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Research Initiatives
Our Research
Our research combines reinforcement learning and advanced language modeling to develop specialized intelligence—secure, reliable language models optimized for autonomous, domain-specific reasoning.
Our research combines measurable performance with ethical responsibility, advancing AI through rigorous testing and transparent benchmarking that prioritizes real-world impact. We're committed to making AI simultaneously more capable through specialized reasoning, more trustworthy through robust privacy guarantees, and more accessible through intuitive customization tools.
Our specialized teams focus on domain-specific reasoning models, privacy-preserving frameworks, and autonomous fine-tuning systems. They collaborate across disciplines to create AI technologies that are more intelligent, secure, and adaptable to diverse needs.
Specialized Reasoning Models (SRM)
Our Specialized Reasoning Models research focuses on developing language models with enhanced reasoning capabilities tailored to specific domains, enabling sophisticated problem-solving where general-purpose models fall short.
Effective performance when labeled data is scarce but verifiable using Reinforcement Learning based optimization
Advanced alignment techniques that enhance model reasoning without extensive supervision
'Aha moment' models that are smaller, faster, and specialized for targeted reasoning tasks
Optimization for complex reasoning tasks without sacrificing general capabilities
TrustML Privacy Research
Our TrustML research focuses on developing frameworks that convert transformer-based architectures into their private counterparts. These frameworks enable inference and fine-tuning while preserving privacy for data and model owners.
Novel permutation and factorization techniques for privacy-preserving AI with minimal computational overhead
Secure multi-party computation for highest-level privacy guarantees
Differential privacy integration for enhanced data protection
Customizable privacy solutions with tunable trade-offs between privacy preservation and computational requirements
Autonomous Finetuning Technology
Our Autonomous Finetuning research focuses on creating systems that enable developers to build custom AI models without machine learning expertise through sophisticated data augmentation and automated optimization.
Multi-agent orchestration for high-quality synthetic data generation
Tunable training parameters balancing depth and resource efficiency
Autonomous evaluation and model selection through parallel training
Domain-specific model customization with configurable creativity controls
Collaboration
Academic Partnerships
Open Source
Let's Collaborate