Advancing the science of artificial intelligence through innovative research in safety, alignment, and capabilities
Collaborating with leading institutions to shape the future of AI technology
RESEARCH FOCUS AREAS
Our interdisciplinary team explores the frontiers of artificial intelligence with a focus on developing robust, ethical, and innovative AI systems that address real-world challenges and advance human knowledge.
Our safety research focuses on developing techniques to make AI systems more reliable, transparent and robust. We're creating methods to audit AI systems, detect vulnerabilities, and ensure alignment with human values and intentions.
Our alignment work explores how to ensure AI systems understand and respect human values. We're developing approaches for value learning, preference elicitation, and techniques to mitigate AI systems finding unintended solutions.
Our innovation research pushes the boundaries of what's possible in AI. We're exploring new architectures, training methods, and applications that can create more capable and useful AI systems.
Senlab Ecosystem
Senlab is the innovation lab of the LTS Group, connecting cutting-edge technologies and platforms to advance AI research and innovation across multiple industries.
At Senlab, we envision a future where AI technology enhances human potential through safe and ethical innovation. Our mission is to pioneer breakthroughs in AI that create meaningful impact across society, guided by scientific excellence and human-centric values.
Advancing core AI capabilities through innovative neural architectures, optimization methods, and learning algorithms
Developing comprehensive safeguards, transparency tools, and ethical frameworks to ensure AI systems align with human values
Creating practical AI solutions for healthcare diagnostics, climate modeling, and personalized education systems
Latest publications
Explore how ReAct framework interleaves reasoning traces and task-specific actions to enhance LLM capabilities.
Our latest research demonstrates significant improvements in computational efficiency while maintaining model performance.
Discover how multiple AI agents can collaborate to solve complex problems through specialized roles and coordinated actions.
Our research on explainable AI focuses on techniques to make model decisions interpretable and trustworthy for users.
