Most cited papers
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Guidelines for Empirical Studies in Software Engineering involving Large Language Models
Baltes, S., Angermeir, F., Arora, C., Barón, M. M., Chen, C., Böhme, L., Calefato, F., Ernst, N., Falessi, D., Fitzgerald, B., et al. (2025) · arXiv
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Telomeric 8-Oxoguanine Drives Rapid Premature Senescence In The Absence Of Telomere Shortening
Barnes, R. P., de Rosa, M., Thosar, S. A., Detwiler, A. C., Roginskaya, V., Van Houten, B., Bruchez, M. P., Stewart-Ornstein, J., & Opresko, P. L. (2021)
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Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Theile, M., Dirnberger, L., Trumpp, R., Caccamo, M., & Sangiovanni-Vincentelli, A. L. (2024) · arXiv
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Middle Archean continent formation by crustal delamination
Zegers, T. E., & van Keken, P. E. (2001) · Geology
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More on counterterms in the gravitational action and anomalies
Taylor-Robinson, M. (2000) · arXiv
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Learning to Generate Long-term Future via Hierarchical Prediction
Villegas, R., Yang, J., Zou, Y., Sohn, S., Lin, X., & Lee, H. (2017) · arXiv
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Blind Dexterity: Whole-Body Humanoid Manipulation via Pure Proprioception
Bhatt, A., Kaidanov, O., Liu, P., & Peters, J. (2026) · arXiv
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When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors
Emmons, S., Jenner, E., Elson, D. K., Saurous, R. A., Rajamanoharan, S., Chen, H., Shafkat, I., & Shah, R. (2025) · arXiv
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Universal Adversarial Triggers for Attacking and Analyzing NLP
Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · arXiv
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Search-Time Data Contamination
Han, Z., Mankikar, M., Michael, J., & Wang, Z. (2025) · arXiv
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Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Reimers, N., & Gurevych, I. (2017) · arXiv
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Constructing Fast Network through Deconstruction of Convolution
Jeon, Y., & Kim, J. (2018) · arXiv
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Exploring the relationship between psychological contracts and organizational change: A process model and case study evidence
Pate, J., Martin, G., & Staines, H. (2000) · Strategic Change
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Loss Minimization Yields Multicalibration for Large Neural Networks
Błasiok, J., Gopalan, P., Hu, L., Kalai, A. T., & Nakkiran, P. (2023) · arXiv
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Whose Facts Win? LLM Source Preferences under Knowledge Conflicts
Schuster, J., Gautam, V., & Markert, K. (2026) · arXiv
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv
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MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
Zhu, K., Du, H., Hong, Z., Yang, X., Guo, S., Wang, Z., Wang, Z., Qian, C., Tang, X., Ji, H., et al. (2025) · arXiv
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Technically Love: The Evolution of Human-AI Romance Discourse on Reddit
Chang, T., Huh-Yoo, J., & Razi, A. (2026) · arXiv
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CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?
Wei, T., Luan, J., Liu, W., Dong, S., & Wang, B. (2023) · arXiv
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Constraints on Low-Dimensional String Compactifications
Sethi, S., Vafa, C., & Witten, E. (1996) · arXiv
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Literacy as a Leisure Activity
Nippold, M. A., Duthie, J. K., & Larsen, J. (2005) · Language, Speech, and Hearing Services in Schools
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Characteristics of the interannual and decadal variability in a general circulation model of the tropical Atlantic Ocean
Huang, B., & Shukla, J. (1997) · Journal of Physical Oceanography
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Fourier spectrum discrepancies in deep network generated images
Dzanic, T., Shah, K., & Witherden, F. (2019) · arXiv
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Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination
Dao, X.-Q., & Le, N.-B. (2023) · arXiv
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