AI Breakdown Podcast Por agibreakdown arte de portada

AI Breakdown

AI Breakdown

De: agibreakdown
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The podcast where we use AI to breakdown the recent AI papers and provide simplified explanations of intricate AI topics for educational purposes. The content presented here is generated automatically by utilizing LLM and text to speech technologies. While every effort is made to ensure accuracy, any potential misrepresentations or inaccuracies are unintentional due to evolving technology. We value your feedback to enhance our podcast and provide you with the best possible learning experience.Copyright 2023 All rights reserved. Ciencia
Episodios
  • World Simulation with Video Foundation Models for Physical AI

    World Simulation with Video Foundation Models for Physical AI
    Nov 8 2025
    In this episode, we discuss World Simulation with Video Foundation Models for Physical AI by NVIDIA, :, Arslan Ali, Junjie Bai, Maciej Bala, Yogesh Balaji, Aaron Blakeman, Tiffany Cai, Jiaxin Cao, Tianshi Cao, Elizabeth Cha, Yu-Wei Chao, Prithvijit Chattopadhyay, Mike Chen, Yongxin Chen, Yu Chen, Shuai Cheng, Yin Cui, Jenna Diamond, Yifan Ding, Jiaojiao Fan, Linxi Fan, Liang Feng, Francesco Ferroni, Sanja Fidler, Xiao Fu, Ruiyuan Gao, Yunhao Ge, Jinwei Gu, Aryaman Gupta, Siddharth Gururani, Imad El Hanafi, Ali Hassani, Zekun Hao, Jacob Huffman, Joel Jang, Pooya Jannaty, Jan Kautz, Grace Lam, Xuan Li, Zhaoshuo Li, Maosheng Liao, Chen-Hsuan Lin, Tsung-Yi Lin, Yen-Chen Lin, Huan Ling, Ming-Yu Liu, Xian Liu, Yifan Lu, Alice Luo, Qianli Ma, Hanzi Mao, Kaichun Mo, Seungjun Nah, Yashraj Narang, Abhijeet Panaskar, Lindsey Pavao, Trung Pham, Morteza Ramezanali, Fitsum Reda, Scott Reed, Xuanchi Ren, Haonan Shao, Yue Shen, Stella Shi, Shuran Song, Bartosz Stefaniak, Shangkun Sun, Shitao Tang, Sameena Tasmeen, Lyne Tchapmi, Wei-Cheng Tseng, Jibin Varghese, Andrew Z. Wang, Hao Wang, Haoxiang Wang, Heng Wang, Ting-Chun Wang, Fangyin Wei, Jiashu Xu, Dinghao Yang, Xiaodong Yang, Haotian Ye, Seonghyeon Ye, Xiaohui Zeng, Jing Zhang, Qinsheng Zhang, Kaiwen Zheng, Andrew Zhu, Yuke Zhu. The paper presents Cosmos-Predict2.5, a unified flow-based model that integrates Text2World, Image2World, and Video2World generation, enhanced by Cosmos-Reason1 for improved text grounding and control. Trained on 200M videos and refined with reinforcement learning, it outperforms its predecessor in video quality and instruction alignment, supporting robotics and autonomous system simulations. Additionally, Cosmos-Transfer2.5 enables high-fidelity Sim2Real and Real2Real translation with smaller model size, and both models and resources are released openly to advance Physical AI research.
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    10 m
  • Towards Robust Mathematical Reasoning

    Towards Robust Mathematical Reasoning
    Nov 6 2025
    In this episode, we discuss Towards Robust Mathematical Reasoning by Thang Luong, Dawsen Hwang, Hoang H. Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Clara Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu H. Trinh, Quoc V. Le, Junehyuk Jung. The paper introduces IMO-Bench, a new suite of challenging mathematical reasoning benchmarks based on International Mathematical Olympiad problems to better evaluate foundation models. Their model, Gemini Deep Think, achieved state-of-the-art results, surpassing previous models significantly on both answer accuracy and proof-writing tasks. The authors also developed reliable autograders aligned with human evaluations and released the benchmark suite publicly to advance robust mathematical reasoning.
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    8 m
  • ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

    ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
    Nov 4 2025
    In this episode, we discuss ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models by Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, Yi Dong. This paper introduces ProRL, a new reinforcement learning training method that uncovers novel reasoning strategies beyond those found in base language models. Empirical results show that models trained with ProRL consistently outperform base models on challenging reasoning tasks, including cases where base models fail even with extensive attempts. The study demonstrates that prolonged RL can meaningfully expand reasoning capabilities by exploring new solution spaces over time, advancing understanding of how RL enhances language model reasoning.
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    7 m
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