This article explains the key points of Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning. 2025-12-23 (arXiv) Kobayashi, Seijin, Schimpf, Yanick, Schlegel, Maximilian, Steger, Angelika, Wolczyk, Maciej, von Oswald, Johannes, Scherrer, Nino, Maile, Kaitlin, Lajoie, Guillaume, Richards, Blake A., et al. Google, Paradigms of Intelligence Team, Google Paper...

이번 글에서는 Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning 논문의 핵심 포인트만 간단히 정리한다. 2025년 12월 23일(Arxiv) Kobayashi, Seijin, Schimpf, Yanick, Schlegel, Maximilian, Steger, Angelika, Wolczyk, Maciej, von Oswald, Johannes, Scherrer, Nino, Maile, Kaitlin, Lajoie, Guillaume, Richards, Blake A., et al. Google, Paradigms of Intelligence Team, Google...

This article explains the key points of Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills. 2025-12-18 (arXiv) Jiang, Pengcheng, Lin, Jiacheng, Shi, Zhiyi, Wang, Zifeng, He, Luxi, Wu, Yichen, Zhong, Ming, Song, Peiyang, Zhang, Qizheng, Wang, Heng, et al. UIUC, Stanford, Princeton, Harvard, UC Berkeley, Caltech, UW,...

이번 글에서는 Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills 논문의 핵심 포인트만 간단히 정리한다. 2025년 12월 18일(Arxiv) Jiang, Pengcheng, Lin, Jiacheng, Shi, Zhiyi, Wang, Zifeng, He, Luxi, Wu, Yichen, Zhong, Ming, Song, Peiyang, Zhang, Qizheng, Wang, Heng, et al. UIUC, Stanford, Princeton, Harvard, UC Berkeley, Caltech,...

This article explains the key points of FrontierCS: Evolving Challenges for Evolving Intelligence. 2025-12-17 (arXiv) Mang, Qiuyang, Chai, Wenhao, Li, Zhifei, Mao, Huanzhi, Zhou, Shang, Du, Alexander, Li, Hanchen, Liu, Shu, Chen, Edwin, Wang, Yichuan, et al. UC Berkeley, Princeton University, UCSD, X-camp Academy, Independent, Georgia Tech, Stanford University, University...