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AI is Reshaping Creative Fields: Music, Writing & Art

The Rise of AI in Creative Domains Artificial Intelligence (AI) has moved far beyond number-crunching and automation. Today, it’s playing a transformative role in traditionally human-centric fields like music, writing, and visual art. Algorithms are composing melodies, generating stories, and producing visuals that rival those created by human hands. As this shift unfolds, it prompts…

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ByteDance Researchers Introduce DetailFlow: A 1D Coarse-to-Fine Autoregressive Framework for Faster, Token-Efficient Image Generation

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing…

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Gemini 2.5’s native audio capabilities

Safety and responsibility We’ve proactively assessed potential risks throughout every stage of the development process for these native audio features, using what we’ve learned to inform our mitigation strategies. We validate these measures through rigorous internal and external safety evaluations, including comprehensive red teaming for responsible deployment. Additionally, all audio outputs from our models are…

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Samsung Researchers Introduced ANSE (Active Noise Selection for Generation): A Model-Aware Framework for Improving Text-to-Video Diffusion Models through Attention-Based Uncertainty Estimation

Video generation models have become a core technology for creating dynamic content by transforming text prompts into high-quality video sequences. Diffusion models, in particular, have established themselves as a leading approach for this task. These models work by starting from random noise and iteratively refining it into realistic video frames. Text-to-video (T2V) models extend this…

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Researchers Introduce MMLONGBENCH: A Comprehensive Benchmark for Long-Context Vision-Language Models

Recent advances in long-context (LC) modeling have unlocked new capabilities for LLMs and large vision-language models (LVLMs). Long-context vision–language models (LCVLMs) show an important step forward by enabling LVLMs to process hundreds of images and thousands of interleaved text tokens in a single forward pass. However, the development of effective evaluation benchmarks lags. It is…

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