Recent progress in Omni-models has yielded strong performance on benchmarks focused on static observations. However, their ability to maintain spatiotemporal continuity when predicting coherent future states from previous context remains underexplored. We address this by formulating Next Scene Prediction (NSP) as a diagnostic task for assessing multimodal near-future state prediction, requiring models to anticipate the immediate future from a given context. We introduce OmniNSP, a benchmark evaluating causal and cross-modal coherence across physical, logical, and social dynamics. Unlike prior tasks that often rely on textual or discriminative proxies, OmniNSP asks models to express predicted future states through text-mediated visual and speech synthesis. Our analysis shows that current Omni-models can produce plausible textual forecasts while still failing to preserve future-state consistency in generated outputs. A frozen-generator control substantially narrows cross-model visual-consistency gaps, attributing much of the original system-level difference to model-specific realization stacks rather than textual foresight alone. A modality-input ablation further shows that audio provides complementary predictive cues beyond vision, with the largest gains for logical transitions and contexts with informative speech. These results position OmniNSP as a diagnostic of both near-future prediction and the preservation of predicted state through multimodal realization.

Modern generative models still lack human-level creativity, particularly in multi-branch diversity. Prior approaches to address this problem often incur heavy computation or strong dependency on model architecture. Therefore, we introduce UAG(Universal Avoidance Generation), a model-agnostic and computationally efficient generation strategy that penalizes similarity among previously generated outputs. Thus, UAG can enhance multi-branch diversity across both diffusion and transformer models, with minimal additional computation. In experiments, our method achieves up to 1.9 times higher diversity, runs 4.4 times faster, and requires only 1/64 of the FLOPs compared to state-of-the-art methods.

Story visualization requires generating a coherent sequence of images that collectively form a narrative, yet existing evaluation metrics and datasets often overlook visual continuity and narrative diversity. In this paper, we introduce the Visual Context-Aware Metric for Story Visualization, which uses large vision-language models to jointly assess caption fidelity and inter-image consistency, achieving Spearman’s correlation comparable to human agreement on two benchmarks. Also, to address the shortcomings of narrowly defined datasets with low diversity, we propose a diffusion-augmented evaluation pipeline that blends diverse and controlled narrative elements at adjustable ratios, producing challenging evaluation sets. By combining VCMS with this pipeline, we provide a scalable, human-aligned framework for evaluating story visualization models.

Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field. In doing so, we propose entropy-adaptive Gibbs sampling and entropy-based noise scheduling to counterbalance each model’s shortcomings. Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.