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https://dx.doi.org/10.1038/s41586-026-11044-y
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https://doi.org/10.5281/zenodo.21722356
com/Sisyphbaous-DT-Project/open-qingyi - Weights: https://huggingface
https://doi.org/10.48550/arXiv.2509.22186
In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables
https://doi.org/10.48550/arXiv.2508.19205
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Abstract:This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion
https://doi.org/10.48550/arXiv.2006.01311
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2006.01758
It has realised a great number of application scenarios, and improved workflow of many domains
https://doi.org/10.48550/arXiv.2311.12983
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2509.02523
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2212.07841
Based on it, we integrate three types of representative pre-training tasks: corrupted passages recovering, related passages recovering and PLMs outputs recovering, to characterize the inner-passage information, inter-passage relations and PLMs knowledge
https://doi.org/10.48550/arXiv.2508.02739
We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations
https://doi.org/10.48550/arXiv.2006.01402
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2608.03063
Against the production DeepSeek-based LLM baseline, it raises screening precision from 92
https://doi.org/10.48550/arXiv.2211.01910
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2211.10435
Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem
https://doi.org/10.48550/arXiv.2006.01957
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2508.16279
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Huggingface Toggle
Hugging Face (What is Huggingface
https://doi.org/10.48550/arXiv.2307.16789
It enables LLMs to evaluate multiple reasoning traces and expand the search space
https://doi.org/10.48550/arXiv.2309.17428
It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks
https://doi.org/10.1145%2F3397271.3401075
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