Researchers introduced NeoHorse-1, a family of agent-native models designed for recursive self-improvement through agentic post-training with a routing harness. The system observes its own capabilities and converts that evidence into subsequent learning rounds, marking a step toward autonomous AI improvement.
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-native models developed to explore this path through agentic post-training. Our sys
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