Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev, “Alice in Wonderland: Simple Tasks showing complete reasoning Breakdown in State-Of-the-Art Large Language Models,” arXiv (Cornell University), Jun. 2024, doi: 10.48550/arxiv.2406.02061.
Resolving Discrepancies in Compute-Optimal Scaling of Language Models
Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, and Yair Carmon, “Resolving discrepancies in Compute-Optimal scaling of language models,” arXiv (Cornell University), Jun. 2024, doi: 10.48550/arxiv.2406.19146.
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models
Julian Spravil, Sebastian Houben, and Sven Behnke, “Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models,” arXiv.org, Mar. 12, 2025. https://arxiv.org/abs/2503.09443
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs
Christoph Schuhmann, Jenia Jitsev, “Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs,” arXiv.org, Feb. 26, 2025. https://arxiv.org/abs/2502.19413
ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail Transfer
Julian Kaltheuner, Patrick Stotko, and Reinhard Klein, “ROSA: Reconstructing object shape and appearance textures by adaptive detail transfer,” arXiv (Cornell University), Jan. 2025, https://doi.org/10.48550/arXiv.2501.18595
NeRFs are Mirror Detectors: Using Structural Similarity for Multi-View Mirror Scene Reconstruction with 3D Surface Primitive
Leif Van Holland, Michael Weinmann, Jan U. Müller, atrick. Stotko, and Reinhard Klein, “NeRFs are Mirror Detectors: Using Structural Similarity for Multi-View Mirror Scene Reconstruction with 3D Surface Primitives,” arXiv.org, Jan. 07, 2025. https://arxiv.org/abs/2501.04074
Simplifying the Theory on Over-Smoothing
Andreas Roth, “Simplifying the theory on Over-Smoothing,” OpenReview, Jan. 01, 2024. https://openreview.net/forum?id=0KIfUdqIEk
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph
Andreas Roth, Franka Bause, Nils Morten Kriege, and Thomas Liebig, “Preventing representational rank collapse in MPNNs by splitting the computational graph,” OpenReview. https://openreview.net/forum?id=DOh3hW1OZu
Multimodale Foundation- Modelle in der Produktion
Hannes Behnen, Jan-Henrik Woltersmann, Dominik Wolfschläger, and Robert Schmitt, “Multimodale Foundation-Modelle in der Produktion/Possibilities of the Latest AI Models in Production – Multi-Modal Foundation Models in Production,” Wt Werkstattstechnik Online, vol. 114, no. 11–12, pp. 747–754, Jan. 2024, doi: 10.37544/1436-4980-2024-11-12-43.
Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines
Enrique Iglesias, Maria-Esther Vidal, Diego Collarana, and David Chaves-Fraga, “Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1,” Semantic Web, pp. 1–28, Mar. 2024, doi: 10.3233/sw-243580.
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