Check computer science papers: citations, claims and retractions

What is a Number, That a Large Language Model May Know It?

Marjieh, R., Veselovsky, V., Griffiths, T. L., & Sucholutsky, I. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Kinetics: Rethinking Test-Time Scaling Laws

Sadhukhan, R., Chen, Z., Zheng, H., Zhou, Y., Strubell, E., & Chen, B. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

TED: Training-Free Experience Distillation for Multimodal Reasoning

Yuan, S., Wang, J., Liu, Z., Yuan, M., Peng, H., Zhao, J., Wang, B., & Wang, H. (2026) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 0

Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs

Mathew, Y., Matthews, O., McCarthy, R., Velja, J., de Witt, C. S., Cope, D., & Schoots, N. (2024) · arXiv

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

Cited by 0

"Shall We Approve?" Consequence-Aware Approval for Autonomous Coding Agents

Wu, W., Chen, J., Sun, Z., Shi, J., Lyu, Y., & Lo, D. (2026) · arXiv

Security and Verification in ComputingSoftware Engineering Research

Cited by 0

Bad Genius: Counterfactual-Guided Harness Evolution Beyond Task-Specific Shortcuts

Zhu, G., Huang, X., Yin, P., Xie, J., Zhang, S., & Zhou, D. (2026) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0

Mendel Gödel Machine: Recursive Self-Improving Coding Agents via Comparative Evolution

Liu, C., Liu, Y., Yan, S., Tresp, V., & Ma, Y. (2026) · arXiv

Evolutionary Algorithms and ApplicationsSoftware Engineering Research

Cited by 0

Hierarchical Memory Consolidation and Context-Efficient Retrieval forPersistent Personality in LLM-Based Game NPCs

Damastuti, F. A., Firmansyah, K., Barakbah, A., & Hariadi, M. (2026) · SSRN Electronic Journal

Artificial Intelligence in GamesPsychiatry, Mental Health, Neuroscience

Cited by 0

Boosting LLM Reasoning via Spontaneous Self-Correction

Zhao, X., Xu, T., Wang, X., Chen, Z., Jin, D., Tan, L., Yen-Ting, Yu, Z., Zhao, Z., He, Y., et al. (2025) · arXiv

Topic ModelingMathematics Education and Pedagogy

Cited by 0

Overview of the TREC 2023 Deep Learning Track

Craswell, N., Mitra, B., Yilmaz, E., Rahmani, H. A., Campos, D., Lin, J., Voorhees, E. M., & Soboroff, I. (2025) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 0

From Code Generation to Learning: Investigating AI-Assisted Programming in Computing Education

El Otmani, S. (2026) · Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 0

Enhancing Vision Foundation Models via Multimodal Continual Pre-Training

Chen, Y., Meng, L., Peng, W., Tao, J., Xu, C., Wu, Z., & Jiang, Y.-G. (2025) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 0

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs

Krojer, B., Komeili, M., Ross, C., Garrido, Q., Sinha, K., Ballas, N., & Assran, M. (2025) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 0

Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing

Pei, Y., Sohn, J., Habchi, S., & Papadakis, M. (2023) · arXiv

Software Testing and Debugging TechniquesMobile and Web Applications

Cited by 0

Case2Code: Scalable Synthetic Data for Code Generation

Shao, Y., Li, L., Ma, Y., Li, P., Song, D., Cheng, Q., Li, S., Li, X., Wang, P., Guo, Q., et al. (2024) · arXiv

Software Engineering ResearchTeaching and Learning Programming

Cited by 0

Achieving Tokenizer Flexibility in Language Models through Heuristic Adaptation and Supertoken Learning

Sharthak, S., Pahalwan, V., Kamath, A., & Shirawalmath, A. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

La Transcomplejidad una forma de innovación para la Educación

González Velasco, J. M. (2022) · REVISTA CON-CIENCIA

Chaos, Complexity, and EducationEducational theories and practices

Cited by 0

CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation

Zhao, J., Shin, C., Huang, T.-H., GNVV, S. S. S. N., & Sala, F. (2026) · arXiv

Topic ModelingMachine Learning and Data Classification

Cited by 0

Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web

Nie, X., Guo, Z., Cui, Z., Yang, J., Chen, Z., De, L., Zhang, Y., Liao, J., Huang, B., Yang, Y., et al. (2026) · arXiv

Multi-Agent Systems and NegotiationArtificial Intelligence Applications

Cited by 0

Automated Table Reproduction via Code Generation

Baek, D., & Pradel, M. (2026) · arXiv

Software Engineering ResearchScientific Computing and Data Management

Cited by 0

Novelty Detection in Reinforcement Learning with World Models

Zollicoffer, G., Eaton, K., Balloch, J., Kim, J., Zhou, W., Wright, R., & Riedl, M. O. (2023) · arXiv

Anomaly Detection Techniques and ApplicationsArtificial Immune Systems Applications

Cited by 0

Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models

Paech, S., Roush, A., Goldfeder, J., & Shwartz-Ziv, R. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis

Liao, C. C., Liao, D., & Gadiraju, S. S. (2025) · arXiv

Multi-Agent Systems and NegotiationSpeech and dialogue systems

Cited by 0

Foundations of GenIR

Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 0