DeepMind论文：从AGI到ASI的四条技术路径

Beautiful paper from Google DeepMind.

Explains the pathways from AGI to ASI, and why that jump could happen through several routes.

The authors frame the AGI-to-ASI transition around 4 technical pathways:

• continued scaling of compute, model size, data, and test-time inference;
• algorithmic paradigm shifts beyond today’s transformer-based foundation-model stack;
• recursive self-improvement, where AI accelerates AI R&D and improves future systems; and
• multi-agent collective intelligence, where large populations of specialized agents coordinate into a superhuman group agent.

Scaling may work for a while, but it could hit limits in data, compute, energy, or weaker returns from making systems larger.

Recursive improvement is the most uncertain path, because AI could speed up AI research, but that loop may also slow if hard research problems need real-world testing, scarce hardware, or new ideas.

Multi-agent collectives may be the most underappreciated path, because a society of competent digital workers could outperform a brilliant individual model through specialization, speed, and coordination.

The big point is that ASI may not arrive as 1 sudden event, but as a chain of faster changes as AI helps create better AI and stronger scientific tools.

– arxiv. org/abs/2606.12683

Title: "From AGI to ASI"