Research Papers

Concerto: How Joint 2D-3D Self-Supervised Learning Is Redefining Spatial Intelligence

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Concerto: How Joint 2D-3D Self-Supervised Learning Is Redefining Spatial Intelligence

The world of artificial intelligence is rapidly evolving and self-supervised learning has become a driving force behind breakthroughs in computer vision and 3D scene understanding. Traditional supervised learning relies heavily on labeled datasets which are expensive and time-consuming to produce. Self-supervised learning, on the other hand, extracts meaningful patterns without manual labels allowing models to … Read more

Pico-Banana-400K: The Breakthrough Dataset Advancing Text-Guided Image Editing

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Pico-Banana-400K: The Breakthrough Dataset Advancing Text-Guided Image Editing

Text-guided image editing has rapidly evolved with powerful multimodal models capable of transforming images using simple natural-language instructions. These models can change object colors, modify lighting, add accessories, adjust backgrounds or even convert real photographs into artistic styles. However, the progress of research has been limited by one crucial bottleneck: the lack of large-scale, high-quality, … Read more

Kimi Linear: The Future of Efficient Attention in Large Language Models

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Kimi Linear: The Future of Efficient Attention in Large Language Models

The rapid evolution of large language models (LLMs) has unlocked new capabilities in natural language understanding, reasoning, coding and multimodal tasks. However, as models grow more advanced, one major challenge persists: computational efficiency. Traditional full-attention architectures struggle to scale efficiently, especially when handling long context windows and real-time inference workloads. The increasing demand for agent-like … Read more

FIBO: The First JSON-Native, Open-Source Text-to-Image Model Built for Real-World Control and Accuracy

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FIBO: The First JSON-Native, Open-Source Text-to-Image Model Built for Real-World Control and Accuracy

The world of generative AI has evolved rapidly with text-to-image tools enabling creators, marketers, designers and enterprises to bring ideas to life with unprecedented ease. However, most existing models have a clear limitation: they prioritize imagination at the cost of control. Whether producing inconsistent styles, unpredictable lighting or drifting away from user prompts, traditional models … Read more

olmOCR: Redefining Document Understanding with Vision-Language Models

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olmOCR: Redefining Document Understanding with Vision-Language Models

The digital era has seen an explosion in the amount of information stored in PDFs, scanned documents and image-based files. From research papers and corporate reports to handwritten notes and invoices, these unstructured sources hold trillions of valuable data points. Yet, extracting and converting this data into structured, machine-readable text has long been a challenge. … Read more

DeepSeek-V3: Pioneering Large-Scale AI Efficiency and Open Innovation

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DeepSeek-V3: Pioneering Large-Scale AI Efficiency and Open Innovation

The field of artificial intelligence has entered a transformative phase – one defined by scale, specialization and accessibility. As the demand for larger and more capable language models grows, the challenge lies not only in achieving state-of-the-art performance but also in doing so efficiently and sustainably. DeepSeek-AI’s latest release, DeepSeek-V3 redefines what is possible at … Read more

LongCat-Video: Meituan’s Groundbreaking Step Toward Efficient Long Video Generation with AI

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LongCat-Video: Meituan’s Groundbreaking Step Toward Efficient Long Video Generation with AI

In the rapidly advancing field of generative AI, the ability to create realistic, coherent, and high-quality videos from text or images has become one of the most sought-after goals. Meituan, one of the leading technology innovators in China, has made a remarkable stride in this domain with its latest open-source model — LongCat-Video. Designed as … Read more

Agent Lightning By Microsoft: Reinforcement Learning Framework to Train Any AI Agent

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Agent Lightning By Microsoft: Reinforcement Learning Framework to Train Any AI Agent

Artificial Intelligence (AI) is rapidly moving from static models to intelligent agents capable of reasoning, adapting, and performing complex, real-world tasks. However, training these agents effectively remains a major challenge. Most frameworks today tightly couple the agent’s logic with training processes making it hard to scale or transfer across use cases. Enter Agent Lightning, a … Read more

Qwen3-VL-8B-Instruct — The Next Generation of Vision-Language Intelligence by Qwen

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Qwen3-VL-8B-Instruct — The Next Generation of Vision-Language Intelligence by Qwen

In the rapidly evolving landscape of multimodal AI, Qwen3-VL-8B-Instruct stands out as a groundbreaking leap forward. Developed by Qwen, this model represents the most advanced vision-language (VL) system in the Qwen series to date. As artificial intelligence continues to bridge the gap between text and vision, Qwen3-VL-8B-Instruct emerges as a powerful engine capable of comprehending … Read more

AgentFly: The Future of Reinforcement Learning for Intelligent Language Model Agents

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AgentFly: The Future of Reinforcement Learning for Intelligent Language Model Agents

AgentFly is a cutting-edge framework developed by researchers at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) to revolutionize how large language models (LLMs) learn and act. It combines the power of reinforcement learning (RL) with language model agents enabling them to go beyond static prompt responses and learn through real-time feedback and experience. … Read more

The Art of Scaling Reinforcement Learning Compute for LLMs: Top Insights from Meta, UT Austin & Harvard University

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The Art of Scaling Reinforcement Learning Compute for LLMs: Top Insights from Meta, UT Austin and Harvard University

As Large Language Models (LLMs) continue to redefine artificial intelligence, a new research breakthrough has emerged from Meta, The University of Texas at Austin, University College London, UC Berkeley, Harvard University and Periodic Labs. Their paper, titled “The Art of Scaling Reinforcement Learning Compute for LLMs,” introduces a transformative framework for understanding how reinforcement learning … Read more