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
Sementara SC secara tradisional dipandang sebagai heuristik auxiliary, kesuksesan empirisnya menimbulkan pertanyaan: apakah model generatif berkelanjutan dapat didasarkan sepenuhnya pada Self-Conditioning? Kami menjawab dengan LAW (‘Loopholing All the Way’), yang menyebarkan Expected Embedding di bawah prediksi denoiser melalui langkah-langkah, mendekodasi token sekali, pada akhirnya.
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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