When Video AI Gets Physics Wrong, the Consequences Are Real

Video generation models can look physically convincing while getting the physics completely wrong. PhyCo, new research from our Media Analytics department, introduces continuous, controllable physical properties to video AI, allowing practitioners to specify friction, bounce, and force.

Mix-CLAP: Teaching Audio AI to Work in the Noisy Real World

Mix-CLAP from NEC Laboratories America delivers near-Transformer accuracy for sound event classification at a fraction of the compute cost, using dual lightweight encoders and adaptive, noise-aware inference for real-world edge deployment.

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.

NEC Labs America Attends CVPR 2026 in Denver, CO June 3-7, 2026

NEC Labs America headed to Denver for CVPR 2026, one of the most prestigious gatherings in computer vision, machine learning, and pattern recognition. The IEEE/CVF Conference on Computer Vision and Pattern Recognition brought innovators from around the world to share breakthroughs.

Automated Negotiation and Multimodal Time-Series Forecasting for Efficient Procurement

Procurement is a key function in supply chain management that involves acquiring goods and services to meet organizational needs. Efficient procurement is crucial for minimizing costs, ensuring timely delivery, and maintaining quality standards. This paper explores the integration of automated negotiation and multimodal time-series forecasting to enhance procurement processes. Automated negotiation can streamline interactions with suppliers, while multimodal time-series forecasting can improve demand prediction accuracy by leveraging diverse data sources leading to better negotiation outputs. By combining these approaches, organizations can optimize procurement strategies, reduce costs, and improve overall supply chain efficiency. We present two case studies using simulations based on real-world data for procurement that show the effectiveness of the proposed framework.

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.