This article explains the key points of Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness. 2026-07-21 (arXiv) Chen, Xilun, Feizollahi, Zhaleh, Goodwin, Ross, Moon, Seungwhan, Yih, Scott, Donmez, Pinar, Damavandi, Babak, Dong, Luna. Meta AI Paper Github Read this article in Korean Summary Gamut (Grounded Assessment...

이번 글에서는 Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness 논문의 핵심 포인트만 간단히 정리한다. 2026년 7월 21일(Arxiv) Chen, Xilun, Feizollahi, Zhaleh, Goodwin, Ross, Moon, Seungwhan, Yih, Scott, Donmez, Pinar, Damavandi, Babak, Dong, Luna. Meta AI 논문 링크 Github 영문판 보기 요약 Gamut(Grounded Assessment of Multimodal...

This article explains the key points of Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing. 2026-07-21 (arXiv) Zhang, Xinjie, Zhang, Peng, Zheng, Shicheng, Guo, Jinghao, Jia, Zhaoyang, Shen, Yifei, Guo, Xun, Luo, Yuxuan, Li, Jiahao, Xie, Wenxuan, et al. Microsoft Mage Team Paper Project Page Read this...

이번 글에서는 Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing 논문의 핵심 포인트만 간단히 정리한다. 2026년 7월 21일(Arxiv) Zhang, Xinjie, Zhang, Peng, Zheng, Shicheng, Guo, Jinghao, Jia, Zhaoyang, Shen, Yifei, Guo, Xun, Luo, Yuxuan, Li, Jiahao, Xie, Wenxuan, et al. Microsoft Mage Team 논문 링크 Project Page...

This article explains the key points of LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks. 2026-07-20 (arXiv), arXiv Ye, Tianzhu, Dong, Li, Chen, Guanheng, Zhu, He, Wu, Xun, Huang, Shaohan, Wei, Furu. Microsoft Research, Tsinghua University, Peking University Paper Read this article in Korean Summary The paper studies post-training for non-verifiable open-ended...