Meta Introduces TRIBE v2: Predictive Foundation Model for Human Brain Activity
Meta AI has released TRIBE v2, a foundation model that predicts high-resolution fMRI brain activity in response to visual, auditory, and language stimuli. Trained on data from over 700 healthy volunteers, it achieves a 70x resolution increase over comparable models and supports zero-shot generalization to new subjects, languages, and tasks. The release includes model weights, codebase, a research paper, and an interactive demo under a CC BY-NC license. Meta positions the work as a bridge between neuroscience and AI development, enabling hypothesis testing without requiring human subjects in every experiment.
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Meta Introduces Muse Spark: First Model from Meta Superintelligence Labs with Multimodal Reasoning and Multi-Agent Orchestration
Meta has launched Muse Spark, the first model from its newly formed Meta Superintelligence Labs, positioned as a natively multimodal reasoning model with tool-use, visual chain-of-thought, and multi-agent orchestration capabilities. The model introduces 'Contemplating mode,' which runs multiple agents in parallel to compete with frontier reasoning modes, achieving 58% on Humanity's Last Exam and 38% on FrontierScience Research. Meta claims a greater than 10x compute efficiency improvement over Llama 4 Maverick through a rebuilt pretraining stack, and describes predictable scaling across pretraining, RL, and test-time reasoning axes. Muse Spark is available at meta.ai with a private API preview, and is framed as the first step on a scaling ladder toward 'personal superintelligence.'
Meta Introduces Muse Spark: First Closed-Weights Model from Superintelligence Labs
Meta released Muse Spark, its first AI model in roughly a year and the debut product of its Superintelligence Labs, marking a significant departure from its open-weights Llama strategy. The natively multimodal reasoning model supports tool use and multi-agent orchestration, achieves fourth place on the Artificial Analysis Intelligence Index, and claims notable token efficiency—matching Llama 4 Maverick with over 10x less training compute. Meta withheld parameter count, architecture, and training details, positioning Muse Spark as a closed commercial product competing with OpenAI, Google, and Anthropic. The release introduces 'thought compression' via RL and a parallel multi-agent 'contemplating' mode, while showing gaps in coding and agentic benchmarks.
Meta Introduces SAM Audio: Unified Multimodal Model for Audio Separation with PE-AV, Benchmark, and Judge Model
Meta has released SAM Audio, a unified multimodal audio separation model that accepts text, visual, and temporal span prompts to isolate sounds from complex audio mixtures. The system is powered by Perception Encoder Audiovisual (PE-AV), an extension of Meta's open-source Perception Encoder released earlier in 2025, and uses a flow-matching diffusion transformer architecture. Alongside the model, Meta is releasing SAM Audio-Bench (the first in-the-wild audio separation benchmark) and SAM Audio Judge (an automatic evaluation model for audio separation). All components are available today via the Segment Anything Playground.
Meta releases Llama 3.2 90B Vision-Instruct multimodal model
Meta released Llama 3.2 90B Vision-Instruct on Hugging Face, a large multimodal model supporting image-text-to-text tasks. The model is part of the Llama 3.2 family and supports English and German. With 858 downloads and 358 likes, it represents Meta's open-weights push into vision-language capabilities at the 90B parameter scale.
Meta SAM 3 (Segment Anything Model 3) Released on GitHub
Meta / Facebook Research has released SAM 3, the third generation of their Segment Anything Model, with code for inference and finetuning, pretrained model checkpoints, and example notebooks. The repository has accumulated over 10,000 stars with strong daily momentum (+93). SAM 3 continues Meta's open-weights tradition in computer vision foundation models. No accompanying paper or technical blog post is referenced in this item.
Meta releases Llama 3.2 90B Vision multimodal model on Hugging Face
Meta released Llama 3.2 90B Vision, a large multimodal model supporting image-text-to-text tasks, published on Hugging Face under the meta-llama organization. The model is part of the Llama 3.2 family and supports English, German, and French. This is a significant open-weights multimodal release from Meta, extending the Llama 3 series with vision capabilities at the 90B parameter scale.
Meta releases Llama 3.2 11B Vision multimodal model on Hugging Face
Meta released Llama 3.2 11B Vision, an open-weights image-text-to-text model, on Hugging Face. The model is part of the Llama 3.2 family and supports multiple languages including English, German, and French. This represents Meta's entry into open-weights multimodal models at the 11B parameter scale.
Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment
This paper introduces a framework for evaluating alignment between artificial vision models and the human visual cortex that goes beyond scalar prediction accuracy. Using repeated fMRI data from the Natural Scenes Dataset, the authors decompose brain response spaces into reproducible dimensions and measure which of these dimensions are recovered by model predictions. A key finding is that pretrained and randomly initialized models can achieve similar prediction accuracy while showing distinct recovery profiles, revealing that accuracy alone can mask fundamental model-brain mismatches. The framework also enables brain-to-brain comparisons as a diagnostic human reference baseline.