The High-Luminosity LHC (HL-LHC) will impose unprecedented demands on event reconstruction, driven by extreme pile-up conditions, increased detector granularity, and stringent latency constraints. In this environment, track reconstruction stands out as one of the most critical and computationally challenging components of future trigger systems, directly impacting physics performance and the efficiency of trigger-level event selection. To address these challenges, the CERN Next Generation Triggers project is devoting significant effort to rethinking trigger-level track reconstruction algorithms, software architectures, and computing models, with the goal of ensuring scalability, robustness, and sustained physics performance throughout the HL-LHC era. A key aspect is the increased tight coupling between online and offline track reconstruction, which blurs the traditional boundaries between them and have broader implications for WLCG preparation for HL-LHC. This presentation highlights selected, concrete developments in trigger-level track reconstruction within the ATLAS and CMS experiments, focusing on innovative solutions tailored to HL-LHC conditions. Topics include the evolution of tracking strategies, algorithmic simplifications and refactorings driven compute resources constraints, the adoption of heterogeneous computing architectures such as GPUs and FPGAs, as well as new approaches based on machine learning techniques.
Noemi Calace (Thu,) studied this question.