To improve the performance of the question dispatcher agent in HAMMR and enhance the overall system's capabilities, several strategies can be implemented:
Fine-tuning the dispatcher agent: The question dispatcher agent can be fine-tuned on a larger and more diverse dataset to improve its ability to accurately identify the type of VQA question and select the appropriate specialized agent.
Introducing context-aware decision-making: The dispatcher agent can be enhanced with context-aware decision-making mechanisms to better understand the relationships between different question types and select the most suitable agent based on the context.
Implementing reinforcement learning: Reinforcement learning techniques can be applied to the question dispatcher agent to optimize its decision-making process and learn from past interactions, leading to more informed choices in selecting specialized agents.
Enabling dynamic agent selection: The question dispatcher agent can be designed to dynamically select specialized agents based on real-time feedback and performance metrics, allowing for adaptive decision-making and continuous improvement.
By implementing these strategies, the performance of the question dispatcher agent in HAMMR can be further improved, leading to enhanced overall system capabilities in handling a wide range of multimodal reasoning tasks.