RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

Researchers introduced RSIAgent, a training-free multi-agent framework that achieves recursive self-improvement through autonomous memory construction, enabling digital agents to adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. RSIAgent coordinates curriculum-based exploration, according to the work, which targets autonomous exploration in unfamiliar settings.

Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce RSIAgent, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curricu