…AI-generated summary Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN, a multimodal system trained on 87,547…
…AI-generated summary While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video generation methods based on diffusion models…
…AI-generated summary Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottleneck. In this work, we propose In-context Sparse…
…AI-generated summary Long-video understanding in VLMs is bottlenecked by a single monolithic forward pass over thousands of frames at quadratic attention cost . A common mitigation is to first select a…
…It implicitly combines the advantages of both model-free and model-based approaches while avoiding the training costs associated with model-based methods. Nevertheless, existing model-based representation methods can fail to…
…Strong open reasoning models combined with next-gen creative tools—like Experience the future of AI image editing —open up some very compelling hybrid workflows for agents, content pipelines, and local-first…
…with implicit differentiation, achieving superior language modeling and reasoning performance with reduced computational costs compared to traditional transformers. AI-generated summary Looped Transformers offer a promising alternative to purely feed-forward computation…
…Semantic self-consistency improves this by clustering sampled answers by meaning using natural language inference , but it adds both sampling cost and external inference overhead. We show that first-token confidence , phi…
…existing strategies are either target-misaligned, incapable of few-step generation, or too costly to scale. We propose Causal Forcing++, a principled and scalable pipeline that uses causal consistency distillation ( causal CD…
…However, existing approaches typically require either training recurrent models from scratch or applying disruptive retrofits, which involve substantial computational costs and may compromise pretrained capabilities. To address these limitations, we introduce Looped…