이번 글에서는 Progress Reward Modeling for Robotic Learning: A Comprehensive Survey 논문의 핵심 포인트만 간단히 정리한다. 2026년 7월 22일(Arxiv) Zhang, Jianshu, Wu, Keliang, Lu, Haoran, Liu, Anbang, Zhang, Ce, Yin, Weijie, Qian, Chengxuan, Yang, Xiyuan, Pan, Zhenyu, Ye, Guo, et al. Northwestern University, Carnegie Mellon University, University of Wisconsin–Madison, University...

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 evaluates factual...

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 GAMUT는 답변에 포함된 주장...

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 Paper Project Page Read this article in...

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 논문 링크 Project Page 영문판 보기...