ASI-Evolve: AI Accelerates AI

arXiv cs.AI / 4/1/2026

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Key Points

論文『ASI-Evolve』は、AIがAI研究を加速するためのエージェント型フレームワーク（learn-design-experiment-analyzeの閉ループ）を提案しています。
進化的エージェントに「人間の事前知識を各探索に注入するcognition base」と「実験結果を再利用可能な知見に要約するdedicated analyzer」を組み込み、弱い教師信号・長期・高コストの研究ループへの適用を狙います。
ニューラルアーキテクチャ設計では線形attentionのSOTAを105件発見し、最良モデルがDeltaNetを+0.97上回ったと報告しています。
データキュレーションでは平均ベンチマークで+3.96、MMLUで18点以上の改善が示され、強化学習アルゴリズムでもGRPOを最大+12.5（タスク別）上回る結果が述べられています。
このAI-for-AIの枠組みは、数学・生物医学などAIスタック外への転移の初期証拠も提示されており、閉ループ研究の実現可能性を示す「有望な一歩」と位置づけられています。

Abstract

Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.