Title: LIMI: Less is More for Agency
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: agency, agentic, LLMs, Agent

Abstract: We define **Agency** as the emergent capacity of AI systems to function as autonomous agents—actively discovering problems, formulating hypotheses, and executing solutions through self-directed engagement with environments and tools. This fundamental capability marks the dawn of **the Age of AI Agency**, driven by a critical industry shift: the urgent need for AI systems that **don't just think, but work**. While current AI excels at reasoning and generating responses, industries demand autonomous agents that can execute tasks, operate tools, and drive real-world outcomes. As agentic intelligence becomes the defining characteristic separating cognitive systems from productive workers, efficiently cultivating machine autonomy becomes paramount.
Current approaches assume that more data yields better agency, following traditional scaling laws from language modeling. We fundamentally challenge this paradigm. **LIMI** (Less Is More for Intelligent Agency) demonstrates that agency follows radically different development principles. Through strategic focus on collaborative software development and scientific research workflows, we show that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior.
Using only 78 carefully designed training samples, LIMI achieves 73.5\% on AgencyBench, dramatically outperforming state-of-the-art models: Kimi-K2-Instruct (24.1\%), DeepSeek-V3.1 (11.9\%), Qwen3-235B-A22B-Instruct (27.5\%), and GLM-4.5 (45.1\%). Most strikingly, LIMI demonstrates 53.7\% improvement over models trained on 10,000 samples—achieving superior agentic intelligence with 128 times fewer samples.

Our findings establish the **Agency Efficiency Principle**: machine autonomy emerges not from data abundance but from strategic curation of high-quality agentic demonstrations. This discovery fundamentally reshapes how we develop autonomous AI systems, suggesting that **mastering agency requires understanding its essence, not scaling training data**. As industries transition from thinking AI to working AI, LIMI provides a paradigm for sustainable cultivation of truly agentic intelligence. Our data and code are available in an [anonymous repository](https://anonymous.4open.science/r/limi-2F47) and will be made publicly available upon acceptance.