A^2RD: Agentic Autoregressive Diffusion for Long Video Consistency
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| Authors | Do Xuan Long et al. |
| Year | 2026 |
| HF Upvotes | 7 |
| arXiv | 2605.06924 |
| Download | |
| Code | https://github.com/dxlong2000/AARD |
Abstract
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^2RD, an Agentic Auto-Regressive Diffusion architecture that decouples creative synthesis from consistency enforcement. A^2RD formulates long video synthesis as a closed-loop process that synthesizes and self-improves video segment-by-segment through a Retrieve--Synthesize--Refine--Update cycle. It comprises three core components: (i) Multimodal Video Memory that tracks video progression across modalities; (ii) Adaptive Segment Generation that switches among generation modes for natural progression and visual consistency; and (iii) Hierarchical Test-Time Self-Improvement that self-improves each segment at frame and video levels to prevent error propagation. We further introduce LVBench-C, a challenging benchmark with non-linear entity and environment transitions to stress-test long-horizon consistency. Across public and LVBench-C benchmarks spanning one- to ten-minute videos, A^2RD outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative coherence. Human evaluations corroborate these gains while also highlighting notable improvements in motion and transition smoothness.
Engineering Breakdown
Plain English
A^2RD solves long-form video synthesis by treating it as a closed-loop process where the system generates video segments, then continuously refines them based on consistency checks. Instead of trying to generate an entire coherent video end-to-end (which fails due to semantic drift), the method decouples generation from consistency enforcement, processing video segment-by-segment while maintaining a multimodal memory of what's already been generated.
Key Engineering Insight
The core innovation is treating long video generation as an iterative refinement problem rather than a single forward pass. A Retrieve–Synthesize–Refine–Update cycle with test-time self-improvement lets the system catch and fix consistency errors locally before they compound across the entire video, which is how human video editing actually works.
Why It Matters for Engineers
Long-form video generation is a hard constraint problem in production systems: frame-to-frame consistency, semantic coherence, and visual quality all degrade exponentially with sequence length. This approach's segment-by-segment refinement with memory tracking directly addresses the 'error accumulation' bottleneck that makes existing autoregressive video models unusable beyond 30-60 seconds.
Research Context
Prior work (like autoregressive diffusion models) struggled with semantic drift because errors in early frames propagate uncorrected into later generations. A^2RD advances beyond these methods by introducing adaptive generation modes and hierarchical self-improvement—essentially adding a feedback loop that existing one-shot generators lack. This enables practical long-video synthesis (likely 5+ minute sequences based on the architecture) by treating consistency as a solvable optimization problem rather than an inherent limitation.
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