Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data EfficientlyTowards Data Science The real value lies in writing clearer code and using your tools right
The post Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently appeared first on Towards Data Science.
The real value lies in writing clearer code and using your tools right
The post Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently appeared first on Towards Data Science. Read More
Evaluate generative AI models with an Amazon Nova rubric-based LLM judge on Amazon SageMaker AI (Part 2)Artificial Intelligence In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs.
In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs. Read More
Scientists create smart synthetic skin that can hide images and change shapeArtificial Intelligence News — ScienceDaily Inspired by the shape-shifting skin of octopuses, Penn State researchers developed a smart hydrogel that can change appearance, texture, and shape on command. The material is programmed using a special printing technique that embeds digital instructions directly into the skin. Images and information can remain invisible until triggered by heat, liquids, or stretching.
Inspired by the shape-shifting skin of octopuses, Penn State researchers developed a smart hydrogel that can change appearance, texture, and shape on command. The material is programmed using a special printing technique that embeds digital instructions directly into the skin. Images and information can remain invisible until triggered by heat, liquids, or stretching. Read More
Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3MarkTechPost 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
The post Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3 appeared first on MarkTechPost.
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
The post Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3 appeared first on MarkTechPost. Read More
Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDKArtificial Intelligence In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices.
In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices. Read More
A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipyMarkTechPost In this tutorial, we build an end-to-end cognitive complexity analysis workflow using complexipy. We start by measuring complexity directly from raw code strings, then scale the same analysis to individual files and an entire project directory. Along the way, we generate machine-readable reports, normalize them into structured DataFrames, and visualize complexity distributions to understand how
The post A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipy appeared first on MarkTechPost.
In this tutorial, we build an end-to-end cognitive complexity analysis workflow using complexipy. We start by measuring complexity directly from raw code strings, then scale the same analysis to individual files and an entire project directory. Along the way, we generate machine-readable reports, normalize them into structured DataFrames, and visualize complexity distributions to understand how
The post A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipy appeared first on MarkTechPost. Read More
NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scaleMarkTechPost 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
The post NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scale appeared first on MarkTechPost.
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
The post NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scale appeared first on MarkTechPost. Read More
Structured outputs on Amazon Bedrock: Schema-compliant AI responsesArtificial Intelligence Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples.
Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples. Read More
SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Modelscs.AI updates on arXiv.org arXiv:2602.04208v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on ‘self-uncertainty’, inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.
arXiv:2602.04208v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on ‘self-uncertainty’, inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency. Read More
SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonizationcs.AI updates on arXiv.org arXiv:2602.04271v1 Announce Type: cross
Abstract: 4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/
arXiv:2602.04271v1 Announce Type: cross
Abstract: 4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/ Read More