Predictive representation learning
Momentum-Guided Semantic Forecasting (MoFore)
Self-supervised video representation learning through forecasting in latent space.
03 / RESEARCH
Exploring the next frontiers of Physical AI.
Building on research in numerical optimization, efficient learning, and physical-world modeling to explore AI systems that can learn, reason, and predict with more accessible computing resources.
01
Efficient Learning Architectures
Investigating improvements to vision transformers and parameter-efficient adaptation, including ViT loss optimization and Gated LoRA.
02
Self-Learning & Predictive Intelligence
Exploring representations that learn from visual and physical dynamics, moving beyond static recognition toward predictive understanding.
03
Accessible Physical Intelligence
Pursuing architectures and learning methods that reduce dependence on large-scale computation while advancing toward more general physical-world understanding.
Selected foundational research related to PhyNexa's technical directions. Publications are attributed to their original authors and institutions; they are not presented as company-owned intellectual property.
Grounded multimodal AI
Reducing Hallucination in Multimodal Large Language Models through Hard Grounding Preference Supervision
Research on improving grounded and reliable vision-language reasoning.
Efficient optimization
Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers
Algorithmic approaches to efficient and accurate decision systems.
Forthcoming
ViT Loss Optimization
Forthcoming
Gated LoRA
Forthcoming