Check computer science papers: citations, claims and retractions

ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality

Cosarinsky, M., Billot, R., Mansilla, L., Jimenez, G., Gaggión, N., Fu, G., Tirer, T., & Ferrante, E. (2025) · arXiv

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 0

SLR: Automated Synthesis for Scalable Logical Reasoning

Helff, L., Omar, A., Friedrich, F., Wüst, A., Shindo, H., Mitchell, R., Woydt, T., Schramowski, P., Stammer, W., & Kersting, K. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Logic, Reasoning, and KnowledgeNatural Language Processing Techniques

Cited by 0

I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders

Galichin, A., Dontsov, A., Druzhinina, P., Razzhigaev, A., Rogov, O. Y., Tutubalina, E., & Oseledets, I. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 0

Base Models Beat Aligned Models at Randomness and Creativity

West, P., & Potts, C. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Learning Program Behavioral Models from Synthesized Input-Output Pairs

Mammadov, T., Klakow, D., Koller, A., & Zeller, A. (2024) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 0

VLSP 2025 Shared Task: Vietnamese Temporal Question Answering (TemporalQA)

Duc, P. T., Linh, H. M., Toan, L. N., & Huyen, N. T. M. (2026) · VNU Journal of Science: Computer Science and Communication Engineering

Topic ModelingNatural Language Processing Techniques

Cited by 0

Let’s read the log: root cause analysis of railway test execution logs with large language models

Hermawan, R., Bucaioni, A., Enoiu, E., Afzal, W., Saadatmand, M., Zaimovic, N., & Ibtasham, M. S. (2026) · Innovations in Systems and Software Engineering

Software Testing and Debugging TechniquesSoftware System Performance and Reliability

Cited by 0

The Price of Anarchy in Disaggregated Inference

Georgiou, A. (2026) · arXiv

Cloud Computing and Resource ManagementParallel Computing and Optimization Techniques

Cited by 0

Putting It All into Context: Simplifying Agents with LCLMs

Jiang, M., Ruan, Y., Lastras, L., Kapanipathi, P., & Hashimoto, T. (2025) · arXiv

Software Engineering ResearchAI-based Problem Solving and Planning

Cited by 0

Do Methods Support the Claims? Intra-Paper Verification for Peer Review

Ballakuraya, R. S., Mahyari, A., & Srinivasan, A. (2026) · arXiv

Topic ModelingAcademic Writing and Publishing

Cited by 0

The Next Educational Revolution: Grand Challenges for Learning @ Scale in the Generative AI Era

Piech, C. (2025) · Proceedings of the Twelfth ACM Conference on Learning @ Scale

Artificial Intelligence in EducationOnline Learning and Analytics

Cited by 0

Memory by Design: Bayesian Layers for Sequence Modeling

Dowling, M., Jeon, H., Savin, C., & Park, I. M. (2026) · arXiv

Topic ModelingNeural Networks and Applications

Cited by 0

MRMMIA: Membership Inference Attacks on Memory in Chat Agents

Chen, K., Pang, Y., & Wang, T. (2026) · arXiv

Spam and Phishing DetectionAdversarial Robustness in Machine Learning

Cited by 0

Anatomically-aware conformal prediction for medical image segmentation with random walks

Gaillochet, M., Desrosiers, C., & Lombaert, H. (2026) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 0

Training-free Regional Prompting for Diffusion Transformers

Chen, A., Xu, J., Zheng, W., Dai, G., Wang, Y., Zhang, R., Wang, H., & Zhang, S. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 0

Reward Is Enough: LLMs Are In-Context Reinforcement Learners

Song, K., Moeini, A., Wang, P., Gong, L., Chandra, R., Zhang, S., & Qi, Y. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Watch and Learn: Learning to Use Computers from Online Videos

Song, C. H., Song, Y., Goyal, P., Su, Y., Riva, O., Palangi, H., & Pfister, T. (2025) · arXiv

AI-based Problem Solving and PlanningInformation Retrieval and Search Behavior

Cited by 0

MCP-ITP: An Automated Framework for Implicit Tool Poisoning in MCP

Li, R., Wang, Z., Yao, Y., & Li, X.-Y. (2026) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 0

KV Cache Translation across Heterogeneous Large Language Models

Lee, J.-W., Song, M., Oh, J., Han, S., Jang, G., Park, S., & Lim, S. (2026) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Training-Free Group Relative Policy Optimization

Cai, Y., Cai, S., Shi, Y., Xu, Z., Chen, L., Qin, Y., Tan, X., Li, G., Li, Z., Lin, H., et al. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 0

The GUI-to-CUI Shift in Mobile Medication Management for Blind and Low-Vision Users: Effects on Usability, Workload, and Information-Seeking Behaviour

Hong, S., Sohn, M., Choi, J., Jung, J., Cho, H., & Park, W. (2026) · SSRN (preprint)

Usability and User Interface DesignDigital Accessibility for Disabilities

Cited by 0

Overconfidence without Understanding: AI Explanations Increase the Illusion of Explanatory Depth

Aslanov, I., Felmer, P., & Guerra, E. (2025)

Explainable Artificial Intelligence (XAI)Decision-Making and Behavioral Economics

Cited by 0

ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay

Lu, F., Zhong, Z., Liu, S., Fu, C.-W., & Jia, J. (2025) · arXiv

Reinforcement Learning in RoboticsSpeech and dialogue systems

Cited by 0

RACAS: Controlling Diverse Robots With a Single Agentic System

Ashley, D. R., Przepióra, J., Chen, Y., Abualsaud, A., Yesmagambet, N., Park, S., Feron, E., & Schmidhuber, J. (2026) · arXiv

AI-based Problem Solving and PlanningRobotics and Automated Systems

Cited by 0