Articles & Guides

Jensen Huang’s two-day visit to Tokyo tied together Japan’s compute ambitions, robotics sector, auto industry, and chip supply chain. Nvidia is positioning itself not just as a GPU vendor, but as the infrastructure layer for Japan’s physical AI push.

Apple’s trade secrets suit against OpenAI adds a new layer of uncertainty to the AI company’s hardware push and potential public-market plans. Even before a court weighs the merits, the case could affect timelines, investor messaging, and trust.

Current AI is not chasing the biggest model. It is trying to build public AI infrastructure that communities can access, inspect, and shape—starting with languages that mainstream systems often ignore.

Qiymor used WAIC 2026 to showcase a full-stack interconnect plan for domestic supernodes, an IBGDA demo linking a domestic GPU to a domestic RDMA NIC, and its roadmap toward optical interconnects. The message is clear: China’s AI stack is moving from chip-level progress to system-level integration.

At WAIC 2026, Arm China presented edge AI as a systems challenge rather than a raw-compute race. Its roadmap spans embedded CPUs, configurable NPUs, machine-oriented video processing and an open AIOS initiative.

Intellifusion outlined a new inference-chip roadmap built around three DeepVerse processors optimized for Prefill, Decode and Decode FFN workloads. The strategy points to a broader industry shift toward heterogeneous clusters and lower token-generation costs.

Riemann Dynamics has introduced Riemann-1.0, a robot manipulation model that reached a 62.6% success rate on RoboCasa-365. The more important story is its training recipe: large-scale human egocentric video pretraining followed by robot action alignment.

Moonshot AI’s latest Kimi release has reignited debate over Chinese open-weight models, distillation, and national security. The argument is less about one benchmark result than about who controls the future AI stack.

Kimi K3 arrived quickly after launch and was added to the model marketplace on the same day. What stands out most is not only its size, but its breadth across coding, long-horizon tasks, search, tables, and front-end work.

The US government is testing AI-assisted prior authorization in Medicare, arguing it could reduce waste and inappropriate spending. Critics warn that the same tools could make denials faster, less transparent, and harder for patients to challenge.

Recent AWS incidents show how leaked AI credentials or overpowered agents can generate large bills before budget alerts catch up. The gap between real-time API consumption and delayed billing data is becoming a security problem.

Token Time Continuous Diffusion introduces a continuous-space diffusion approach to language modeling, with different tokens progressing from noise to text at different rates. The method targets better high-speed generation, especially for conditional tasks.

RoboTTT brings test-time training into robot foundation models, enabling policies to condition on up to 8K visuomotor timesteps without increasing inference latency.

VIABench is a video benchmark built from first-person footage recorded or shared by blind and visually impaired individuals. It evaluates whether multimodal large language models can provide practical assistance in navigation, question answering, and guided interaction.

DeepLoop tackles a subtle issue in looped Transformer architectures: reusing the same physical blocks changes how residual updates behave. The paper proposes scaling rules that account for repeated parameter visits rather than only nominal layer depth.

GRASP addresses a central challenge in Agentic RAG: deciding when to retrieve, which retrieval tool to use, and how much context to read. The framework uses reinforcement learning to coordinate semantic search, keyword search, and paragraph reading during multi-step reasoning.

LongStraw targets the gap between million-token inference and much shorter RL post-training. By detaching shared prompt processing and replaying response branches, it makes ultra-long-context RL execution more memory-efficient.

UniVR aims to train models to reason, model physical dynamics, and plan over long horizons directly from visual demonstrations. Its key components are the VR-GRPO reinforcement learning scheme and the VR-X benchmark for purely visual evaluation.

SearchOS-V1 targets a common failure mode in long-horizon web-search agents: losing track of progress and repeating unproductive searches. It introduces an explicit state layer for evidence, coverage, tasks, and failures across collaborating agents.

A new paper proposes storing verified knowledge as byte-exact KV-cache artifacts and grafting them back into a frozen small language model. The result is striking efficiency on repeated cases, but the claims also raise questions about reproducibility and what counts as improved reasoning.

SEED introduces an on-policy distillation framework for agentic reinforcement learning, converting completed trajectories into natural-language hindsight skills and distilling their behavioral effects back into the policy.