{"data":{"id":"4fe338ab-3d63-4edb-8bab-e1a8c485ccf1","title":"Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation","summary":"Adversarial Gradient Forcing (AGF) is a training framework for few-step autoregressive video generation models that targets the local history robustness gap, where small errors in the model's own generated history get amplified over time. AGF uses distribution matching gradients from future block predictions to choose adversarial perturbations of self-generated histories, then trains the generator on both original and perturbed histories. Experiments report the highest VBench Total score on 60-second video generation and the best visual quality on four-minute videos, using only 5-second rollouts for training.","solution":"N/A -- no mitigation discussed in source.","labels":["research","safety"],"sourceUrl":"https://arxiv.org/abs/2610.14553v1","publishedAt":"2026-10-10T04:22:20.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-10-10T04:22:20.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.8,"researchCategory":"preprint","atlasIds":null}}