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    <title>The Rise of Diffusion LMs — Can They Become an Alternative to Autoregression</title>
    <link>https://www.youngju.dev/blog/llm/2026-06-12-diffusion-language-models-rise.en</link>
    <description>In June 2026 Google released DiffusionGemma, and text diffusion models are lighting up GeekNews and Hacker News. We critically analyze the structural limits of autoregressive generation, the principles of mask-based denoising, block-wise parallel generation, what the 4x speed claim really means, and the quality trade-offs behind it.</description>
    <pubDate>Fri, 12 Jun 2026 00:00:00 GMT</pubDate>
    <author>fjvbn2003@gmail.com (Youngju Kim)</author>
    <category>llm</category><category>diffusion</category><category>text-generation</category><category>gemma</category><category>inference</category><category>architecture</category>
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    <guid>https://www.youngju.dev/blog/llm/2026-06-12-diffusion-language-models-rise.ja</guid>
    <title>Diffusion LMの台頭 — 自己回帰の代替になり得るか</title>
    <link>https://www.youngju.dev/blog/llm/2026-06-12-diffusion-language-models-rise.ja</link>
    <description>2026年6月、GoogleがDiffusionGemmaを公開し、テキスト拡散モデルがGeekNewsとHacker Newsを賑わせています。自己回帰生成の構造的限界、マスキングに基づくデノイジングの原理、ブロック単位の並列生成、4倍速いという主張の実態と品質トレードオフまで批判的に分析します。</description>
    <pubDate>Fri, 12 Jun 2026 00:00:00 GMT</pubDate>
    <author>fjvbn2003@gmail.com (Youngju Kim)</author>
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    <guid>https://www.youngju.dev/blog/llm/2026-06-12-diffusion-language-models-rise</guid>
    <title>Diffusion LM의 부상 — 자기회귀의 대안이 될 수 있을까</title>
    <link>https://www.youngju.dev/blog/llm/2026-06-12-diffusion-language-models-rise</link>
    <description>2026년 6월 구글이 DiffusionGemma를 공개하며 텍스트 확산 모델이 GeekNews와 해커뉴스를 달구고 있습니다. 자기회귀 생성의 구조적 한계, 마스킹 기반 디노이징의 원리, 블록 단위 병렬 생성, 4배 빠르다는 주장의 실체와 품질 트레이드오프까지 비판적으로 분석합니다.</description>
    <pubDate>Fri, 12 Jun 2026 00:00:00 GMT</pubDate>
    <author>fjvbn2003@gmail.com (Youngju Kim)</author>
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