber Defense Deployments in Critical Infrastructure and Enterprise Digital Transformation Initiatives

Adepoju, A. S. (2025) · International Journal of Research Publication and Reviews

Information and Cyber SecurityInformation Technology Governance and Strategy

Cited by 3

Towards an Ontology for Robot Introspection and Metacognition

Nolte, R., Pomarlan, M., Bessler, D., Porzel, R., Malaka, R., & Bateman, J. A. (2023) · Frontiers in Artificial Intelligence and Applications

Psychiatry, Mental Health, NeuroscienceAI-based Problem Solving and Planning

Cited by 3

Improving healthcare communication: AI-driven emotion classification in imbalanced patient text data with explainable models

Hamza-Cherif, S., Tani, L. F. K., & Settouti, N. (2024) · Advances in Technology Innovation

Sentiment Analysis and Opinion MiningMental Health via Writing

Cited by 3

Personalized Non-Player Characters: A Framework for Character-Consistent Dialogue Generation

Liu, X., Xie, Z., & Jiang, S. (2025) · AI

Artificial Intelligence in GamesSpeech and dialogue systems

Cited by 3

LLMs as Meta-Reviewers’ Assistants: A Case Study

Hossain, E., Sinha, S. K., Bansal, N., Knipper, R. A., Sarkar, S., Salvador, J., Mahajan, Y., Guttikonda, S. R. P. K., Akter, M., Hassan, M. M., et al. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 3

“Artificial Intelligence”, or Statistics?

Lowe, D. (2019) · Significance

Artificial Intelligence ApplicationsStatistical Methods and Applications

Cited by 3

Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions

Tan, C., Lyu, D., Li, S., Gao, Z., Wei, J., Ma, S., Liu, Z., & Li, S. Z. (2024) · arXiv

Topic ModelingAcademic Writing and Publishing

Cited by 3

Escaping the Panopticon Over Time: Balancing the Right To Be Forgotten and Freedom of Expression in a Technological Architecture

Gorzeman, L., & Korenhof, P. (2017) · Philosophy & Technology

Ethics and Social Impacts of AIPrivacy, Security, and Data Protection

Cited by 3

Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts

Haimes, J., Wenner, C., Thaman, K., Tashev, V., Neo, C., Kran, E., & Schreiber, J. (2024) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 3

Increasing depth, distribution distillation, and model soup: erasing backdoor triggers for deep neural networks

Zhang, Y., Zhang, T., Liu, Q., Sun, G., & Wu, H. (2022) · Journal of Electronic Imaging

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 2

Video models are zero-shot learners and reasoners

Wiedemer, T., Li, Y., Vicol, P., Gu, S. S., Matarese, N., Swersky, K., Kim, B., Jaini, P., & Geirhos, R. (2025) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 2

An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis

Williams, A. (2026) · Cogent Education

Online Learning and AnalyticsEthics and Social Impacts of AI

Cited by 2

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation

Miculicich, L., Parmar, M., Palangi, H., Dvijotham, K. D., Montanari, M., Pfister, T., & Le, L. T. (2025) · arXiv

Formal Methods in VerificationSafety Systems Engineering in Autonomy

Cited by 2

Power-seeking can be probable and predictive for trained agents

Krakovna, V., & Kramar, J. (2023) · arXiv

Ethics and Social Impacts of AIReinforcement Learning in Robotics

Cited by 2

Low-Resource Multilingual and Zero-Shot Multispeaker TTS

Lux, F., Koch, J., & Vu, N. T. (2022) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 2

Next-Day Medical Activities Recommendation Model with Double Attention Mechanism Using Generative Adversarial Network

Li, W., Yang, J., & Min, X. (2022) · Journal of Healthcare Engineering

Machine Learning in HealthcareRecommender Systems and Techniques

Cited by 2

HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference

Zhang, Z., Shen, H., Vargaftik, S., Basat, R. B., Mitzenmacher, M., & Yu, M. (2025) · Proceedings of the ACM SIGCOMM 2025 Conference

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 2

Random Subspace Mixture Models for Interpretable Anomaly Detection

Savkli, C., & Schwartz, C. (2021) · arXiv

Anomaly Detection Techniques and ApplicationsFault Detection and Control Systems

Cited by 2

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

Lyu, Y., Zhang, X., Zhao, Y., Papakostas, K., Yi, X., Guo, S., Hu, W., Piotrowski, J., Kaliski, J., Urbani, J., et al. (2026) · arXiv

Artificial Intelligence ApplicationsEvolutionary Algorithms and Applications

Cited by 2

Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing

Williams, S., Indelman, V., Kaess, M., Roberts, R., Leonard, J. J., & Dellaert, F. (2014) · The International Journal of Robotics Research

Robotics and Sensor-Based LocalizationTarget Tracking and Data Fusion in Sensor Networks

Cited by 2

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments

Dedieu, A., Lehrach, W., Zhou, G., George, D., & Lázaro-Gredilla, M. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 2