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Monthly AI Papers: May 2026

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Read this article in Korean Trends The three papers selected for May 2026 examine adaptation through persistent state: δ-mem, SkillOS, and AutoScientists all carry information from earlier interactions or experiments into later decisions. They store different kinds of experience. δ-mem maintains associative memory, a compact matrix of learned key–value relationships;...

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2026년 5월 AI 논문 동향

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영문판 보기 이달의 트렌드 2026년 5월에 선정한 세 논문은 지속 상태를 통한 적응을 다룬다. δ-mem, SkillOS, AutoScientists는 모두 이전 상호작용이나 실험에서 얻은 정보를 이후 의사결정에 활용한다. 각 방법이 저장하는 경험의 형태는 다르다. δ-mem은 학습한 key–value 관계를 작은 행렬에 담는 연관 메모리를 유지한다. SkillOS는 Markdown 파일에 저장한 재사용 가능한 지침인 절차적...

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Self-Improving Language Models with Bidirectional Evolutionary Search | Summary

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This article explains the key points of Self-Improving Language Models with Bidirectional Evolutionary Search. 2026-05-27 (arXiv), Preprint Xu, Guowei, Qi, Zhenting, Su, Huangyuan, Ye, Weirui, Lakkaraju, Himabindu, Kakade, Sham M., Du, Yilun. Harvard University, MIT Paper Github Project Page Read this article in Korean Summary Self-Improving Language Models with Bidirectional...

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Self-Improving Language Models with Bidirectional Evolutionary Search 요약 설명

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이번 글에서는 Self-Improving Language Models with Bidirectional Evolutionary Search 논문의 핵심 포인트만 간단히 정리한다. 2026년 5월 27일(Arxiv), Preprint Xu, Guowei, Qi, Zhenting, Su, Huangyuan, Ye, Weirui, Lakkaraju, Himabindu, Kakade, Sham M., Du, Yilun. Harvard University, MIT 논문 링크 Github Project Page 영문판 보기 요약 Self-Improving Language Models with Bidirectional Evolutionary...

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Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players | Summary

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This article explains the key points of Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players. 2026-05-27 (arXiv) Liu, Fangfu, He, Kai, Shen, Tianchang, Cao, Tianshi, Fidler, Sanja, Duan, Yueqi, Gao, Jun, Gilitschenski, Igor, Wang, Zian, Ren, Xuanchi. NVIDIA, Tsinghua University, University of Toronto, Vector Institute Paper Project Page Read this...

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