  Atmospheric retrievals of exoplanetary transmission spectra provide important
constraints on various properties such as chemical abundances, cloud/haze
properties, and characteristic temperatures, at the day-night atmospheric
terminator. To date, most spectra have been observed for giant exoplanets due
to which retrievals typically assume H-rich atmospheres. However, recent
observations of mini-Neptunes/super-Earths, and the promise of upcoming
facilities including JWST, call for a new generation of retrievals that can
address a wide range of atmospheric compositions and related complexities. Here
we report Aurora, a next-generation atmospheric retrieval framework that builds
upon state-of-the-art architectures and incorporates the following key
advancements: a) a generalised compositional retrieval allowing for H-rich and
H-poor atmospheres, b) a generalised prescription for inhomogeneous
clouds/hazes, c) multiple Bayesian inference algorithms for high-dimensional
retrievals, d) modular considerations for refraction, forward scattering, and
Mie-scattering, and e) noise modeling functionalities. We demonstrate Aurora on
current and/or synthetic observations of hot Jupiter HD209458b, mini-Neptune
K218b, and rocky exoplanet TRAPPIST1d. Using current HD209458b spectra, we
demonstrate the robustness of our framework and cloud/haze prescription against
assumptions of H-rich/H-poor atmospheres, improving on previous treatments.
Using real and synthetic spectra of K218b, we demonstrate the agnostic approach
to confidently constrain its bulk atmospheric composition and obtain precise
abundance estimates. For TRAPPIST1d, 10 JWST NIRSpec transits can enable
identification of the main atmospheric component for cloud-free CO$_2$-rich and
N$_2$-rich atmospheres, and abundance constraints on trace gases including
initial indications of O$_3$ if present at enhanced levels ($\sim$10-100x Earth
levels).
