Good morning，

 distinguished scholars, colleagues, and friends from across the globe. It is my greatest honor to stand before you today at Peking University to officially extend our warmest welcome to the "AI@LHC 2026: What's Next" workshop.
On behalf of the organizing committee, I want to express our sincere appreciation to every participant who has traveled far and wide to join us—especially our colleagues from CERN, leading research institutes, and industry partners. This gathering is a collaborative initiative supported by the CHEP–CERN Joint Program, bringing together two world-class communities in particle physics and artificial intelligence to explore the frontier of collider science.

We gather at a truly pivotal moment. The Large Hadron Collider has entered a new era of discovery, and artificial intelligence is no longer just an auxiliary tool—it is a core catalyst reshaping how we decode the universe’s most fundamental laws.

The scientific program of this workshop is designed to map the full spectrum of AI innovation for the LHC, covering multiple frontier topics:
• AI for core collider physics tasks: Including AI for event reconstruction, event generation, simulation-based inference for collider physics, and ML-based particle flow.
• Jet physics innovation: From DNN-based to Transformer-based jet tagging, anomalous jet detection, to jet tagging on the edge, pushing the boundaries of real-time processing.
• Cutting-edge AI paradigms: We will explore foundation models for high-energy physics (HEP), anomaly detection on the edge, and the future of AI on edge for real-time processing.
• Cross-domain frontier insights: The workshop also features keynote talks on frontier AI and computing topics delivered by industry and research institute experts, jointly exploring new challenges and future directions of AI in collider physics.

As we dive into these topics, I am proud to highlight the pioneering work happening right here at Peking University. Our high-energy physics team has been at the forefront of this integration:
• Development of Generalizable Jet Foundation Models: Since 2022, our team has led the development of the Global Particle Transformer (GloParT) at the CMS experiment, which is now deployed in experiments and has attracted participation from over ten international institutions. This work, along with the Particle Transformer algorithm and the JetClass/JetClass-II datasets co-developed with CERN and UCSD, has laid a critical foundation for large-scale model development in high-energy physics.
• AI for Full-Event Analysis: Our researchers have demonstrated that through precise AI engineering, the signal detection sensitivity for complex hadronic final states (such as double Higgs decay to 4b quarks) can surpass traditional methods by more than 5 times, showcasing the transformative potential of AI for LHC measurements.
• Next-Generation Jet Identification: In collaboration with IHEP and CERN, our team has developed AI-driven techniques combining the original Arbor particle flow algorithm with ParticleNet, capable of efficiently distinguishing 11 different types of jets (from 5 quarks, 5 antiquarks, and gluons). This "game-changing" technology, published in Physical Review Letters, can improve the precision of key physical measurements at future colliders by an order of magnitude.

Beyond experimental applications, Peking University is also pushing the boundaries of AI theory itself. Our interdisciplinary team recently made a landmark discovery: by applying physics’ least action principle, they identified detailed balance phenomena in LLM agent dynamics—the first time macroscopic physical laws have been found in AI generation processes without relying on specific model details. This work elevates AI research from empirical engineering to quantifiable physical science.

We also celebrate the BBT-Neutron scientific computing foundation model co-developed by PKU and partner institutions. Using innovative binary tokenization to unify multi-modal data processing, this open-source model matches the performance of specialized models like ParticleNet in jet origin identification tasks, even showing emergent phenomena not observed in traditional architectures.


Colleagues, the intersection of LHC experiments and artificial intelligence is one of the most dynamic and promising frontiers in modern science. With the solid foundation built by teams like ours at Peking University, and the collective expertise gathered in this room today, this workshop is more than a meeting—it is a launching pad for ideas that could redefine how we explore the building blocks of our universe.


So let’s embrace the next few days with curiosity, rigor, and a spirit of collaboration. I now declare the AI@LHC 2026: What's Next workshop officially open.

Thank you, and let the inspiring discussions begin!