Andre Rusli

Continuous Improvement and Parallel Autonomous Exploration: An LLM-Agent Framework for Searching Large Solution Spaces

A framework that gives LLM agents two mechanisms for searching large solution spaces autonomously: a leaderboard scored on held-out data acts as a reward signal that drives each agent to refine its solutions over repeated submissions, and a loop that operates even with a single agent.