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Michael Ying

As seen in: Nature, arXiv, MDPI, Schwab, Archives of Physical Medicine and Rehabilitation
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Articles

4 Ways AI Trading Tools Help Increase Efficiency

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By Michael Ying, Agustín Escobar
| Schwab
As promising as these new AI tools are, they're still subject to errors, including data misinterpretations or so-called hallucinations where the software fabricates information. Particularly when using agents, the accuracy and efficacy of results depend greatly on the construction of the prompts; education and the ability to create detailed prompts go hand in hand. Investors cannot expect those tools to make decisions for them.
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Computer Science > Computer Vision and Pattern Recognition

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By Michael Ying
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Multi-Object Tracking Retrieval with LLaVA-Video: A Training-Free Solution to MOT25-StAG Challenge

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By Michael Ying
| arXiv
arXiv:2511.03332 (cs) View PDF HTML (experimental) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2511.03332 [cs.CV] (or arXiv:2511.03332v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2511.03332 Submission history From: Yi Yang [ view email] [v1] Wed, 5 Nov 2025 10:01:31 UTC (8,990 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps...
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