multimodal AI
From early fusion to cross-modal attention, understanding the five core architectures behind AI systems that can see, read, and understand simultaneously—the foundation of modern synthetic media.
AI video generation
ByteDance launches Seedance 2.0, a next-generation AI model that generates video clips from text, images, audio, and video inputs, expanding multimodal capabilities in synthetic media.
voice AI
New research equips large language models with directional multi-talker speech capabilities, enabling AI to understand who is speaking and from where in complex audio environments.
multimodal AI
Researchers introduce MMR-Bench, a comprehensive benchmark evaluating how well routing systems direct queries to optimal multimodal LLMs across diverse visual reasoning tasks.
multimodal AI
New research introduces Omni-R1, a unified generative paradigm combining vision-language models with reinforcement learning for enhanced multimodal reasoning capabilities.
multimodal AI
The human brain seamlessly integrates sight, sound, and touch. Replicating this took a decade of AI research and seven critical innovations that now power today's video and image generation systems.
GUI Agents
Alibaba Tongyi Lab releases MAI-UI, a family of GUI agents achieving state-of-the-art results on AndroidWorld benchmarks, surpassing Gemini 2.5 Pro and other leading models.
vision models
Z.ai debuts GLM-4.6V, an open-source multimodal vision model with native tool-calling capabilities for complex reasoning tasks and automated workflows.
Reinforcement Learning
New research introduces agentic verifier approach to multimodal reinforcement learning, improving AI agent performance through self-verification and iterative refinement across vision-language tasks.
multimodal AI
Researchers develop training approach that enhances multimodal AI reasoning using smaller, more efficient datasets, potentially reducing computational costs while improving model performance across vision-language tasks.
AI Agents
Modern AI agents leverage vision-language models to interpret visual data, from video frames to UI screenshots. This technical overview explores the architectures and methods enabling multimodal agent capabilities.
AI Agents
Google DeepMind's SIMA 2 represents a significant evolution in AI agents capable of understanding and operating in 3D virtual environments, with implications for synthetic media creation and interactive AI systems.