This article explains the key points of Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis. 2026-03-31 (arXiv) Chen, Shuang, Shou, Quanxin, Chen, Hangting, Zhou, Yucheng, Feng, Kaituo, Hu, Wenbo, Zhang, Yi-Fan, Lin, Yunlong, Huang, Wenxuan, Song, Mingyang, et al. University of California, Los Angeles, Tencent Hunyuan, The Chinese University...

이번 글에서는 Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis 논문의 핵심 포인트만 간단히 정리한다. 2026년 3월 31일(Arxiv) Chen, Shuang, Shou, Quanxin, Chen, Hangting, Zhou, Yucheng, Feng, Kaituo, Hu, Wenbo, Zhang, Yi-Fan, Lin, Yunlong, Huang, Wenxuan, Song, Mingyang, et al. University of California, Los Angeles, Tencent Hunyuan, The Chinese...

Read this article in Korean Trends Learned allocation is a common theme across March’s architecture studies. Beyond Language Modeling uses fine-grained Mixture-of-Experts routing to allocate capacity between language and vision, improving both modalities at approximately fixed active computation. Attention Residuals instead learns token-dependent mixtures of earlier layer outputs, replacing fixed...

영문판 보기 이달의 트렌드 3월의 아키텍처 연구에서는 자원 배분 방식을 학습하는 접근이 공통적으로 나타난다. Beyond Language Modeling은 세분화된 Mixture-of-Experts 라우팅으로 언어와 시각에 용량을 배분하며, 활성 연산량을 대략 고정한 상태에서 두 모달리티의 성능을 높인다. Attention Residuals는 이전 층 출력의 혼합 가중치를 토큰별로 학습하여, 고정된 잔차 누적을 깊이 방향의 선택적 참조로 대체한다....

This article explains the key points of Meta-Harness: End-to-End Optimization of Model Harnesses. 2026-03-30 (arXiv) Lee, Yoonho, Nair, Roshen, Zhang, Qizheng, Lee, Kangwook, Khattab, Omar, Finn, Chelsea. Stanford, KRAFTON, MIT Paper Read this article in Korean Summary Meta-Harness: End-to-End Optimization of Model Harnesses searches over the executable code surrounding a...