이번 글에서는 In-Place Test-Time Training 논문의 핵심 포인트만 간단히 정리한다. 2026년 4월 7일(Arxiv) Feng, Guhao, Luo, Shengjie, Hua, Kai, Zhang, Ge, He, Di, Huang, Wenhao, Cai, Tianle. ByteDance Seed, Peking University 논문 링크 Github 영문판 보기 요약 In-Place Test-Time Training은 attention을 유지하면서 문맥에 따라 기존 Transformer MLP 블록의 마지막 projection...

This article explains the key points of Synthetic Sandbox for Training Machine Learning Engineering Agents. 2026-04-06 (arXiv) Zhou, Yuhang, Zhang, Lizhu, Wu, Yifan, Liu, Jiayi, Fan, Xiangjun, Zhao, Zhuokai, Yan, Hong. Meta AI Paper Read this article in Korean Summary Synthetic Sandbox for Training Machine Learning Engineering Agents introduces SandMLE,...

이번 글에서는 Synthetic Sandbox for Training Machine Learning Engineering Agents 논문의 핵심 포인트만 간단히 정리한다. 2026년 4월 6일(Arxiv) Zhou, Yuhang, Zhang, Lizhu, Wu, Yifan, Liu, Jiayi, Fan, Xiangjun, Zhao, Zhuokai, Yan, Hong. Meta AI 논문 링크 영문판 보기 요약 Synthetic Sandbox for Training Machine Learning Engineering Agents는 궤적 단위 온폴리시...

This article explains the key points of How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings. 2026-04-06 (arXiv), Preprint. Under review. Liu, Yujian, Ji, Jiabao, An, Li, Jaakkola, Tommi, Zhang, Yang, Chang, Shiyu. UC Santa Barbara, MIT CSAIL, MIT-IBM Watson AI Lab Paper...

이번 글에서는 How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings 논문의 핵심 포인트만 간단히 정리한다. 2026년 4월 6일(Arxiv), Preprint. Under review. Liu, Yujian, Ji, Jiabao, An, Li, Jaakkola, Tommi, Zhang, Yang, Chang, Shiyu. UC Santa Barbara, MIT CSAIL, MIT-IBM Watson AI Lab...