Super AGI Research Lab

Research lab dedicated to explore and pursue Generalized Super Intelligence

Neurosymbolic AI

Integrating symbolic reasoning with neural networks to achieve more abstract and human-like cognitive abilities.

Autonomous Agents & Multi-Agent Systems

Developing intelligent agents capable of independent decision-making and collaboration within complex environments.

Novel Model Architectures

Exploring innovative architectures and frameworks to enhance the capabilities and efficiency of AI systems.

System 2 Thinking

Investigating higher-order cognitive processes, such as reasoning, planning, and problem-solving, akin to human System 2 thinking.

Recursive Self-Improvement Systems

Designing AI systems capable of autonomously improving their own algorithms, architectures, and learning strategies over time.

Socio-Economic Research Areas

Digital Workforce

Examining the impact of AI and automation on employment dynamics, skill requirements, and the future of work in the digital age.

Algorithmic Governance

Studying the use of AI algorithms in decision-making processes within governance structures, and the associated implications for accountability, transparency, and fairness.

Universal Basic Income (UBI)

Investigating the potential role of UBI in mitigating the socio-economic effects of automation and AI-driven labor market shifts.

Ethical AI

Addressing ethical considerations and challenges in the development, deployment, and governance of advanced AI systems, with a focus on fairness, accountability, transparency, and societal impact.

Human-AI Collaboration

Exploring the dynamics of collaboration between humans and AI systems in various contexts, including work, education, healthcare, and creative endeavors.

Our Publications

VEagle: Advancements in Multimodal Representation Learning

Recursive Agent Trajectory Fine-Tuning: Utilizing Agent Instructions for Enhanced Autonomy and Efficiency in AI Agents


  • Understanding dedicated & shared tool memory in SuperAGI

    SuperAGI tools enable developers to extend agent capabilities, helping them [...]

  • Introducing AACP (Agent to Agent Communication Protocol)

    Agents are kernels of micro-autonomous intelligence which have the potential to [...]

  • Agent Instructions: Autonomous AI Agent Trajectory Fine-Tuning

    Software ate the world, AI ate the software and now agents are eating AI Agents, arguably are the early design pattern of what could eventually evolve into the holy grail [...]

Spotlight Papers

Research papers we are reading

  • InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

  • FERRET: Refer and Ground Anything Anywhere at any Granularity

  • BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions

  • Improved Baselines with Visual Instruction Tuning

  • PoSE: Efficient Context Window Extention of LLMs via Positional Skip-wise Training

  • Extending Context Window Of Large Language Models Via Position Interpolation