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Robbyant Open Sources LingBot World: a Real Time World Model for Interactive Simulation and Embodied AI

Robbyant, the embodied AI unit inside Ant Group, has open sourced LingBot-World, a large scale world model that turns video generation into an interactive simulator for embodied agents, autonomous driving and games. The system is designed to render controllable environments with high visual fidelity, strong dynamics and long temporal horizons, while staying responsive enough for […]

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AI2 Releases SERA, Soft Verified Coding Agents Built with Supervised Training Only for Practical Repository Level Automation Workflows

Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first release in AI2’s Open Coding Agents series. The flagship model, SERA-32B, is built on the

AI2 Releases SERA, Soft Verified Coding Agents Built with Supervised Training Only for Practical Repository Level Automation Workflows Read More »

A Coding Implementation to Training, Optimizing, Evaluating, and Interpreting Knowledge Graph Embeddings with PyKEEN

In this tutorial, we walk through an end-to-end, advanced workflow for knowledge graph embeddings using PyKEEN, actively exploring how modern embedding models are trained, evaluated, optimized, and interpreted in practice. We start by understanding the structure of a real knowledge graph dataset, then systematically train and compare multiple embedding models, tune their hyperparameters, and analyze

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Microsoft Unveils Maia 200, An FP4 and FP8 Optimized AI Inference Accelerator for Azure Datacenters

Maia 200 is Microsoft’s new in house AI accelerator designed for inference in Azure datacenters. It targets the cost of token generation for large language models and other reasoning workloads by combining narrow precision compute, a dense on chip memory hierarchy and an Ethernet based scale up fabric. Why Microsoft built a dedicated inference chip?

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DeepSeek AI Releases DeepSeek-OCR 2 with Causal Visual Flow Encoder for Layout Aware Document Understanding

DeepSeek AI released DeepSeek-OCR 2, an open source document OCR and understanding system that restructures its vision encoder to read pages in a causal order that is closer to how humans scan complex documents. The key component is DeepEncoder V2, a language model style transformer that converts a 2D page into a 1D sequence of

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A Coding Deep Dive into Differentiable Computer Vision with Kornia Using Geometry Optimization, LoFTR Matching, and GPU Augmentations

We implement an advanced, end-to-end Kornia tutorial and demonstrate how modern, differentiable computer vision can be built entirely in PyTorch. We start by constructing GPU-accelerated, synchronized augmentation pipelines for images, masks, and keypoints, then move into differentiable geometry by optimizing a homography directly through gradient descent. We also show how learned feature matching with LoFTR

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Ant Group Releases LingBot-VLA, A Vision Language Action Foundation Model For Real World Robot Manipulation

How do you build a single vision language action model that can control many different dual arm robots in the real world? LingBot-VLA is Ant Group Robbyant’s new Vision Language Action foundation model that targets practical robot manipulation in the real world. It is trained on about 20,000 hours of teleoperated bimanual data collected from 9

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Beyond the Chatbox: Generative UI, AG-UI, and the Stack Behind Agent-Driven Interfaces

Most AI applications still showcase the model as a chat box. That interface is simple, but it hides what agents are actually doing, such as planning steps, calling tools, and updating state. Generative UI is about letting the agent drive real interface elements, for example tables, charts, forms, and progress indicators, so the experience feels

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Google DeepMind Unveils AlphaGenome: A Unified Sequence-to-Function Model Using Hybrid Transformers and U-Nets to Decode the Human Genome

Google DeepMind is expanding its biological toolkit beyond the world of protein folding. After the success of AlphaFold, the Google’s research team has introduced AlphaGenome. This is a unified deep learning model designed for sequence to function genomics. This represents a major shift in how we model the human genome. AlphaGenome does not treat DNA

Google DeepMind Unveils AlphaGenome: A Unified Sequence-to-Function Model Using Hybrid Transformers and U-Nets to Decode the Human Genome Read More »

Alibaba Introduces Qwen3-Max-Thinking, a Test Time Scaled Reasoning Model with Native Tool Use Powering Agentic Workloads

Qwen3-Max-Thinking is Alibaba’s new flagship reasoning model. It does not only scale parameters, it also changes how inference is done, with explicit control over thinking depth and built in tools for search, memory, and code execution. https://qwen.ai/blog?id=qwen3-max-thinking Model scale, data, and deployment Qwen3-Max-Thinking is a trillion-parameter MoE flagship LLM pretrained on 36T tokens and built

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