Tag: 递归自我改进 (2 articles)

Lilian Weng argues that the key to AI self-improvement lies not in model size but in the 'harness' layer connecting models to reality, and proposes design patterns that can evolve themselves.

Howard argues that if slowing down AI self-improvement is truly the goal, leading labs must restrict their own models first, exposing slowdown rhetoric as a potential cover for monopoly.