Check computer science papers: citations, claims and retractions

Beyond Fixed Tasks: Unsupervised Environment Design for Task-Level Pairs

Furelos-Blanco, D., Pert, C., Kelbel, F., Spies, A. F., Russo, A., & Dennis, M. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 0

On Epistemic Diversity in Large Language Models

Kirsten, E., Krämer, N., & Zafar, M. B. (2026) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0

Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation

Yang, C., Zhao, X., Wu, T., & Kästner, C. (2026) · arXiv

Artificial Intelligence ApplicationsNatural Language Processing Techniques

Cited by 0

rdracor: Access to the 'DraCor' API

Pozdniakov, I. (2023) · CRAN: Contributed Packages

Data Analysis with RDigital Humanities and Scholarship

Cited by 0

ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization

Wang, Y., Yang, L., Li, G., Wang, M., & Aragam, B. (2025) · arXiv

Natural Language Processing TechniquesMulti-Agent Systems and Negotiation

Cited by 0

Do acolhimento ao pódio: um relato de experiência sobre o uso de programação competitiva como estratégia para empoderar mulheres na área de computação

Silva, L. C. A. D., Batista, E. M., Silva, T. R. D. M. B., & Braga e Silva, G. (2026) · Anais do XX Women in Information Technology (WIT 2026)

Teaching and Learning ProgrammingGender and Technology in Education

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MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection

Lin, B., Zeng, Z., Xiao, Z., Kou, S., Hou, T., Gao, X., Zhang, H., & Deng, Z. (2024) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 0

Separations in the Representational Capabilities of Transformers and Recurrent Architectures

Bhattamishra, S., Hahn, M., Blunsom, P., & Kanade, V. (2024) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 0

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

Bilal, A., Mohsin, M. A., Umer, M., Bangash, M. A. K., & Jamshed, M. A. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models

Lin, Z., Xie, Y., Qu, B., Wang, H., Li, J., Wu, H., Dong, Y., Yang, Z., Zhu, J., Lu, H., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 0

ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases

Zhong, Z., Raghunathan, A., & Carlini, N. (2025) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 0

Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases

Wong, S., Qi, Z., Wang, Z., Hu, N., Lin, S., Ge, J., Gao, E., Chen, W., Du, Y., Yu, M., et al. (2025) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 0

Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints

Bowyer, S., Aitchison, L., & Ivanova, D. R. (2025) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 0

CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation

Pugachev, S. (2025) · arXiv

Distributed systems and fault toleranceParallel Computing and Optimization Techniques

Cited by 0

$\texttt{DiffLense}$: A Conditional Diffusion Model for Super-Resolution of Gravitational Lensing Data

Reddy, P., Toomey, M. W., Parul, H., & Gleyzer, S. (2024) · arXiv

Advanced Image Processing TechniquesAstrophysical Phenomena and Observations

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Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training

Lin, J. (2026) · arXiv

Reinforcement Learning in RoboticsSoftware Engineering Research

Cited by 0

Scaling Data-Constrained Language Models with Synthetic Data

Kiyomaru, H., Oda, Y., Kodama, T., Liu, C., & Kawahara, D. (2026) · Findings of the Association for Computational Linguistics: EACL 2026

Topic ModelingNatural Language Processing Techniques

Cited by 0

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Wu, Z., Arora, A., Geiger, A., Wang, Z., Huang, J., Jurafsky, D., Manning, C. D., & Potts, C. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 0

SwiftCache: Efficient LLM Serving for Multi-turn Conversations with Heterogeneous KV Cache Sharing

Hu, J., Xu, M., Wang, S., Ma, C., Shen, M., Ye, K., Qu, L., & Xu, C. (2026) · arXiv

Parallel Computing and Optimization TechniquesNatural Language Processing Techniques

Cited by 0

Efficient LLM Safety Evaluation through Multi-Agent Debate

Lin, D., Shen, G., Yang, Z., Liu, T., Zhao, D., & Zeng, Y. (2025) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

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The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization

Slendebroek, Z., & Metaxa, D. (2026) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Music Technology and Sound StudiesEthics and Social Impacts of AI

Cited by 0

Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter

Qin, R., He, W., Wang, Y., Li, Z., Xu, X., Wu, Y., Zheng, W., & Zhang, M. (2026) · arXiv

Cloud Computing and Resource ManagementParallel Computing and Optimization Techniques

Cited by 0

Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures

Rombaut, B. (2026) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 0

MMA: Multimodal Memory Agent

Lu, Y., Cheng, W., Zhang, Z., & Tang, H. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 0