Most cited papers
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Conservation properties of a leapfrog finite-difference time-domain method for the Schrödinger equation
Bekmambetova, F., & Triverio, P. (2023) · arXiv
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Towards Understanding Grokking: An Effective Theory of Representation Learning
Liu, Z., Kitouni, O., Nolte, N., Michaud, E. J., Tegmark, M., & Williams, M. (2022) · arXiv
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Optically controlling the emission chirality of microlasers
Zambon, N. C., St-Jean, P., Milićević, M., Lemaître, A., Harouri, A., LeGratiet, L., Bleu, O., Solnyshkov, D., Malpuech, G., Sagnes, I., et al. (2018) · arXiv
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The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., et al. (2023) · arXiv
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What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?
Gao, S., Wen, X.-C., Gao, C., Wang, W., Zhang, H., & Lyu, M. R. (2023) · arXiv
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Four-dimensional topological lattices through connectivity
Price, H. M. (2018) · arXiv
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Knowledge Conflicts for LLMs: A Survey
Xu, R., Qi, Z., Guo, Z., Wang, C., Wang, H., Zhang, Y., & Xu, W. (2024) · arXiv
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A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
Laskar, M. T. R., Bari, M. S., Rahman, M., Bhuiyan, M. A. H., Joty, S., & Huang, J. X. (2023) · arXiv
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From Code to Courtroom: LLMs as the New Software Judges
He, J., Shi, J., Zhuo, T. Y., Treude, C., Sun, J., Xing, Z., Du, X., & Lo, D. (2025) · arXiv
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CocoaBench: Evaluating Unified Digital Agents in the Wild
CocoaBench Team, Hao, S., Zhang, Z., Liang, Z., Liu, T., Zha, Y., Gao, Q., Chen, J., Wang, Z., Cheng, Z., et al. (2026) · arXiv
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LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models
Lin, Y.-T., & Chen, Y.-N. (2023) · arXiv
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Neural Episodic Control
Pritzel, A., Uria, B., Srinivasan, S., Puigdomènech, A., Vinyals, O., Hassabis, D., Wierstra, D., & Blundell, C. (2017) · arXiv
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Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases
Neelam, S., Sharma, U., Bhatia, S., Karanam, H., Likhyani, A., Abdelaziz, I., Fokoue, A., & Subramaniam, L. V. (2022) · arXiv
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Bubble fluctuations in $Ω<1$ inflation
Garriga, J. (1996) · arXiv
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Amending published articles: time to rethink retractions and corrections?
Barbour, V., Bloom, T., Lin, J., & Moylan, E. (2017)
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A simple gate for linear optics quantum computing
Rudolph, T., & Pan, J.-W. (2001) · arXiv
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Three dimensional loop quantum gravity: physical scalar product and spin foam models
Noui, K., & Perez, A. (2004) · arXiv
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Applied and Filtered: An End-to-End Algorithmic Fairness Audit of A Public Employment Agency
Galdón-Clavell, G. (2026) · arXiv
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Demonstration of Non-Deterministic Quantum Logic Operations using Linear Optical Elements
Pittman, T. B., Jacobs, B. C., & Franson, J. D. (2001) · arXiv
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Euclid Definition Study Report
Laureijs, R., Amiaux, J., Arduini, S., Auguères, J. L., Brinchmann, J., Cole, R., Cropper, M., Dabin, C., Duvet, L., Ealet, A., et al. (2011) · arXiv
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Measurement Techniques for Thermal Conductivity and Interfacial Thermal Conductance of Bulk and Thin Film Materials
Zhao, D., Qian, X., Gu, X., Jajja, S. A., & Yang, R. (2016) · arXiv
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LLM-Powered Virtual Patient Agents for Interactive Clinical Skills Training with Automated Feedback
Voigt, H., Sugamiya, Y., Lawonn, K., Zarrieß, S., & Takanishi, A. (2025) · arXiv
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An important Bottleneck in LLM Optimizers: Principle-Level Local Optima
Ran, N., Jia, T., Mu, Z., Wang, Y., Brulin, S., Olhofer, M., Alvarez, M. A., Liu, Y., & Allmendinger, R. (2026) · arXiv
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You can remove GPT2's LayerNorm by fine-tuning
Heimersheim, S. (2024) · arXiv
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