Adrien Deschenaux, Nadezhda Ilieva, Justin Deschenaux, Manuel Madeira, Dorina Thanou, Aurélien Lucchi, Antonio Orvieto, Caglar Gulcehre · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30068 · open-access PDF
تستخدم النماذج اللغوية التلفزيونية الأخيرة (DLMs) Self-Conditioning (SC) ، أو Loopholing ، لتعزيز أداءها. في حين أن SC يُعتبر تقليدياً إعلاميًا مساعدًا ، فإن نجاحها الفني يضع سؤالًا: هل يمكن أن يعتمد نموذج التلفزيون المستمر فقط على Self-Conditioning؟ ونحن نرد على ذلك مع LAW ("Loopholing All the Way") ، الذي يُنشر التلفزيون المتوقع تحت التوقيت من التلفزيون عبر الخطوات ، وتقسيم الأوراق مرة واحدة في نهاية المطاف.
Machine translation (Meta M2M100); the English original follows.
Recent Diffusion Language Models (DLMs) use Self-Conditioning (SC), or Loopholing, to improve their performance. While SC is traditionally seen as an auxiliary heuristic, its empirical success raises the question: can a continuous generative model be based purely on Self-Conditioning? We answer affirmatively with LAW (``Loopholing All the Way''), which propagates the Expected Embedding under the denoiser's prediction across steps, decoding the tokens once, at the end. On Sudoku, LAW outperforms previous Discrete and Continuous Diffusion Models, including Loopholing Discrete Diffusion. On Maze,
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