{"schema":"record/v1","id":"art_01M5MBYSG7FZ8VBZ34KHQH5CAZ","type":"article","revision":2,"etag":"\"r2.s0\"","title":"Why does an autoregressive video model get worse the longer it keeps generating, and what are the names and visible symptoms of the failure?","summary":"Corrected 2026-10-24 - see C3-B1094-02; the original statements below are kept for history and are superseded where the correction says so. The primary papers agree on one mechanism: the model is trained on clean or short context but, when it keeps generating, it conditions on its own slightly wrong frames, so small errors compound (called error accumulation or exposure bias). Reported symptoms differ by method: brightness drift (over-exposure or darkening), motion that freezes, colour shift, flicker when a window shifts, and (separately) content that diverges because nothing remembers what left the frame.","status":"active","body":{"text":"The primary papers agree on one mechanism: the model is trained on clean or short context but, when it keeps generating, it conditions on its own slightly wrong frames, so small errors compound (called error accumulation or exposure bias). Reported symptoms differ by method: brightness drift (over-exposure or darkening), motion that freezes, colour shift, flicker when a window shifts, and (separately) content that diverges because nothing remembers what left the frame.\n\nCurator correction 2026-10-24 (discovery audit record C3-B1094-02, curator-reviewed C3R-0699; excerpts re-located by independent curator GET 2026-10-24): Correction. 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It was not accurate when written to present flicker as a paper-reported long-rollout symptom of error accumulation.\n\nCurator qualifications: All excerpts located; error accumulation names and visible symptoms.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2506.08009v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2407.01392v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2510.02283v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2509.25161v1","note":"candidate evidence excerpts"}]},"categories":["T06","T15","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2506.08009v2","claim":"Curator correction 2026-10-24 (discovery audit record C3-B1094-02, curator-reviewed C3R-0699; excerpts re-located by independent curator GET 2026-10-24): Correction. 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Reported symptoms differ by method: brightness drift (over-exposure or darkening), motion that freezes, colour shift, flicker when a window shifts, and (separately) content that diverges because nothing remembers what left the frame.\n\nCurator qualifications: All excerpts located; error accumulation names and visible symptoms.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2506.08009v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2407.01392v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2510.02283v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2509.25161v1","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Why does an autoregressive video model get worse the longer it keeps generating, and what are the names and visible symptoms of the failure?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"The primary papers agree on one mechanism: the model is trained on clean or short context but, when it keeps generating, it conditions on its own slightly wrong frames, so small errors compound (called error accumulation or exposure bias). Reported symptoms differ by method: brightness drift (over-exposure or darkening), motion that freezes, colour shift, flicker when a window shifts, and (separately) content that diverges because nothing remembers what left the frame.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2506.08009v2","claim":"The primary papers agree on one mechanism: the model is trained on clean or short context but, when it keeps generating, it conditions on its own slightly wrong frames, so small errors compound (called error accumulation or exposure bias). 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It was not accurate when written to present flicker as a paper-reported long-rollout symptom of error accumulation.\n\nCurator qualifications: All excerpts located; error accumulation names and visible symptoms.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2506.08009v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2407.01392v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2510.02283v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2509.25161v1","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Why does an autoregressive video model get worse the longer it keeps generating, and what are the names and visible symptoms of the failure?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Corrected 2026-10-24 - see C3-B1094-02; the original statements below are kept for history and are superseded where the correction says so. 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