Decision Making Processes

Lists online learning methods learnt by agent and supported by a multi-agent prompt recursion framework. These methods were motivated by traditional strategies of agent deciding to explore or exploit.

Note

• Computational Methods

  Gaby AI does not fine-tune large language models. Computational demand is expected to remain at the scale of basic numerical methods—comparable to ordinary least squares (OLS) regression or stochastic gradient descent (SGD)—rather than the high FLOPs required for large-scale model training.

Pros of RL / Temporal Isolation

The benefits per tech department:

• Software development: decoupling the knowledge base from model training avoids the instability and inconsistency inherent to current AI systems. By isolating Gaby’s temporal and long-term memory from the underlying base models, the application gains scalability and resilience, reducing susceptibility to fluctuations in evolving AI technologies.

• Database management: As the application is still in its early stages, it requires a strong and robust framework for database management. Once this framework is optimized, the efficiency of the existing data-cleaning ETL pipeline can also be improved.

Cons of RL / Temporal Isolation

• Database vulnerability: If the database workflow and architecture are not properly defined, the system is at high risk of structural failure.

• LLM Personalization: would require more time invested in the data analytc stages when drawing data features to fine-tune models.