Quantum gravity represents the frontier where the very large meets the very small, attempting to unify Einstein's theory of gravity with the strange rules of quantum mechanics. This field explores the fundamental fabric of spacetime, seeking to understand how the universe behaves at its most extreme scales, from the heart of black holes to the moment of the Big Bang. Because these concepts often involve complex mathematics, they can feel distant to non-specialists, yet they hold the key to a complete picture of physical reality.

At Gist.Science, we bridge this gap by processing every new preprint in this category directly from arXiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, ensuring that groundbreaking research is accessible to everyone, from curious students to seasoned researchers. Below are the latest papers in quantum gravity, offering fresh insights into the nature of our cosmos.

ModMax black hole surrounded by perfect-fluid dark matter in Lorentz-violating Kalb-Ramond gravity

This paper investigates a static, spherically symmetric ModMax black hole surrounded by perfect-fluid dark matter within Lorentz-violating Kalb-Ramond gravity, revealing how the interplay of nonlinear electrodynamics, Lorentz symmetry breaking, and dark matter modifies the horizon structure, thermodynamic stability, and phase behavior of the system.

Fernando M. Belchior, Faizuddin Ahmed, Edilberto O. Silva2026-05-27⚛️ gr-qc

Covariant Dynamical Systems Formulation of the Tolman-Oppenheimer-Volkoff Equations

This paper reformulates the Tolman-Oppenheimer-Volkoff equations for static, spherically symmetric perfect-fluid stars within the 1+1+21+1+2 semi-tetrad formalism as a covariant first-order dynamical system, enabling a geometric analysis of stellar structure through autonomous flows in phase space for both linear and general equations of state.

Eduardo Bittencourt, Mariam Campbell, Peter K. S. Dunsby, Sergio E. Jorás2026-05-27⚛️ gr-qc

Field-level multi-tracers simulation-based inference of cosmological parameters from 3D maps

This paper presents a proof-of-concept Simulation-Based Inference pipeline that utilizes neural emulators trained on hydrodynamical simulations to extract cosmological parameters from 3D galaxy and neutral hydrogen maps, demonstrating that field-level multi-tracer analysis significantly outperforms traditional summary statistics by improving constraints by factors of 2 to 7 while robustly marginalizing over astrophysical uncertainties.

Giulio Scelfo, Satvik Mishra, Mauro Rigo, Roberto Trotta, Matteo Viel2026-05-27🔭 astro-ph