Blog Post number 2
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AI / ML Engineer @ PyTorch-Meta
less than 1 minute read
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
less than 1 minute read
Published:
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9 minute read
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Authors: Hangrui Hu, Xinfa Zhu, Ting He, Dake Guo, Bin Zhang, Xiong Wang, Zhifang Guo, Ziyue Jiang, Hongkun Hao, Zishan Guo, Xinyu Zhang, Pei Zhang, Baosong Yang, Jin Xu, Jingren Zhou, Junyang Lin arXiv: arxiv.org/abs/2601.15621 Summary: Qwen3-TTS is a multilingual streaming text-to-speech family trained on more than five million hours of speech across ten languages, with three-second voice cloning and description-based voice control. The report describes two speech tokenizers and a dual-track language-model architecture, including a low-bitrate configuration with 97-millisecond first-packet emission, and releases the models and tokenizers under Apache 2.0.
10 minute read
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Authors: Hao Fei arXiv: arxiv.org/abs/2611.05376 Summary: This perspective proposes AI-augmented human intelligence and a cognitive symbiont that maintains personal context over years, senses situations through devices, and proactively supports thinking and everyday action. It organizes the design around the gap between a person’s available knowledge and working memory, treating privacy, trust, covert assistance, and adoption pressure as integral considerations rather than reporting a deployed system’s performance.
11 minute read
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Authors: Lang Mo, Xingxuan Zhang, Qi Wang, Mingchao Hao, Hao Yuan, Chunjing Gan, Wang Lu, Peng Cui arXiv: arxiv.org/abs/2611.02075 Summary: DPDM combines a language model’s semantic representations with a shared frozen conditional predictor, using deployment history to support tool judgments, candidate preferences, and reasoning assessments without updating neural parameters. With Qwen3.5-27B and a LimiX-2 predictor, the authors report mean accuracy rising from 78.80 percent to 84.92 percent across 50 benchmarks under the respective evaluated history budgets, or 82.33 percent with matched histories and queries.