LIMI: Less is More for Agency
Authors: name: Yang Xiao, name: Mohan Jiang, name: Jie Sun, name: Keyu Li, name: Jifan Lin, name: Yumin Zhuang, name: Ji Zeng, name: Shijie Xia, name: Qishuo Hua, name: Xuefeng Li, name: Xiaojie Cai, name: Tongyu Wang, name: Yue Zhang, name: Liming Liu, name: Xia Wu, name: Jinlong Hou, name: Yuan Cheng, name: Wenjie Li, name: Xiang Wang, name: Dequan Wang, name: Pengfei Liu

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 comprehensive agency benchmarks,
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.