Alireza Shamsoshoara

Alireza Shamsoshoara

AI / ML Engineer @ PyTorch-Meta

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Daily AI Papers — November 08, 2026

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1. Qwen3-TTS Technical Report

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.

Daily AI Papers — November 07, 2026

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1. From Autonomous Agents to Cognitive Symbionts: Toward AI-Augmented Human Intelligence

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.

Daily AI Papers — November 06, 2026

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1. Decoupled Predictive Decision Making: A Shared Conditional Predictor for Agents

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.

Daily AI Papers — November 05, 2026

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1. Turns Matter: Advancing Streaming Video RL via Turn-Level Credit

Authors: Shuai Dong, Kanghui Tian, Linghan Chen, Wenqi Shao, Hongrui Wu, Haowen Hou, Yongfu Zhu, Xuehui Wang, Ziyue Wang, Wenpu Liu, Weichu Xie, Yuqi Xu, Congcong Wang, Kaiwen Tuo, Caoyuan Ma, Taiqiang Wu, Ji Zhao, Wenzheng Chang, Dianyi Wang, Siyuan Wang, Zhongyu Wei, Yu Qiao, Jiaqi Wang arXiv: arxiv.org/abs/2611.06728 Summary: TurnWise assigns credit to individual respond-or-wait decisions in streaming video GRPO, using silence-completed prefix scoring and losses averaged equally across turns. The authors report better streaming-video understanding than prior RL approaches without a learned critic or additional rollouts.