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      "statement": "# Diffusion Forcing: independently noised token sequences with causal generation\n\nTrained a causal denoising sequence model with independent per-token noise levels, permitting variable-horizon generation, guidance and planning within one training/sampling framework.\n\n## Scientific scope\n\nInherits diffusion, teacher forcing and planning. The paper's conditional variational-bound result is source-reported, not proof-audited here. Long-rollout examples do not guarantee indefinite physical fidelity, bounded control error or all-environment planning.\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\nBoyuan Chen; Diego Martí Monsó; Yilun Du; Max Simchowitz; Russ Tedrake; Vincent Sitzmann\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- [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](https://arxiv.org/abs/2407.01392v1)\n",
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