The concept of lepton flavour universality (LFU), according to which the three lepton families are equivalent except for their masses, is a cornerstone prediction of the Standard Model (SM). LFU can be violated in models beyond the SM by new physics particles that couple preferentially to certain generations of leptons. In the last few years, hints of LFU violation have been observed in both tree-level b → c ν and loop-level b → s transitions. These measurements, combined with the tensions observed in angular observables and branching fractions of rare semileptonic b decays, point to a coherent pattern of anomalies that could soon turn into the first observation of physics beyond the SM. These proceedings review the anomalies seen by the LHC experiments and the B factories, and give an outlook for the near future.

In this report I will describe the latest results of the ALICE, ATLAS, CMS, and LHCb experiments in the fields of production, spectroscopy, and properties of heavy hadrons. In particular, I will concentrate on measurements of quarkonium production cross sections, polarization, and mass, on measurements of production cross sections and lifetimes of open heavy flavors, on the recent observations of new states and decay modes, and on other searches for new and exotic hadrons.

Latest results on the heavy flavour production and spectroscopy at the LHC are reviewed. These include measurements of production rates of the charmed and beauty hadrons, and observations of new excited charmed and beauty hadrons and exotic states.

The use of machine learning is increasing at the LHC experiments including both the ATLAS and LHCb collaborations, in terms of the number of users, the breadth of applications, and the set of different techniques under study. While traditionally applied in the context of improving the final analysis selection for a given physics result, machine learning is now also being applied in many other places, including object reconstruction, object calibration, object identification, simulation, and automation. The variety of machine learning tools being used is also expanding, and many areas are benefiting from the use of deep learning methods. It is expected that this growth in machine learning within particle physics will continue, as the large and rapidly increasing datasets provide the perfect environment to develop and refine new machine learning algorithms which can maximally exploit the complex data.

The conventional description of heavy-flavour hadron production in pp collisions is based on a factorisation approach, assuming universal fragmentation functions among collision systems. Recent results on heavy-flavour baryon measurements from the LHC experiments show tensions with model calculations based on this approach and employing fragmentation functions constrained from e + e − and e − p collision experiments. In this contribution, the most recent results from ALICE, ATLAS, CMS and LHCb experiments on the heavy-flavour hadron production in pp collisions at the TeV scale are reported. The comparison with the theoretical predictions that address the baryon enhancement in hadronic collisions at the LHC is also discussed.