SciMaster: Towards General-Purpose Scientific AI Agents, Part I.
  X-Master as Foundation: Can We Lead on Humanity's Last Exam?
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.
6870a7c6c8391850d60978ca
Jingyi Chai
6870a7c6c8391850d60978cb
Shuo Tang
6870a7c6c8391850d60978cc
Rui Ye
6870a7c6c8391850d60978cd
Yuwen Du
6870a7c6c8391850d60978ce
Xinyu Zhu
6870a7c6c8391850d60978cf
Mengcheng Zhou
6870a7c6c8391850d60978d0
Yanfeng Wang
6870a7c6c8391850d60978d1
Weinan E
6870a7c6c8391850d60978d2
Yuzhi Zhang
6870a7c6c8391850d60978d3
Linfeng Zhang
6870a7c6c8391850d60978d4
Siheng Chen
SciMaster: Towards General-Purpose Scientific AI Agents, Part I.
  X-Master as Foundation: Can We Lead on Humanity's Last Exam?
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.
X-Master, a tool-augmented reasoning agent using Python libraries and customized tools, achieves state-of-the-art performance on Humanity's Last Exam with a score of 32.1%.
tool-augmented reasoning agent X-Master X-Masters scattered-and-stacked agentic workflow interaction language Python libraries conceptualization of code