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  1. 5 days ago · This manuscript proposes and presents an analysis to quantify whether the OpenAI O1-preview model demonstrates a spark of intelligence or merely makes random guesses when outputting an assignment for a Boolean satisfiability problem. In this manuscript I present an analysis on the performance of OpenAI O1-preview model in solving random K-SAT instances for K$\\in {2,3,4}$ as a function of ...

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  2. 4 days ago · Using large language models (LLMs), Elicit finds papers relevant to your topic by searching through papers and citations and extracting and synthesizing key information. Semantic Scholar Database. Free trial available. Pay for credits after trial expires. Elicit FAQs.

  3. 3 days ago · The Semantic Reader product d is a free interactive interface for research papers. Semantic Reader supports useful augmentations atop the existing PDF—for example, (A) in situ Paper Cards when clicking inline citations and integration with Semantic Scholar, and (B) save to library.

  4. 3 days ago · SEMANTIC SCHOLAR. SCIENTIFIC LITERATURE SEARCH ENGINE - finding semantically similar research papers. "A free, AI-powered research tool for scientific literature." <https://www.semanticscholar.org/>. But login is required in order to use all functions.

  5. 5 days ago · Provides on-demand source of data about authors, papers, citations, venues, and more from Semantic Scholar, a free, AI-powered academic search engine with nearly 200 million papers covering all disciplines. No API key is required.

  6. 3 days ago · We propose Hi-SLAM, a semantic 3D Gaussian Splatting SLAM method featuring a novel hierarchical categorical representation, which enables accurate global 3D semantic mapping, scaling-up capability, and explicit semantic label prediction in the 3D world. The parameter usage in semantic SLAM systems increases significantly with the growing complexity of the environment, making it particularly ...

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  8. 4 days ago · However, these models are trained to predict semantic similarity without utilizing interaction data with hidden patterns specific to recommender systems. In this paper, we propose beeFormer, a framework for training sentence Transformer models with interaction data.

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