{"data":{"id":"350abf59-39a8-47a0-8119-0ba080be3e09","title":"\nError Analyses of Auto-Regressive Video Diffusion Models\n","summary":"Auto-regressive video diffusion models (AR-VDMs, systems that generate videos by predicting one frame at a time) struggle with two problems: history forgetting, where they lose track of earlier frames they created, and temporal degradation, where video quality gets worse over time. Researchers created Meta-ARVDM, a theoretical framework that analyzes both problems and shows that using more past frames reduces history forgetting, while also introducing a new way to evaluate these models using a \"needle-in-a-haystack\" test (finding specific content buried in larger data).","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"\nhttp://jmlr.org/papers/v27/25-3128.html\n","publishedAt":"2026-01-01T00:00:00.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-01-01T00:00:00.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}