Leveraging digital twin technologies: all-photonics networks-as-a-service for Data Center Xchange in the era of AI

This tutorial paper presents a data center exchange (Data Center Xchange, DCX) architecture for all-photonics networks-as-a-service in distributed data center infrastructures, enabling the creation of a virtual large-scale data center by directly interconnecting geographically distributed data centers in metropolitan areas. In contrast to existing vendor-driven optical networking approaches, the proposed architecture adopts an operator-driven and open digital twin paradigm, leveraging cloud-native transponder architectures and open tools/interfaces such as GNPy and CMIS/TAI, and a user–carrier collaborative control framework. In particular, the cloud-native architecture enables operators to flexibly develop, deploy, and manage their own control and automation functions across transponders and controllers using container-based software components. Key requirements for such an architecture in the era of AI are identified: support for low-latency operations, scalability, reliability, and flexibility within a single network architecture; the ability to add new operator-driven automation functionalities based on an open networking approach; and the ability to control and manage remotely deployed transponders connected via access links with unknown physical parameters. We propose a set of technologies that enable digital twin operations for optical networks, including a cloud-native architecture for coherent transceivers, remote transponder control, fast end-to-end optical path provisioning, transceiver-based physical-parameter estimation incorporating digital longitudinal monitoring, and optical line system calibration, demonstrating their feasibility through field validations.

Field Trial of Urban Monitoring over Telecom Networks with Rayleigh-based DTSS

We present field trial on urban monitoring over telecom networks using Rayleigh-based distributed temperature/ strain sensor (DTSS), showing the capability of detecting subtle infrastructure signatures including manhole locations, road traffic, sprinklers and underground leakage events.

Bridging the Domain Gap in DAS: Adapting Vision Foundation Models for Infrastructure Security

Distributed Acoustic Sensing may suffer severe cross-day domain shift. We bridge this gap by encoding 1D phasesignals into physics-informed 3-channel gradient tensors andadapting Vision Foundation Models (ViT-B/16), achieving superior threat detection over baselines.

A Low-cost Wavelength Selectable Distributed Vibration Sensor with a Unified Sensitivity for the Whole Route Security Monitoring in PONs

We demonstrate a low-cost wavelength selectable distributed vibration sensor to monitor the whole routes in a 1×32 PON architecture with a unified sensitivity. The results obtained from the lab and a field testbed are presented.

NEC Labs America Attends ICML 2026 Seoul, South Korea July 6-11, 2026

NEC Laboratories America researchers are heading to Seoul this July for ICML 2026, the Forty-Third International Conference on Machine Learning. One of the most prestigious gatherings in the field, ICML draws academic and industry researchers from around the world to share work spanning machine learning, artificial intelligence, data science, and their many applications.

NEC Labs America Attends ACL 2026 San Diego July 2-7, 2026

NEC Laboratories America heads to ACL 2026 in San Diego, California, July 2–7, to present accepted papers spanning knowledge updating and memory control in large language models, task-aware cultural alignment, uncertainty-aware reasoning, and adaptive chain-of-thought optimization, representing some of the most active frontiers in NLP and AI research today.

How AI Can Transform the Way Companies Buy What They Need

Procurement teams lose time and money to inaccurate demand forecasts and manual supplier negotiations. A new framework from NEC Corporation and NEC Laboratories America combines automated negotiation with multimodal AI forecasting to optimize both sides of the procurement process.

NEC Labs America Attends OECC June 28 – July 2, 2026

NEC Laboratories America is proud to participate in OECC 2026, the 31st Opto-Electronics and Communications Conference, taking place in Busan, South Korea. We look forward to connecting with the international photonics and communications community and sharing the work we’re doing to shape the next generation of optical networks.

Open SAT: How We Taught AI to Search Satellite Images Like a Search Engine

Satellite imagery is vast, high-resolution, and rich with information, but finding specific objects within it using natural language has remained a stubborn challenge. Open-SAT, developed by researchers at NEC Laboratories America and North South University, tackles this problem without retraining any models.

Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models

Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty. This motivates the need for adaptively balancing data and teacher supervision. We propose Beta-weighted Knowledge Distillation (Beta-KD), an uncertainty-aware distillation framework that adaptively modulates how much the student relies on teacher guidance. Specifically, we formulate teacher-student learning from a unified Bayesian perspective and interpret teacher supervision as a Gibbs prior over student activations. This yields a closed-form, uncertainty-aware weighting mechanism and supports arbitrary distillation objectives and their combinations. Extensive experiments are conducted on multimodal VQA benchmarks by distilling a student Vision-Language Model from a large teacher VLM. The results demonstrate that Beta-KD consistently outperforms existing knowledge distillation methods. Code is available at https://github.com/Jingchensun/beta-kd.