Most cited papers
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Designing Self-Care: Affect and Debility in #SELFCARE
Stone, K. (2020) · transcript
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One-shot Learning for Temporal Knowledge Graphs
Mirtaheri, M., Rostami, M., Ren, X., Morstatter, F., & Galstyan, A. (2020) · arXiv
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Jailbreaking ChatGPT via prompt engineering: an empirical study
Liu, Y., Deng, G., Xu, Z., Li, Y., Zheng, Y., Zhang, Y., Zhao, L., Zhang, T., Wang, K., & Liu, Y. (2023) · arXiv
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Using Search Queries to Understand Health Information Needs in Africa
Abebe, R., Hill, S., Vaughan, J. W., Small, P. M., & Schwartz, H. A. (2018) · arXiv
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Meta$^n$: Recursive Self-Improvement through Emergent Depth
Kim, Z. M., Lee, Y.-J., Jwa, S., & Kang, D. (2026) · arXiv
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Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017) · arXiv
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General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
Ilharco, G., Jain, V., Ku, A., Ie, E., & Baldridge, J. (2019) · arXiv
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Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South
Mehta, A., Chauhan, S., & Choudhury, M. (2024) · arXiv
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Neutralizing Topological Obstructions to Bubbles of Nothing
Draper, P., Lillard, B., & Skye, C. (2023) · arXiv
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Misstrauen und Vertrauen
Vaske, C. (2016) · Universität Vechta
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Dirac, Majorana and Weyl fermions
Pal, P. B. (2010) · arXiv
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D-SMART: Enhancing LLM Dialogue Consistency via Dynamic Structured Memory And Reasoning Tree
Lei, X., Li, Q., Zhang, M., & Zhang, M. (2025) · arXiv
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Ripple Effect Protocol: Coordinating Agent Populations
Chopra, A., Sharma, A., Ahmad, F., Muscariello, L., Pandey, V., & Raskar, R. (2025) · arXiv
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Bridging graph data models: RDF, RDF-star, and property graphs as directed acyclic graphs
Gelling, E., Fletcher, G., & Schmidt, M. (2023) · arXiv
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Avoiding Haag's theorem with parameterized quantum field theory
Seidewitz, E. (2015) · arXiv
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F., & Hein, M. (2020) · arXiv
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Decentralized autonomous organizations – Evolution, challenges, and opportunities
Schneider, B., Ballesteros, R., Moriggl, P., & Asprion, P. (2022) · Fachhochschule Nordwestschweiz FHNW
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RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
Chan, C.-M., Xu, C., Yuan, R., Luo, H., Xue, W., Guo, Y., & Fu, J. (2024) · arXiv
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Killing Horizons as Equipotential Hypersurfaces
Smolić, I. (2012) · arXiv
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Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
Wu, J., Zhu, J., Qi, Y., Chen, J., Xu, M., Menolascina, F., & Grau, V. (2024) · arXiv
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2019) · arXiv
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Microlocal spectrum condition and Hadamard form for vector-valued quantum fields in curved spacetime
Sahlmann, H., & Verch, R. (2000) · arXiv
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A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models
Lyu, C., Du, Z., Xu, J., Duan, Y., Wu, M., Lynn, T., Aji, A. F., Wong, D. F., Liu, S., & Wang, L. (2023) · arXiv
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Land Tenure in Ethiopia: Continuity and Change, Shifting Rulers, and the Quest for State Control
Crewett, W., Bogale, A., Korf, B., Crewett, W., Bogale, A., & Korf, B. (2008) · Unknown
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