Our Publication-to-Blog Post Series highlights the real-world impact of our latest research, translating complex innovations into practical applications. From AI and machine learning to optical networking and intelligent systems, we showcase how our work goes beyond theory to address real-world challenges. Explore how cutting-edge research at NEC Laboratories America is driving measurable outcomes across industries.

Posts

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.

Training Small AI Models Without Blindly Trusting Big Teacher Models

Machine learning is shifting from learning from data alone to learning from both data and teacher models. Beta-KD uses uncertainty-aware Bayesian weighting to train compact multimodal AI without blindly trusting every teacher signal.

How Rule-Driven Routing Makes Retrieval-Augmented Generation Smarter

Most retrieval-augmented generation systems stop at documents, ignoring the relational databases that power finance, healthcare, and research. Our researchers built a rule-driven framework that learns which source to query for each question, delivering better answers at lower computational cost.

Rethinking Molecular Drug Design: From Generation to Control

Designing drug molecules is no longer just about generation, but control. NEC Laboratories America introduces MolDiffdAE, a diffusion-based framework that enables precise, multi-objective tuning of 3D molecular properties. By learning a semantic space, researchers can efficiently guide design, accelerating drug discovery and exploration of chemical space.

Driving the Future of Scene Editing with HorizonForge

HorizonForge introduces a new approach to driving scene generation, enabling precise control over both vehicle behavior and identity. By allowing arbitrary trajectories and flexible vehicle insertion, it creates realistic, scalable simulations for autonomous driving, digital twins, and advanced AI development.

Beyond Explainability: How We Are Redefining Interpretability in AI

AI interpretability has long been the focus, but what if it’s only part of the story? New research introduces model semantics, a framework for understanding what AI systems truly represent and how their internal structures connect to real-world phenomena.