Automatic (meta-) discovery and continual learning

Core objective

This research topic reimagines search and optimization within high-dimensional, non-differentiable, and structurally complex environments. Instead of relying on traditional black-box solvers or hyperparameter optimization, the project utilizes a population of “English-driven agents”—equipped with the reasoning and generative capabilities of foundation models—to collaboratively explore and discover high-performing solutions.

The language-mediated search paradigm

By shifting from purely numerical exploration to language-mediated search, the system leverages the vast general world knowledge embedded within foundation models. This enables the agents to propose intelligent search strategies based on known algorithms, architectural heuristics, system design best practices, and prior scientific literature, bypassing the inefficiencies of blind search.