Check computer science papers: citations, claims and retractions

CodeShell Technical Report

Xie, R., Zeng, Z., Yu, Z., Gao, C., Zhang, S., & Ye, W. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 3

Quantum Computing Approach to Nonrelativistic and Relativistic Molecular Energy Calculations

Veis, L., & Pittner, J. (2014) · Advances in Chemical Physics

Quantum Computing Algorithms and ArchitectureAdvanced Chemical Physics Studies

Cited by 3

A Comprehensive Survey on Long Context Language Modeling

Liu, J., Zhu, D., Bai, Z., He, Y., Liao, H., Que, H., Wang, Z., Zhang, C., Zhang, G., Zhang, J., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

Episodic Memories Generation and Evaluation Benchmark for Large Language Models

Huet, A., Houidi, Z. B., & Rossi, D. (2025) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 3

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

Luo, J., Tian, Y., Cao, C., Luo, Z., Lin, H., Li, K., Kong, C., Yang, R., & Ma, J. (2026) · Findings of the Association for Computational Linguistics: ACL 2026

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 3

Understanding Retrieval Augmentation for Long-Form Question Answering

Chen, H.-T., Xu, F., Arora, S., & Choi, E. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 3

Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions

Bhattamishra, S., Patel, A., Blunsom, P., & Kanade, V. (2023) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3

Learning with 3D rotations, a hitchhiker's guide to SO(3)

Geist, A. R., Frey, J., Zhobro, M., Levina, A., & Martius, G. (2024) · arXiv

Neural Networks and Applications3D Shape Modeling and Analysis

Cited by 3

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Kim, H., Hessel, J., Jiang, L., West, P., Lu, X., Yu, Y., Zhou, P., Bras, R. L., Alikhani, M., Kim, G., et al. (2022) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 3

SlimPajama-DC: Understanding Data Combinations for LLM Training

Shen, Z., Tao, T., Ma, L., Neiswanger, W., Liu, Z., Wang, H., Tan, B., Hestness, J., Vassilieva, N., Soboleva, D., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

Smarter AI Agents: Optimizing Tokens the Right Way

Kommuru, M. (2025) · International Journal of Machine Learning and Predictive Analytics

Topic ModelingArtificial Intelligence Applications

Cited by 3

Survey of the security risks of Wi-Fi networks based on the information elements of beacon and probe response frames

Reyes-Moncayo, H. I., Malaver- Mendoza, L. D., & Ochoa-Murillo, A. L. (2020) · Scientia et technica

Wireless Networks and ProtocolsNetwork Security and Intrusion Detection

Cited by 3

Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge

Ahmad, H., & Ghafar, N. A. (2026) · AI and Ethics

Ethics and Social Impacts of AIMedia, Religion, Digital Communication

Cited by 3

Generating Game Levels by Defining Player Experiences

Fernandes, P. M., Lopes, M., & Prada, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Artificial Intelligence in GamesDigital Games and Media

Cited by 3

Multi-Agent Large Language Models for Conversational Task-Solving

Becker, J. (2024) · arXiv

Speech and dialogue systemsMulti-Agent Systems and Negotiation

Cited by 3

First class dynamic effect handlers: or, polymorphic heaps with dynamic effect handlers

Leijen, D. (2018) · Proceedings of the 3rd ACM SIGPLAN International Workshop on Type-Driven Development

Logic, programming, and type systemsTeaching and Learning Programming

Cited by 3

Introduction to the Special Issue on Big Data, Machine Learning, and AI Technologies for Art and Design

She, J. P. M. (2019) · ACM Transactions on Multimedia Computing, Communications, and Applications

Digital Media and Visual ArtArt, Technology, and Culture

Cited by 3

CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion

Yao, J., Li, H., Liu, Y., Ray, S., Cheng, Y., Zhang, Q., Du, K., Lu, S., & Jiang, J. (2026) · ACM Transactions on Computer Systems

Topic ModelingNatural Language Processing Techniques

Cited by 3

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

Green, T., Gubri, M., Puerto, H., Yun, S., & Oh, S. J. (2025) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 3

Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text

Cheng, Y., Sadasivan, V. S., Saberi, M., Saha, S., & Feizi, S. (2025) · Advances in Neural Information Processing Systems 38

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 3

A Potential Teammate?: Understanding How Indie Game Developers Approach Generative AI’s Involvement in Their Small-Scale Creative Teamwork

Panchanadikar, R., & Freeman, G. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

Software Engineering Techniques and PracticesDigital Games and Media

Cited by 3

Exploring the Potential of Anomaly Detection Through Reasoning with Large Language Models

Park, S., & Choi, D. (2025) · Applied Sciences

Spam and Phishing DetectionTopic Modeling

Cited by 3

Robust LLM safeguarding via refusal feature adversarial training

Yu, L., Do, V., Hambardzumyan, K., & Cancedda, N. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 3

Training language models to be warm and empathetic makes them less reliable and more sycophantic

Ibrahim, L., Hafner, F. S., & Rocher, L. (2025) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 3