#Robotics

iFlytek launched the Xingchen TaaS platform for the first time, making Tokens truly productive for thousands of industries.

iFlytek released the Xingchen TaaS platform, which transforms Token consumption into business value such as simultaneous interpretation translation and digital employees through intermediate capabilities like model scheduling and intelligent agent engines. It provides solutions for industries such as enterprise services, finance, and healthcare, ensuring that Tokens are not wasted. The Spark X2.6 edge model is open-sourced, with an expanded context window to 1 million Tokens and an edge-cloud routing accuracy of 96.1%.

Using an AI agent to let Microduck learn to walk in a simulated environment.

NVIDIA is developing an AI agent to teach Microduck to walk in a simulated environment. The model navigates obstacle courses by perceiving the environment, maintaining balance, and adjusting actions. This technology combines stages of asset creation, physics simulation, and training evaluation.

Who should own the keys when writing code through an agent?

The amount and speed of code generated by the agent make manual review a bottleneck; the team needs to use tools as a substitute; the agent is only responsible for writing code without deployment permissions, deployment is controlled by pipelines; all data read by the agent is considered untrusted, the pipeline must ensure security through hash verification and short-term identity, and continuously monitor permission changes.

MiHoYo, in collaboration with Tsinghua and Peking Universities, has released the open-source GameScaling game model, covering parameter scales from 0.8B to 27B.

MiHoYo, in collaboration with Tsinghua and Peking Universities, has released the open-source GameScaling game model, covering parameter scales from 0.8B to 27B. This series of 8 models is based on the Qwen3.5 visual language model for continued pre-training, with training data involving games such as Minecraft and Doom. The model generates subsequent instructions based on game screens and operation records, supporting real-time game interaction. There are two versions for each of the 4 parameter scales: S1 and S3, and they are licensed under Apache 2.0.

The Adaptive Intelligent Agent Framework using Strands Agents

The Adaptive Agentic Intelligence Framework (AAIF) uses Strands Agents to process routine tasks with local small language models (SLMs), and routes complex requests to Amazon Bedrock cloud models, reducing costs while maintaining low latency.

NVIDIA released SimReady Central on Hugging Face, opening a library of over 2000 simulation-ready 3D assets.

NVIDIA launched SimReady Central Library on Hugging Face, aggregating over 2000 OpenUSD assets ready for direct simulation, covering robotic arms, grippers, warehousing equipment, racks, and cooling facilities, free to use without application.

NVIDIA European partners expand computing capabilities, building sovereign, open, and secure AI agent cloud ecosystems.

NVIDIA European partners are expanding regional computing capabilities to support model training and inference, helping developers build agentic and physical AI applications. Nscale achieves 37% more GPUs and 50% higher inference throughput using the NVIDIA DSX platform, Crusoe develops a liquid-cooled AI factory for low-latency inference, and Nebius adopts the NVIDIA Groq 3 LPX to accelerate sensitive applications.

NVIDIA has released the Cosmos3-H-Dreams surgical generative simulation model.

NVIDIA has released Cosmos3-H-Dreams, extending interactive generative simulation to clinical gallbladder removal scenarios, capable of generating surgical scenes in real-time based on instrument movements.

From Standalone Coding Tools to Development Platforms: How Trust Affects the Adoption of AI Code Assistants

Engineering teams' trust in AI code assistants determines their scope of use, ranging from viewing code differences to full authorization for merging. Trust is built through rapid validation mechanisms. Development platform costs increase with the intensity of AI agent work, with about 80% of the budget consumed by 20% of heavy users, requiring an evaluation of the effective cost per unit of work. Successful teams start with auxiliary tasks and gradually expand the scope of AI, rather than applying it directly to core systems.

ContextCue project explores how AI can improve collaboration and context understanding in distributed teams

NVIDIA RTX Spark laptops are officially launched, featuring Blackwell RTX GPUs and an AI platform.

NVIDIA RTX Spark laptops are now available, equipped with Blackwell RTX GPUs, up to 128GB unified memory, and the full NVIDIA AI platform, starting at $2399. They are launched by ASUS, Dell, HP, Lenovo, Microsoft, and MSI, bringing powerful local AI, content creation, and gaming capabilities to portable devices. NVIDIA held global launch events, including one in Berlin, where founder Jensen Huang made a surprise appearance.

Reflection announces the Beam large model: Designed specifically for coding and agent workloads.

