Research on LLM-Based Autonomous Agents and Agentic Systems

Knowledge-Driven and Self-Improving Systems

1. Lifelong Learning and Adaptation: Enable agents to continuously learn and adapt over time.

• Hybrid Memory Systems: Develop memory architectures combining short-term and long-term memory.

• Reinforcement Learning and Skill Acquisition: Techniques for guided skill acquisition and reinforcement learning.

• Catastrophic Forgetting and Hierarchical Abstraction: Methods to prevent forgetting and manage complexity through hierarchical planning.

• Neurosymbolic Integration: Combine neural networks with symbolic reasoning to enhance interpretability and robustness.

2. Agentic Frameworks

Frameworks for Multi-Agent Systems: Develop customizable, conversable agents and agent frameworks that can collaborate effectively with humans and other agents.

• Human-Agent Interaction: Develop advanced methods for human-in-the-loop learning, enabling effective collaboration between humans and agents.
• Neurosymbolic Frameworks: Implement neurosymbolic techniques within multi-agent frameworks to improve reasoning capabilities and facilitate better coordination among agents.

Use Cases:

• Software Development and Coding: Utilize teams of agents for collaborative coding, debugging, and documentation.
• Customer Support: Develop multi-agent systems to handle complex customer inquiries, provide real-time support, and enhance customer satisfaction.
• Cybersecurity: Explore the use of agentic frameworks in identifying, mitigating, and responding to cybersecurity threats.
• Data Analysis and Insights: Utilize agents to analyze large datasets, generate insights, and automate reporting processes.
• Education and Training: Create agents that can serve as tutors, providing personalized learning experiences and continuous assessment.

3. Multimodal Integration

Multimodal Capabilities: Develop agents that integrate vision, language, and speech capabilities for comprehensive environmental interaction.

• Multimodal Integration: Use methods to integrate multiple modalities, enabling more sophisticated interpretation and interaction capabilities.
• Software Development: Apply agents to software development tasks including multi-modal requirements capturing via diagrams and text, visual programming, textual and visual representations of intent and output.
• Visual Question Answering and Data Interpretation: Enhance agents' ability to understand and respond to visual inputs, facilitating tasks like visual question answering and data interpretation.

4. Ethical and Safety Considerations

Interpretable AI and Bias Mitigation: Ensure that agents provide explainable decisions and mitigate biases to operate ethically and transparently.

• Safety Assurance: Leverage the interpretability of symbolic reasoning and the learning capabilities of neural networks to develop robust safety mechanisms that align agent actions with human values and goals.