Researchers introduce NeoHorse-1, an approach to recursive self-improvement built on agentic post-training over a heterogeneous model pool with intelligent routing. The system combines capability evaluation with curriculum-based training so models can identify their own performance gaps and then improve through targeted learning. This creates a feedback loop in which what the system learns to do shapes what it learns from next, aiming at continuous autonomous improvement across successive iterations.
