SciMaster: Towards General-Purpose Scientific AI Agents, Part I.
  X-Master as Foundation: Can We Lead on Humanity's Last Exam?
Authors: name: Jingyi Chai, name: Shuo Tang, name: Rui Ye, name: Yuwen Du, name: Xinyu Zhu, name: Mengcheng Zhou, name: Yanfeng Wang, name: Weinan E, name: Yuzhi Zhang, name: Linfeng Zhang, name: Siheng Chen

The rapid advancements of AI agents have ignited the long-held ambition of
leveraging them to accelerate scientific discovery. Achieving this goal
requires a deep understanding of the frontiers of human knowledge. As such,
Humanity's Last Exam (HLE) provides an exceptionally challenging touchstone for
evaluating scientific AI agents. In this work, we aim to construct the
foundational architecture for general-purpose agents and validate the
capabilities through leading performance on HLE. To achieve this, we introduce
X-Master, a tool-augmented reasoning agent designed to emulate human
researchers by interacting flexibly with external tools during its reasoning
process. This agent, guided by the conceptualization of code as an interaction
language, can flexibly leverage built-in Python libraries and our customized
tools to augment the reasoning. We further scale its capabilities through
X-Masters, a scattered-and-stacked agentic workflow that systematically
enhances breadth and depth of reasoning. Our open-source solution, X-Masters,
sets a new state-of-the-art record on HLE with a score of 32.1%, surpassing
OpenAI's and Google's Deep Research (26.6% and 26.9%) and becoming the first to
exceed the 30% threshold. This work allows us to gain a deeper understanding of
complex task-solving and accumulates valuable experience that can inform future
advancements, guiding subsequent model training.