Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds

arXiv 2026 47.5 system, method

TLDR

JoyAI-Echo-1.5 is a unified audio-visual generation system with long-video and world-model variants, achieving state-of-the-art results on WBench and SANA-WM-Bench.

Reasoning

The paper presents a strong unified system with novel memory and geometry-aware conditioning, supported by benchmark results. However, the abstract lacks details on limitations and ablations, and the world-model claims rely on standard benchmarks rather than real-world interactive deployment.

Read-first score

Read-first score 47.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 41.

Methodology quality 18%

100

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=baseline,benchmark,evaluation,experiment,metric,result

Recency 6%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Topical relevance 29%

58.6

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=github

Citation impact 18%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 12%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 689.

Keyword Scores

world model

generative world model

interactive world model

video world model

world simulator

world dynamics prediction

model-based reinforcement learning world model

Deep Analysis

Innovations

Unified audio-visual generation system (JoyAI-Echo-1.5) with two purpose-built variants for long-form video and interactive world generation.
Composable cross-shot memory that aggregates visual evidence across multiple prior shots and speaker cues derived from speech-filtered full-shot audio to preserve character appearance and voice identity.
Geometry-aware conditioning pathway that converts heterogeneous navigation inputs into calibrated metric 6-DoF camera trajectories for controller-agnostic interaction.
Causal few-step generation via progressive teacher forcing and short- and long-horizon Self-Gradient Forcing on self-generated rollouts.

Methodology

JoyAI-Echo-1.5 transforms a bidirectional audio-visual backbone into a causal few-step generator using progressive teacher forcing and short- and long-horizon Self-Gradient Forcing on self-generated rollouts. The long-video variant conditions on text, image, and composable cross-shot memory, while the world-model variant injects geometry-aware calibrated 6-DoF camera trajectories. Evaluation uses existing long-video baselines and benchmarks including WBench and SANA-WM-Bench, with metrics such as cross-shot consistency, visual quality, text alignment, speech fidelity, and average benchmark score.

Key Results

JoyAI-Echo-1.5 improves over existing long-video baselines in cross-shot consistency, visual quality, text alignment, and speech fidelity. Its world-model variant ranks first on WBench with an average score of 81.7 and achieves leading visual quality and long-horizon persistence on SANA-WM-Bench.

Resources

Paper