Today, the observable cosmos exhibits a remarkable degree of isotropy and plausibly began in a nearly isotropic initial state. The properties of the Lorentzian Chern-Simons-Kodama (CSK) functional can provide an understanding of this initial state. In gravity with a positive cosmological constant, the Chern-Simons-Kodama (CSK) wavefunctional is an exact, chiral solution of the quantum gravitational constraints. We suggest that the normalizability and other issues with this functional, if interpreted as a proper state of quantum gravity, instead suggest an embedding into a larger quantum gravitational completion, and recast the CSK functional as a gravitational sphaleron with observationally desirable properties. By perturbing around the dominant de Sitter saddle of the wavefunctional with appropriate quantum gravitational boundary conditions, we find that for a closed universe the system is dynamically driven to spatial isotropy, while all anisotropic modes acquire positive quadratic curvature and are Gaussian-suppressed. The decay of this sphaleron therefore proceeds along an isotropic channel, providing an intrinsic quantum-gravitational mechanism for dynamical isotropization. This isotropization effect is robust under the inclusion of a slow-roll inflaton, and no analogous isotropic sphaleron exists for spatially flat or hyperbolic geometries. Taken together, these results recast the Lorentzian CSK functional as a chiral sphaleron that naturally prepares an approximately isotropic de Sitter background for inflation. Beyond this phenomenological study, we further suggest that the CSK functional can be understood as a boundary functional for a class of anomaly-free objects, including a complexified generalization of the Hartle-Hawking state.
Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present \texttt{GWAgent}, a large language model (LLM)-based workflow that constructs interpretable analytic surrogates directly from simulation data. Surrogate modeling is well suited to agentic workflows because candidate models can be quantitatively validated against ground-truth simulations at each iteration. As a demonstration, we build a surrogate for gravitational waveforms from eccentric binary black hole mergers. We show that providing the agent with a physics-informed domain ansatz substantially improves output model accuracy. The resulting analytic surrogate attains a median Advanced LIGO mismatch of $6.9\times10^{-4}$ together with an $\sim 8.4\times$ speedup in waveform evaluation, surpassing both symbolic regression and conventional machine learning baselines. Beyond producing an accurate model, the workflow identifies compact physical structure from the learned representation. As an astrophysical application, we use \texttt{GWAgent} to analyze the eccentricity of GW200129 and infer $e_{20\mathrm{Hz}}=0.099^{+0.063}_{-0.044}$. These results show that validation-constrained agentic workflows can produce accurate, fast, and interpretable surrogates for scientific simulations and inference.
Tensions often arise between different datasets in cosmology, and consistency tests can serve as a powerful tool for diagnosing potential issues. The density-shear Baryon Acoustic Oscillations (GI BAO) are the imprint of the BAO feature on the shear field induced by the large-scale tidal field. We highlight that GI BAO can provide a robust consistency check for the density BAO, shear measurement, and alignment model. Failure of this check hints at systematics in any of these parts. As an illustration, we present the first GI BAO measurement on photometric data, using the DES Y3 dataset. We find the GI BAO constraint on the BAO scale dilation parameter $\alpha $ to be $ 0.966 \pm 0.252 $ (1$\sigma$), in good agreement with the density BAO constraint, $ 0.966 \pm 0.037 $, thereby validating the density BAO, shear measurement, and the linear alignment model. Furthermore, we argue that combining the density BAO with the GI BAO yields results that are more resilient to systematic effects. Thanks to the massive data volumes of stage IV surveys, the GI BAO will play an even more prominent role as a consistency check.
this https URL . Comments/corrections welcome