Most cited papers
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Sampling in Software Engineering Research: A Critical Review and Guidelines
Baltes, S., & Ralph, P. (2020) · arXiv
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Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination
Lucchinetti, C., Br�ck, W., Parisi, J., Scheithauer, B., Rodriguez, M., & Lassmann, H. (2000) · Annals of Neurology
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MS-YOLO: Infrared Object Detection for Edge Deployment via MobileNetV4 and SlideLoss
Zhang, J., White, T. S., Zhang, H., Hu, W., Wunsch, D. C., & Liu, J. (2025) · arXiv
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Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
Hoang, A., Bosselut, A., Celikyilmaz, A., & Choi, Y. (2019) · arXiv
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Unlearnable examples for diffusion models: protect data from unauthorized exploitation
Zhao, Z., Duan, J., Hu, X., Xu, K., Wang, C., Zhang, R., Du, Z., Guo, Q., & Chen, Y. (2023) · arXiv
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Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem
Wei, Q., Holt, S., Yang, J., Wulfmeier, M., & van der Schaar, M. (2025) · arXiv
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Sentiment Analysis in the Era of Large Language Models: A Reality Check
Zhang, W., Deng, Y., Liu, B., Pan, S. J., & Bing, L. (2023) · arXiv
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Should ChatGPT and bard share revenue with their data providers? A new business model for the AI era
Zhang, D. (2023) · arXiv
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A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
Newman, B., Soldaini, L., Fok, R., Cohan, A., & Lo, K. (2023) · arXiv
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ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning
Lopez-Miguel, I. D., Happe, A., Cito, J., Bartocci, E., Könighofer, B., & Tappler, M. (2026) · arXiv
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AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation
Wang, Z., Ma, D., Zhong, H., Li, J., Zhang, W., Wang, B., & He, C. (2026) · arXiv
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H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark
LeGris, S., Vong, W. K., Lake, B. M., & Gureckis, T. M. (2024) · arXiv
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SinGAN: learning a generative model from a single natural image
Shaham, T. R., Dekel, T., & Michaeli, T. (2019) · arXiv
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Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv
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Evaluating Language Models for Mathematics through Interactions
Collins, K. M., Jiang, A. Q., Frieder, S., Wong, L., Zilka, M., Bhatt, U., Lukasiewicz, T., Wu, Y., Tenenbaum, J. B., Hart, W., et al. (2023) · arXiv
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An Exact Apéry Limit for the AESZ 182 Calabi–Yau Recurrence
Shvets, A. (2026) · Zenodo
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RuleFlow : Generating Reusable Program Optimizations with LLMs
Singh, A., Bharadwaj, D., Baziotis, S., Varadharajan, K., & Mendis, C. (2026) · arXiv
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Seeing through randomness with topological light
Peters, C., Everts, K., Kleine, T., Ornelas, P., & Forbes, A. (2025) · arXiv
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An In-Context Learning Agent for Formal Theorem-Proving
Thakur, A., Tsoukalas, G., Wen, Y., Xin, J., & Chaudhuri, S. (2023) · arXiv
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Dataset of first appearances of the scholarly bibliographic references on English Wikipedia articles as of 1 March 2017 and as of 1 October 2021
Kikkawa, J., Takaku, M., & Yoshikane, F. (2021) · Zenodo
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M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
Zhang, W., Aljunied, S. M., Gao, C., Chia, Y. K., & Bing, L. (2023) · arXiv
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Copyright protection in generative AI: a technical perspective
Ren, J., Xu, H., He, P., Cui, Y., Zeng, S., Zhang, J., Wen, H., Ding, J., Huang, P., Lyu, L., et al. (2024) · arXiv
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Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
Cao, Z., Deng, J., Yu, L., Zhou, W., Liu, Z., Ding, B., & Zhao, H. (2025) · arXiv
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Non-Hermitian Topological Theory of Finite-Lifetime Quasiparticles: Prediction of Bulk Fermi Arc Due to Exceptional Point
Kozii, V., & Fu, L. (2017) · arXiv
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