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Multimodal AI Needs More Than Modality Support: Researchers Propose General-Level and General-Bench to Evaluate True Synergy in Generalist Models

Artificial intelligence has grown beyond language-focused systems, evolving into models capable of processing multiple input types, such as text, images, audio, and video. This area, known as multimodal learning, aims to replicate the natural human ability to integrate and interpret varied sensory data. Unlike conventional AI models that handle a single modality, multimodal generalists are…

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Researchers at Physical Intelligence Introduce π-0.5: A New AI Framework for Real-Time Adaptive Intelligence in Physical Systems

Designing intelligent systems that function reliably in dynamic physical environments remains one of the more difficult frontiers in AI. While significant advances have been made in perception and planning within simulated or controlled contexts, the real world is noisy, unpredictable, and resistant to abstraction. Traditional AI systems often rely on high-level representations detached from their…

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Empowering LLMs to Think Deeper by Erasing Thoughts

Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called chain-of-thought (CoT), where the model iteratively generates intermediate…

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When Algorithms Dream of Photons: Can AI Redefine Reality Like Einstein? | by Manik Soni | Jan, 2025

In 1905, Albert Einstein published a paper on the photoelectric effect — a deceptively simple observation that light could eject electrons from metals. This work, which later won him the Nobel Prize, didn’t just explain an oddity in physics. It shattered classical mechanics, birthing quantum theory and reshaping our understanding of reality. But here’s a…

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This AI Paper Introduces MAETok: A Masked Autoencoder-Based Tokenizer for Efficient Diffusion Models

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly when operating directly on high-dimensional pixel data. Researchers have been investigating ways to optimize latent space representations to improve efficiency without compromising image quality. A critical problem in diffusion models is…

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