01
Trustworthy & Grounded Intelligence
Improving visual grounding, OCR understanding, hallucination reduction, and reliable model evaluation so AI decisions are better supported by real-world evidence.
PhyNexa Labs · Physical Intelligence
φ-N, Physical AI, Everywhere.
Grounded. Efficient. Self-Learning.
PhyNexa Labs develops trustworthy and efficient AI that understands the physical world. We improve multimodal grounding, reduce hallucinations, strengthen visual reasoning, and make advanced intelligence accessible across computing platforms—from GPUs to CPUs and edge devices.
We turn these advances into practical solutions for industrial inspection, compact vision-language reasoning, and efficient model optimization, while exploring new architectures for predictive, self-learning physical intelligence.
02
Efficient Models & Practical Solutions
Developing compact multimodal models, lightweight industrial inspection methods, and efficient post-training optimization to bring capable AI to applications with limited computing resources.
03
Predictive & Self-Learning Physical AI
Exploring new architectures for 3D perception, sensor fusion, spatiotemporal reasoning, and self-learning representations—toward physical intelligence that understands and predicts how the world evolves.
PERCEPTION/PREDICTION/EFFICIENCY/PHYSICAL INTELLIGENCE
01 / PRODUCTS & DEMOS
Built for practical impact.
Turning advances in grounded perception and efficient AI into practical tools for industrial inspection, multimodal reasoning, and model development.
01
Industrial Visual Inspection
Lightweight anomaly detection and reliable visual classification for industrial quality inspection, with alternatives to computationally expensive models.
02
Compact Multimodal Intelligence
Efficient vision-language models that combine classification and reasoning for specialized applications and resource-constrained environments.
03
Efficient Model Development
Automated evaluation, checkpoint selection, and numerical optimization to improve model reliability while reducing development and post-training costs.
Compact VLM Classification & Reasoning
Large vision-language models can be computationally expensive to deploy, particularly for specialized visual understanding tasks that require both accurate classification and interpretable reasoning. We develop compact VLM architectures with task-specific classification and reasoning capabilities, combining efficient multimodal representations with specialized post-training strategies. In our evaluation, an 8B model achieved an F1 score of 0.863, outperforming a larger 31B model (0.834) on 1,500 held-out expert-labeled examples.
Click a slide to enlarge.
Lightweight Adaptive Anomaly Detection
Industrial defect detection requires reliable inspection with minimal computational overhead. We combine normalized cross-correlation (NCC) template matching with statistical KDE and image-adaptive fusion to provide a lightweight alternative to deep neural networks. On ceramic debris detection, our method achieved 95.2% F1, compared with 99.3% for ResNet-50, demonstrating competitive accuracy without neural-network training.
Click a slide to enlarge.
Automated Model Evaluation & Checkpoint Selection
Selecting reliable model checkpoints from extensive post-training experiments is costly and difficult when evaluation depends on inconsistent or expensive manual judgments. We develop an automated evaluation framework that progressively combines pointwise scoring, listwise ranking, and pairwise comparison to assess model quality and identify promising checkpoints. The framework supports systematic, repeatable model selection and reduces the need for exhaustive manual comparisons.
Click a slide to enlarge.
Newton-Based Optimization
Hyperparameter optimization and post-training can require many expensive evaluations, slowing model development and increasing computational costs. We explore Newton-inspired numerical optimization methods that use local objective information to guide efficient parameter updates and search. In a reported XGBoost experiment, the approach required 8 optimization iterations versus 320 grid-search evaluations to reach competitive performance. The framework also targets efficient optimization in VLM post-training and classical machine learning.
Click a slide to enlarge.
02 / OUR TECHNOLOGY
Grounded intelligence for the physical world.
Advancing the methods behind reliable multimodal understanding, efficient 3D perception, and predictive modeling of real-world systems.
01
Reliable Multimodal Grounding
Improving visual reasoning, multilingual OCR understanding, and hallucination reduction through grounding-aware training and evaluation.
02
Efficient 3D Physical Perception
Exploring cross-plane volumetric representations that capture three-dimensional information while reducing the computational demands of conventional 3D processing.
03
Sensor Fusion & Physical Prediction
Integrating heterogeneous measurements with spatial and temporal modeling to estimate and predict physical properties in real-world environments.
Multimodal Grounding & Hallucination Reduction
Vision-language models can generate plausible descriptions that are not supported by visual evidence. We investigate grounding-aware supervised fine-tuning and preference optimization to improve factual reliability and reduce hallucinations. Our research focuses on aligning model responses with observable image content, supported by curated training data, evaluation benchmarks, and experimental results.
Click a slide to enlarge.
OCR-Aware Visual Understanding
Reading text in real-world images is challenging due to blur, occlusion, perspective distortion, and multilingual content. We develop OCR-aware multimodal post-training methods to improve text extraction and grounded visual reasoning. In our experiments, information completeness improved from 71.3 to 84.6, while hallucination decreased from 18.3% to 5.5%, using the same frozen visual encoder.
Click a slide to enlarge.
DVNet: Efficient 3D Physical Perception
Processing volumetric images with conventional 3D neural networks can be computationally expensive. We investigate Distributive Volumetric Networks (DVNet), which integrate features across orthogonal spatial planes to capture three-dimensional information efficiently. An initial implementation demonstrates cross-plane feature mapping, providing a foundation for future 3D X-ray classification and object detection.
Click a slide to enlarge.
Sensor Fusion & Spatiotemporal Prediction
Physical-world measurements are often sparse, noisy, and distributed across multiple sensors, locations, and time periods. We investigate sensor fusion, spatial statistical modeling, and temporal data analysis to reconstruct and predict physical properties. Prior applications include intelligent road construction, where heterogeneous measurements support spatial prediction and data-driven understanding of material and infrastructure conditions.
Click a slide to enlarge.
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.
Predictive representation learning
Momentum-Guided Semantic Forecasting (MoFore)
Self-supervised video representation learning through forecasting in latent space.
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
Self-Learning & Predictive Intelligence
04 / CONTACT
Let's build what's next.
Interested in product service, research collaboration, technology partnerships, or learning more about PhyNexa Labs?