Meta How to Build Forum Applications with Native AI Engineering

Meta uses AI agents to participate in the construction of engineering processes for Forum applications, viewing AI as a participant in the engineering process rather than an auxiliary tool. This experiment measures success by production-ready outcomes rather than lines of code, using a Human-On-The-Loop model to allow AI to autonomously execute planning, implementation, testing, and validation, with engineers responsible for setting goals, supervision, and acceptance.

Nordics and NVIDIA demonstrate how their commitment to advanced computing in the region is driving technological development

The Nordic region has become the area with the highest density of European unicorns, with the most valuable startups worth up to $1 billion being born per million people. NVIDIA has invested in the Nordic tech ecosystem over the past year, including leading startups such as Lovable and Legora in Sweden.

NVIDIA bets on physical AI to ensure the safety of autonomous driving and bionic robots

NVIDIA's physical AI full-stack security solutions have been adopted by robotics companies, aimed at enhancing the safety of autonomous driving and bionic robots.

NVIDIA and Microsoft team up to usher in a new era of AI agents for Windows PCs: RTX Spark and local AI calculator

NVIDIA and Microsoft collaborate to launch RTX Spark laptops and Windows AI agent infrastructure, supporting local running of 125B parameter Qwen 3.8 Flash Next models; Microsoft releases MXC operating system-level container to ensure agent secure operation, pre-orders for RTX Spark open today, paired with Grace Blackwell chip, offering up to 20 PFLOPS inference performance.

MetaInfer skill toolbox: An inference engine optimizer built solely with knowledge achieves 50-90% faster engine performance.

MetaInfer's skill toolbox constructs custom inference engines using only knowledge, making the Qwen-3.6-35B-A3B model 90% faster than vLLM 0.25.1 in single-stream decoding speed, and TTFT improves 2.33 times. This framework does not require model fine-tuning or dedicated adapters, enabling LLMs to achieve highly optimized inference engines for specific deployments by defining constraints and optimization goals.

Anthropic Research: What Work Can Robots Currently Complete?

Anthropic has released a Robot Exposure Index study, using Claude to evaluate about 19,000 tasks in O*NET, finding that existing robots can complete 74% of physical tasks in the U.S. and account for 34% of all work time, but most are limited to controlled environments.

Microsoft Research explores the advantages of offloading physical AI reasoning to edge or cloud GPUs.

Offloading physical AI reasoning to edge or cloud GPUs can significantly improve robot task success rates, support larger models, and extend battery life. In mobile operation tasks, offloading reasoning enhances robot response speed and accuracy, increases battery endurance, and reduces costs. Microsoft Research has developed a Kubernetes-based toolkit to support distributed reasoning deployment on robots, edge devices, and the cloud.

Meta AI's DINOv3 and SAM models help the University of Pittsburgh reshape assistive robots

Meta AI's DINOv3 and SAM models achieve real-time perception in edge computing, supporting the University of Pittsburgh's RAMMP project development of assistive robots, interacting through natural language and image data to reduce user cognitive load, already integrated in prototypes with automatic door button detection, cup recognition, and navigation assistance features, and plans to add voice and touch input support.

True Agents Model the World pre-training research progress

True Agents Model the World pre-training methods enable LLMs to simulate environments with high precision. In the forth-lang environment, the combination of RLHF and SFT makes model tool calls smarter. With alpha=0.05, the lookup_doc usage significantly increases but drops early, while with alpha=1.0, the reward improvement is more stable.

Open-source large language models have crossed a key threshold.

Open-source large language models GLM-5 and MiniMax M2.7 perform similarly to proprietary cutting-edge models (such as Claude Opus 4.6, GPT-5.4) in core intelligent agent tasks like file operations, tool usage, and instruction following, with reliable tool calls and consistent instruction adherence.

Dify Team's European Journey: 5 Countries, 4 Stories, and Outlook for 2026

The Dify team will visit Europe by the end of 2025, exchanging with partners in London, Berlin, Oslo, Lisbon, and Paris, observing different scenarios of Dify's deployment in local servers in German historical towns, building new tools on individual developers' computers, and scaling applications in complex global enterprise infrastructure.

DeepSeek releases SYNTHETIC-2 open-source reasoning dataset and planetary-scale pipeline parallel reasoning system.

DeepSeek releases SYNTHETIC-2, which includes 20 complex reinforcement learning tasks and verification reasoning trajectories, supporting heterogeneous computing from consumer-grade GPUs to super-large-scale clusters, using the DeepSeek-R1-0528 model to generate data and label difficulty.