EN
17 Feb 2026
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Paper Review
AI Coding Agents
Reinforcement Learning
Sparse Attention
Environment Scaling
This article explains the key points of GLM-5: from Vibe Coding to Agentic Engineering. 2026-02-17 (arXiv) GLM-5-Team, :, Zeng, Aohan, Lv, Xin, Hou, Zhenyu, Du, Zhengxiao, Zheng, Qinkai, Chen, Bin, Yin, Da, Ge, Chendi, et al. Zhipu AI, Tsinghua University Paper Github Project Page Read this article in Korean Summary...
KO
17 Feb 2026
|
Paper Review
AI Coding Agents
Reinforcement Learning
Sparse Attention
Environment Scaling
이번 글에서는 GLM-5: from Vibe Coding to Agentic Engineering 논문의 핵심 포인트만 간단히 정리한다. 2026년 2월 17일(Arxiv) GLM-5-Team, :, Zeng, Aohan, Lv, Xin, Hou, Zhenyu, Du, Zhengxiao, Zheng, Qinkai, Chen, Bin, Yin, Da, Ge, Chendi, et al. Zhipu AI, Tsinghua University 논문 링크 Github Project Page 영문판 보기 요약 GLM-5:...
EN
16 Feb 2026
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Paper Review
LLM Evaluation
Strategic Reasoning
LLM Safety
Metacognition
This article explains the key points of AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises. 2026-02-16 (arXiv) Payne, Kenneth. King’s College London Paper Read this article in Korean Summary AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises evaluates GPT-5.2, Claude...
KO
16 Feb 2026
|
Paper Review
LLM Evaluation
Strategic Reasoning
LLM Safety
Metacognition
이번 글에서는 AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises 논문의 핵심 포인트만 간단히 정리한다. 2026년 2월 16일(Arxiv) Payne, Kenneth. King’s College London 논문 링크 영문판 보기 요약 AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises는 모의 핵 위기 21건에서...
EN
15 Feb 2026
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Paper Review
Reinforcement Learning
Self-Reflection
Self-Distillation
This article explains the key points of Experiential Reinforcement Learning. 2026-02-15 (arXiv) Shi, Taiwei, Chen, Sihao, Jiang, Bowen, Song, Linxin, Yang, Longqi, Zhao, Jieyu. University of Southern California, Microsoft, University of Pennsylvania Paper Read this article in Korean Summary Experiential Reinforcement Learning (ERL) trains language-model agents through an experience–reflection–consolidation loop....