# 《从AGI到ASI》--Google DeepMind论文

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-13 05:40
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmqbh4mxt01gaslam51796b1k
- 原文链接：https://x.com/rohanpaul_ai/status/2065549739266048120

## AI 摘要

Google DeepMind新论文提出从通用人工智能到超级智能的四条路径：持续扩展（计算、模型规模、数据、测试时推理）、算法范式革新（超越Transformer架构）、递归自我改进（AI加速自身研发）、多智能体集体智能（众多专业AI智能体协作出超人类智能）。扩展可能遇到数据、算力、能源瓶颈；递归改进最不确定；多智能体路径最易被低估，通过专业化与协调能超越单个强模型。ASI可能不是单次跃迁，而是AI辅助创造更好AI的加速链。

## 正文

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.

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Link - arxiv. org/abs/2606.12683

Title： "From AGI to ASI"