Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers. Also, don’t forget to register for NVIDIA GTC 2026 event (In person/Virtual). NVIDIA has been supporting us to bring free and unlocked AI research and dev news content to you.
Meet OAT: The New Action Tokenizer Bringing LLM-Style Scaling and Flexible, Anytime Inference to the Robotics World
Ordered Action Tokenization (OAT), developed by researchers at Harvard and Stanford, is a new framework that enables robots to learn and move using the same autoregressive methods as large language models. Traditional robot tokenizers were often too slow, lacked structure, or caused system crashes due to "undecodable" math. OAT solves these issues by satisfying three "desiderata": high compression, total decodability, and a left-to-right causal ordering. Using a technique called Nested Dropout, OAT forces the most important global movements into the first few tokens, while later tokens add fine-grained details. This unique "ordered" structure allows for anytime inference, where a robot can stop generating tokens early to react quickly or continue for higher precision. Across more than 20 tasks, OAT consistently outperformed industry-standard diffusion policies and other tokenization methods, offering a more scalable and flexible foundation for future robotic control..… Read the full analysis/article here.
Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links..
OrbitalBrain is a distributed machine learning framework from Microsoft Research that moves EO model training from ground data centers into satellite constellations. Instead of the traditional BentPipe design that can only downlink about 11.7% of 300 MB images per day, OrbitalBrain uses onboard compute, inter-satellite links, and a predictive scheduler to coordinate local training, model aggregation, and raw data transfer under strict power, bandwidth, and storage constraints. A guided profiler tracks model staleness and label imbalance using utilities based on loss and Jensen–Shannon divergence, allowing the system to decide when to compute, aggregate, or rebalance data. In simulations on Planet and Spire constellations with fMoW and So2Sat, OrbitalBrain achieves 1.52×–12.4× faster time-to-accuracy and 5.5%–49.5% higher final accuracy than BentPipe and federated learning baselines.… Read the full analysis/article here.
Latest Releases in Last 72 Hours
Project Notebooks/Tutorials
▶ A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow Codes Tutorial
▶ Meet CopilotKit: Framework for building agent-native applications with Generative UI, shared state, and human-in-the-loop workflows Codes
▶ How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning Codes Tutorial
▶ Build an Autonomous Wet-Lab Protocol Planner and Validator Using Salesforce CodeGen for Agentic Experiment Design and Safety Optimization Codes Tutorial
▶ [Open Source] Rogue: An Open-Source AI Agent Evaluator worth trying Codes & Examples