This article explains the key points of Scaling Laws for Looped Mixture of Experts. 2026-09-30 (arXiv) Chen, Yanbei, Goyal, Anirudh, Krishnamoorthi, Raghuraman. Meta AI Paper Read this article in Korean Summary The paper studies how to allocate language-model capacity between recurrence, repeated passes through shared layers, and Mixture-of-Experts (MoE), which...

이번 글에서는 Scaling Laws for Looped Mixture of Experts 논문의 핵심 포인트만 간단히 정리한다. 2026년 9월 30일(Arxiv) Chen, Yanbei, Goyal, Anirudh, Krishnamoorthi, Raghuraman. Meta AI 논문 링크 영문판 보기 요약 이 논문은 언어 모델의 용량을 recurrence와 Mixture-of-Experts(MoE)에 어떻게 배분할지 연구한다. Recurrence는 공유 계층을 반복해서 통과하는 방식이고, MoE는 토큰마다 일부 expert...

This article explains the key points of Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents. 2026-09-30 (arXiv) Kang, Minki, Hachiuma, Ryo, Zhang, Shaokun, Radhakrishnan, Subhashree, Fu, Yonggan, Jiang, Jindong, Liu, Mingjie, Hosseini-Asl, Ehsan, Dong, Yi, Wang, Yu-Chiang Frank, et al. NVIDIA, KAIST Paper Project Page Read this article...

이번 글에서는 Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents 논문의 핵심 포인트만 간단히 정리한다. 2026년 9월 30일(Arxiv) Kang, Minki, Hachiuma, Ryo, Zhang, Shaokun, Radhakrishnan, Subhashree, Fu, Yonggan, Jiang, Jindong, Liu, Mingjie, Hosseini-Asl, Ehsan, Dong, Yi, Wang, Yu-Chiang Frank, et al. NVIDIA, KAIST 논문 링크 Project Page 영문판...

Read this article in Korean Trends September 2026’s theme is testing whether measured capability survives practical use: the three selected papers examine whether improvements in search scores, mixed-input predictions, or written problem solving translate into unseen-task performance, separate text generation, or interactive execution. RRSI studies an agent harness, the prompts,...