  Identifying and reconstructing hadronic $\tau$ decays ($\tau_{\textrm{h}}$)
is an important task at current and future high-energy physics experiments, as
$\tau_{\textrm{h}}$ represent an important tool to analyze the production of
Higgs and electroweak bosons as well as to search for physics beyond the
Standard Model. The identification of $\tau_{\textrm{h}}$ can be viewed as a
generalization and extension of jet-flavour tagging, which has in the recent
years undergone significant progress due to the use of deep learning. Based on
a granular simulation with realistic detector effects and a particle flow-based
event reconstruction, we show in this paper that deep learning-based
jet-flavour-tagging algorithms are powerful $\tau_{\textrm{h}}$ identifiers.
Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet
and ParticleTransformer can be adapted in an end-to-end fashion for
discriminating $\tau_{\textrm{h}}$ from quark and gluon jets. We find that the
end-to-end transformer-based approach significantly outperforms contemporary
state-of-the-art $\tau_{\textrm{h}}$ reconstruction and identification
algorithms currently in use at the Large Hadron Collider.
