AI RESEARCH

Our latest AI advancements

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Introducing Muse Spark

Introducing
Muse Spark

Muse Spark is the first LLM from Meta Superintelligence Labs — small and fast by design, but capable enough to reason through complex questions in science, math and health.
RESOURCES
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Safety and Preparedness Report

How Muse Spark performs across safety, security, and reliability—and the decisions behind its deployment.
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Introducing Muse Spark

Read more on the new model innovations and our path to personal superintelligence.
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Get Model Updates

Sign up to get updates on access to our new and upcoming Muse Spark models.
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PERCEPTION

Segment Anything 3

Our most advanced segmentation model, now with 3D, audio and more capabilities

With SAM 3, you can use text and visual prompts to precisely detect, segment and track any object in an image or video.

SAM 3D
SAM 3D enables precise reconstruction and analysis of 3D people and objects, providing new opportunities for spatial understanding and applications.
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SAM Audio
A more conversational Meta Al means you can ask anything, anytime and get helpful answers wherever you are.
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CORE LEARNING & REASONING

DINOv3

Our most powerful self-supervised algorithm and vision foundation model

DINOv3 scales self-supervised learning to train a powerful, more versatile model

More from Meta's FAIR Team

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Meta Motivo

A first-of-its-kind behavioral foundation model for embodied humanoid virtual agents.
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Video Seal

A state-of-the-art, open-source model for video watermarking.
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Movie Gen

The most advanced family of media foundation AI models empowering immersive storytelling.
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Seamless Communication

AI research models that enable more natural, authentic communication across languages.
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AI Chemistry

Building AI systems solving most important chemistry and material problems for Meta and the world.
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SAFETY

Alignment and preparedness

As we chart the path towards superintelligence, AI tools can deeply understand your world and help you get things done faster. With that, reliability, security and user protections are more important than ever.
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This framework sets the standards and safeguards we hold our most capable models to — covering how we identify, assess and address the most severe risks before deployment. It's built around comprehensive safeguards, rigorous testing, ongoing research and a level of transparency that goes further than industry norms.

The framework is focused on evaluating the most severe areas of potential risks for advanced models including cybersecurity threats, chemical and biological security and safeguarding against loss of control. As we build more agentic AI experiences, we've expanded assessments to evaluate how AI performs with greater autonomy and whether controls around that behavior work as intended.
Core priorities include: comprehensive safeguards, rigorous testing, ongoing research to identify new risks and mitigation techniques.

We stress-test across a wide range of scenarios using automated and human-led evaluations, including adversarial probing. Models are evaluated before and after mitigations are applied to confirm safeguards work in real-world conditions — and only deployed when they meet our framework thresholds.

We've moved beyond rules-based systems to teach models why something is safe — not just what is allowed. By translating guidelines across child safety, response quality and handling different viewpoints into testable principles, models can reason through situations rigid rules might never have anticipated.

A document covering how each frontier model performs across safety, security, accuracy, helpfulness and user protections. We'll publish one for every frontier model — including risk assessments, evaluation results, methodology, known limitations and deployment reasoning.

Try experimental demos

How Meta is applying cutting-edge AI research to real-world interactions.

A state-of-the-art, open-source model for video watermarking
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Create video cutouts and effects with a few clicks

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OUR RESEARCH

For researchers and developers

Meta FAIR is advancing research and delivering breakthroughs in a variety of areas.
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RESEARCH AREAS

The north star goal of our Perception research teams is to enable general AI systems to perceive the visual world to inform action, communication and generation. To achieve this goal, we're developing next generation perception models capable of understanding images and videos not as pixels, but as a capture of visual entities like people, objects, activities and their spatial and temporal relationships.

We advance AI capabilities in expressive communication, social interaction and use of language. Through foundational research in natural language processing and multimodal AI, we develop systems that enable more natural, meaningful interactions between humans and machines.

We advance the fundamental capabilities needed for AI to understand and act within the physical and digital world. From robots that can move around and interact with objects, to helping accomplish household tasks, to wearable glasses that understand the real and digital world, we hope to unlock a wide variety of future agents that help humans do more throughout all aspects of their lives.

Our research focuses on aligning models and decisions with human intent and societal interests through deeper fundamental understanding and enhanced steerability and efficiency of AI models. The pillar is at the forefront of research on AI for science and AI for society.

We conduct fundamental research in pre-training methods and new architectural paradigms that enable foundational models to learn and reason with agility and efficiency across novel downstream challenges. Our work expands the frontier of approaches such as world models, non-autoregressive architectures and memory-augmented models to unlock new capabilities in adaptive intelligence.

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