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.

⚛️ general relativity

Scalar-Tensor Symmetric Teleparallel Gravity: Reconstruct the Cosmological History with a Steep Potential

This paper investigates a scalar-non-metricity gravity model with a steep potential and power-law coupling within the symmetric teleparallel framework, demonstrating through dynamical analysis and Center Manifold Theory that it can unify early and late-time cosmic acceleration across three distinct connection branches while exhibiting a rich hierarchy of cosmological behaviors ranging from matter-dominated epochs to various singularities.

Ghulam Murtaza, Avik De, Andronikos Paliathanasis2026-09-10
⚛️ general relativity

Constraints on Scalar--Tensor--Vector Gravity Theory Parameters Inferred from Quasiperiodic Oscillations

This paper utilizes twin kilohertz quasiperiodic oscillations from twelve accreting compact objects to constrain the parameters of Scalar--Tensor--Vector Gravity (STVG), revealing that while charged STVG solutions best fit neutron star data, the orbital dynamics depend only on two effective mass and charge combinations, thereby setting model-independent limits on what QPO timing alone can measure.

Marco Muccino, Kuantay Boshkayev, Binay Prakash Akhouri, Izzet Sakallı, Yassine Sekhmani2026-09-10
🔭 astrophysics

Decoding the Imprints of Energy-Momentum Squared Gravity in Neutron Stars with Machine Learning Analysis

This study demonstrates that supervised machine learning classifiers, particularly Random Forest, can achieve over 99% accuracy in distinguishing between General Relativity and Energy-Momentum Squared Gravity models using neutron star observables (mass, radius, tidal deformability, and oscillation frequency), even after applying observational constraints.

Sayantan Ghosh, Premachand Mahapatra, Dipti Deb2026-09-10