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ASI-Evolve:自主 AI 研究取得突破

📅 2026-04-06 03:58 Nav Toor 人工智能 2 分鐘 2200 字 評分: 83
ASI-Evolve AI 研究 自动化机器学习 上海交通大学 神经架构搜索
📌 一句话摘要 上海交通大学的研究人员推出了“ASI-Evolve”,这是一个能够自主设计神经架构、训练数据和算法的系统,其表现优于人类设计的模型。 📝 详细摘要 这条推文总结了一篇题为《ASI-Evolve: AI Accelerates AI》的研究论文。该系统实现了整个研究循环的自动化——包括阅读论文、形成假设、设计实验和分析结果,全程无需人工干预。据报道,它发现了 105 种优于人类设计模型的新架构,改进了数据筛选策略,并创造了新型强化学习算法。这标志着 AI 系统向递归式自我改进迈出了重要一步。 📊 文章信息 AI 评分:83 来源:Nav Toor(@heynavtoor)

🚨BREAKING: Researchers built an AI that designs better AI than humans can. It discovered 105 new architectures that beat human-designed models. Nobody guided it. It taught itself.

The paper is called "ASI-Evolve: AI Accelerates AI." Published this week by researchers at Shanghai Jiao Tong University. Fully open-sourced. And what it demonstrates should stop every AI researcher cold.

They built a system that runs the entire AI research loop on its own. It reads scientific papers. It forms hypotheses. It designs experiments. It runs them. It analyzes the results. Then it uses what it learned to design better experiments. Over and over. Without human intervention.

They pointed it at neural architecture design first. Over 1,773 rounds of autonomous exploration, the system generated 1,350 candidate architectures. 105 of them beat the best human-designed model. The top architecture surpassed DeltaNet by +0.97 points. That is nearly 3 times the gain of the most recent human-designed state-of-the-art improvement.

Humans spent years to get +0.34 points. The AI got +0.97 on its own.

Then they pointed it at training data. The AI designed its own data curation strategies and improved average benchmark performance by +3.96 points. On MMLU, the most widely used knowledge benchmark, the improvement exceeded 18 points.

Then they pointed it at learning algorithms. The AI invented novel reinforcement learning algorithms that outperformed the leading human-designed method GRPO by up to +12.5 points on competition math.

Three pillars of AI development. Data. Architecture. Algorithms. The AI improved all three by itself.

Then they tested whether what the AI built actually works in the real world. They applied an AI-discovered architecture to drug-target interaction prediction. It achieved a +6.94 point improvement in scenarios involving completely unseen drugs. The AI designed something that works better than human experts in biomedicine.

This is the first system to demonstrate AI-driven discovery across all three foundational components of AI development in a single framework.

The recursive loop is now closed. AI is building AI. And it is already better at it than we are.

查看原文 → 發佈: 2026-04-06 03:58:04 收錄: 2026-04-06 06:00:51

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