Research Direction
"We aim to build intelligent and versatile systems that address high-impact societal needs across language boundaries, multimodal contexts, and specialized domains."
Omni Language AI Research is a dedicated research collective operating at the intersection of deep learning, linguistics, and multimodal intelligence. By blending rigorous academic methodology with modern engineering, we create systems that don't just process text but comprehend context, reason over complex modalities, and adapt dynamically to new domains.
Multilingual NLP & Reasoning
Large Language Models (LLMs) have demonstrated impressive capabilities, but scaling them globally requires addressing representation, alignment, and learning efficiency. We focus on bridging these challenges through:
- Massively Multilingual NLP: Scaling and adapting systems to support a few hundred languages, ensuring equitable access to state-of-the-art AI for underrepresented communities.
- Continual Learning: Investigating training paradigms and architectures that enable models to continuously learn new languages and tasks over time without catastrophic forgetting.
- Reinforcement Learning: Applying reinforcement learning techniques to align models across diverse languages and optimize translation and reasoning performance.
- Test-Time Scaling: Investigating how allocating additional compute during inference (e.g., via search and verification traces) affects translation quality and reasoning performance across diverse languages.
Clinical Intelligence & Health AI
AI has the potential to transform healthcare, but clinical environments demand extreme robustness, explainability, and multimodal integration. Our research focuses on:
- Reinforcement Learning for Care: Designing RL algorithms that learn optimal, personalized treatment paths from historical patient data while respecting medical safety boundaries.
- Mental Health and Well-being: Leveraging natural language interfaces and domain-specific LLMs to detect emotional changes and support mental health monitoring and psychotherapy.
- Multimodal Pathology Models: Integrating gigapixel histopathology images with text and genomics to build diagnostic and prognostic models.
Multimodal AI & Reasoning
Real-world applications require models to perceive, reason, and act across different data modalities. Our research in multimodal AI concentrates on:
- Vision-Language Models: Developing unified architectures that seamlessly integrate visual and textual information to understand complex scenes, documents, and diagrams.
- Multimodal Reasoning: Enabling systems to perform multi-step reasoning, cross-modal retrieval, and logical deduction using joint visual and textual contexts.
- Evaluation and Benchmarking: Establishing rigorous benchmarks and metrics to assess the safety, robustness, and factual consistency of multimodal models in real-world environments.
Collaborators
We work closely with leading research groups and academic institutions globally.
Funders & Support
We are grateful to the following institutions and funding bodies for supporting our research agenda and enabling scientific progress.
Academic funding and fostering machine learning networks.
Institutional support and research infrastructure.
Supporting AI research excellence and competitiveness.
Supporting doctoral/postdoctoral training and researcher mobility via the HAIF program.
Research collaboration and compute support under the LINGUA program.
Funding research, innovation, and technology partnerships.