Most cited papers
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Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence
Bara, M. (2026) · arXiv
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Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks
Kirk, J. R., Wray, R. E., Lindes, P., & Laird, J. E. (2022) · arXiv
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AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Wallach, I., Dzamba, M., & Heifets, A. (2015) · arXiv
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Fault-tolerant quantum computation by anyons
Kitaev, A. Y. (1997) · arXiv
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CodeUpdateArena: Benchmarking Knowledge Editing on API Updates
Liu, Z. L., Pandit, S., Ye, X., Choi, E., & Durrett, G. (2024) · arXiv
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Cross-country dataset C19 ISWS
Buffel, V., & Van De Velde, S. (2022) · Zenodo
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LAraBench: Benchmarking Arabic AI with Large Language Models
Abdelali, A., Mubarak, H., Chowdhury, S. A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y. E., Izham, D., Dalvi, F., Hawasly, M., et al. (2023) · arXiv
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Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms
Azad, A. T., Ahmed, F., Apan, I. S., Jubaer, E., Katha, S. K., Alam, A., Ali, A. A., Chadha, A., Islam, M. M., & Rahman, A. M. (2026) · arXiv
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Generative AI in Game Development: A Qualitative Research Synthesis
Ternar, A., Denisova, A., Cunha, J. M., Kultima, A., & Guckelsberger, C. (2025) · arXiv
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Charles Dickens and the Critique of Victorian Society: Literature as a Catalyst for Social Reform
Padra, D. B. (2024) · The Criterion
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A survey on measuring indirect discrimination in machine learning
Zliobaite, I. (2015) · arXiv
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Time travel for knowledge graphs: live queries over RDF change histories
Massari, A., & Peroni, S. (2022) · arXiv
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The ICF
Eadie, T. L. (2003) · American Journal of Speech-Language Pathology
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Fairness in Serving Large Language Models
Sheng, Y., Cao, S., Li, D., Zhu, B., Li, Z., Zhuo, D., Gonzalez, J. E., & Stoica, I. (2024) · arXiv
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Towards a Framework for Assessing the Impact of Cadastral Development on Land Rights-Holders
Hull, S., & Whittal, J. (2016) · figshare
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Ai2 Scholar QA: Organized Literature Synthesis with Attribution
Singh, A., Chang, J. C., Anastasiades, C., Haddad, D., Naik, A., Tanaka, A., Zamarron, A., Nguyen, C., Hwang, J. D., Dunkleberger, J., et al. (2025) · arXiv
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WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation
Ding, S., Dai, X., Xing, L., Ding, S., Liu, Z., JingYi, Y., Yang, P., Zhang, Z., Wei, X., Fang, X., et al. (2026) · arXiv
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Sampling of Structure and Sequence Space of Small Protein Folds
Linsky, T., Noble, K., Tobin, A., Crow, R., Carter, L., Urbauer, J., Baker, D., & Strauch, E. (2021)
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LabQAR (Lab oratory Q uestion A nswering with R eference Ranges) Data Set
bhasuran, B., & He, Z. (2026) · figshare
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Assessment of Cd, Cr and Pb Pollution in Sediment and Water of Gheshlagh River, Iran, in September 2013
Majnoni, F., Alipour, H., Hassanpour, M., Banagar, G., & Ajorlo, M. (2015) · Unpublished
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Retrieval-Augmented Generation with Conflicting Evidence
Wang, H., Prasad, A., Stengel-Eskin, E., & Bansal, M. (2025) · arXiv
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Replication data for: What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
Brynjolfsson, E., Mitchell, T., & Rock, D. (2018) · ICPSR - Interuniversity Consortium for Political and Social Research
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Fredholm Operators and Spectral Flow
Waterstraat, N. (2016) · arXiv
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Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Yuan, H., Chan, S., Creagh, A. P., Tong, C., Acquah, A., Clifton, D. A., & Doherty, A. (2022) · arXiv
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