Most cited papers
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Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
Lopez-Lira, A., & Tang, Y. (2023)
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Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Barak, B., Edelman, B. L., Goel, S., Kakade, S., Malach, E., & Zhang, C. (2022) · arXiv
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Few-Shot In-Context Imitation Learning via Implicit Graph Alignment
Vosylius, V., & Johns, E. (2023) · arXiv
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Conformity Breaks Conformal Prediction
Hu, Y., & Su, H. (2026) · arXiv
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The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
Li, H., Zhang, H., & Hassan, A. E. (2025) · arXiv
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Output Length Effect on DeepSeek-R1's Safety in Forced Thinking
Li, X., Li, Z., Kosuga, Y., & Bian, V. (2025) · arXiv
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Narratives of Return : The Contemporary Caribbean Woman Writer and the Quest for Home.
Thompson, R. G. (2015) · Goldsmiths, University of London
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E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes
Sakamoto, K., Miyanishi, T., Azuma, D., Kurita, S., Morikuni, S., Chiba, N., Kawanabe, M., Iwasawa, Y., & Matsuo, Y. (2026) · arXiv
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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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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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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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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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