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      "statement": "# Chefer et al.: dual-noise self-supervised representation learning within flow models\n\nApplied different noise levels across token subsets and matched mixed-input features to a cleaner internal target, combining representation prediction with standard flow loss without an externally pretrained representation teacher.\n\n## Scientific scope\n\nFlow matching, denoising and self-supervised alignment have predecessors, including independently noised sequence tokens. Scoped delta is dual-noise internal feature prediction combined with flow training, not invention of arbitrary heterogeneous noise. No external teacher encoder does not mean no training data, text supervision, autoencoder or extra forward cost. Report/index/arXiv dates differ; no exact first-disclosure-day priority inferred.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nHila Chefer; Patrick Esser; Dominik Lorenz; Dustin Podell; Vikash Raja; Vinh Tong; Antonio Torralba; Robin Rombach\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis](https://arxiv.org/abs/2603.06507v1)\n- [Self-Flow — original Black Forest Labs project](https://bfl.ai/research/self-flow)\n",
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