Reading
Research shows that AI agents based on large language models can now independently complete the entire workflow of high-energy physics analysis—from event selection, background estimation, uncertainty quantification, statistical inference to paper writing—with minimal expert input.
Section 01
Research shows that AI agents based on large language models can now independently complete the entire workflow of high-energy physics analysis—from event selection, background estimation, uncertainty quantification, statistical inference to paper writing—with minimal expert input.
Section 02
The research team demonstrated that AI agents based on large language models can now independently perform most steps in the high-energy physics (HEP) analysis workflow, requiring only minimal expert-planned input.
Section 03
After obtaining high-energy physics datasets, execution frameworks, and prior experimental literature libraries, Claude Code successfully automated all stages of a typical analysis:
Section 04
The research team proposed a proof-of-concept framework Just Furnish Context (JFC), which integrates:
This framework successfully completed analyses of electroweak interactions, quantum chromodynamics (QCD), and Higgs boson measurements using open data from ALEPH, DELPHI, and CMS.
Section 05
The paper points out that the high-energy physics experimental community may have underestimated the capabilities of current AI systems. These tools are not intended to replace physicists, but rather:
Section 06
The research team calls on the community to rethink how to train students, organize analysis work, and allocate human expertise.
Section 07
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