University of Tokyo, NTT, and NEC Demonstrate Real-Time AR Assistance on 6G/IOWN Platform

Mar 6, 2026
The University of Tokyo, NTT, and NEC have demonstrated real-time augmented reality assistance by integrating three new technologies on a 6G/IOWN platform, optimizing data transmission and computation for AI agents.

The University of Tokyo, NTT, and NEC announced in a press release that they successfully demonstrated real-time augmented reality (AR) assistance using a 6G/IOWN platform. The trial integrated three new technologies aimed at improving data transmission and computational efficiency for AI agents supporting safety and security applications.

The collaboration combined streaming semantic communication, AI-oriented media control, and In-Network Computing (INC) architecture. Together, these technologies reduced communication traffic and computational load while maintaining stable end-to-end latency and AI inference accuracy during continuous AR monitoring scenarios.

The demonstration used a 60-second video dataset to test how AI agents could process input from AR glasses in real time. Results showed that the system maintained consistent latency without performance degradation, addressing challenges in high-bandwidth data transmission and real-time AI processing.

The joint research will be exhibited at the Japan Pavilion during Mobile World Congress 2026, where the partners plan to present their findings and continue development toward practical implementation of AI agents for safety and security use cases.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief, Silicon Brief or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Chips & Datacenters AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.