For years, the computer vision community has operated on two separate tracks: generative models (which produce images) and discriminative models (which understand them). The assumption was straightforward — models good at making pictures aren’t necessarily good at reading them. A new paper from Google, titled “Image Generators are Generalist Vision Learners” (arXiv:2604.20329), published April 22,…

The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the Qwen3.6 generation, and it is making a compelling argument that parameter efficiency matters far more than raw model size. With 35 billion total parameters but only 3 billion activated…

class MolmoActVisualizer: """Visualization utilities for MolmoAct outputs""" def __init__(self, figsize: Tuple[int, int] = (12, 8)): self.figsize = figsize self.colors = plt.cm.viridis(np.linspace(0, 1, 10)) def plot_trace( self, …

Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means…

In this tutorial, we build and run an advanced pipeline for Netflix’s VOID model. We set up the environment, install all required dependencies, clone the repository, download the official base model and VOID checkpoint, and prepare the sample inputs needed for video object removal. We also make the workflow more practical by allowing secure terminal-style…

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then progressively transform it using UDFs, feature engineering, aggregations, joins, and lazy execution. Also, we demonstrate how to seamlessly combine structured data processing, numerical computation, and…

Frontier multimodal models usually process an image in a single pass. If they miss a serial number on a chip or a small symbol on a building plan, they often guess. Google’s new Agentic Vision capability in Gemini 3 Flash changes this by turning image understanding into an active, tool using loop grounded in visual…

import subprocess, sys, os, json, hashlib def pip(cmd): subprocess.check_call([sys.executable, "-m", "pip"] + cmd) pip(["uninstall", "-y", "pillow", "PIL", "torchaudio", "colpali-engine"]) pip(["install", "-q", "--upgrade", "pip"]) pip(["install", "-q", "pillow<12", "torchaudio==2.8.0"]) pip(["install", "-q", "colpali-engine", "pypdfium2", "matplotlib", "tqdm", "requests"]) Source link

Waymo is introducing the Waymo World Model, a frontier generative model that drives its next generation of autonomous driving simulation. The system is built on top of Genie 3, Google DeepMind’s general-purpose world model, and adapts it to produce photorealistic, controllable, multi-sensor driving scenes at scale. Waymo already reports nearly 200 million fully autonomous miles…

How do you combine SigLIP2, DINOv3, and SAM3 into a single vision backbone without sacrificing dense or segmentation performance? NVIDIA’s C-RADIOv4 is a new agglomerative vision backbone that distills three strong teacher models, SigLIP2-g-384, DINOv3-7B, and SAM3, into a single student encoder. It extends the AM-RADIO and RADIOv2.5 line, keeping similar computational cost while improving…