7 papers
Probing RLVR training instability through the lens of objective-level hacking
Yiming Dong, Kun Fu, Haoyu Li +5
Prolonged reinforcement learning with verifiable rewards (RLVR) has been shown to drive continuous improvements in the reasoning capabilities of large language models, but the trai…
Inferring neutron-star Love-Q relations from gravitational waves in the hierarchical Bayesian framework
Zhihao Zheng, Ziming Wang, Jinwen Deng +2
Despite the large uncertainties in the equation of state for neutron stars (NSs), a tight universal ``Love-Q'' relation exists between their dimensionless tidal deformability, …
Anatomy of parameter-estimation biases in overlapping gravitational-wave signals: detector network
Ziming Wang, Dicong Liang, Lijing Shao
With the significantly improved sensitivity and a wider frequency band, the next-generation gravitational-wave (GW) detectors are anticipated to detect GW signals per y…
Lightweight posterior construction for gravitational-wave catalogs with the Kolmogorov-Arnold network
Wenshuai Liu, Yiming Dong, Ziming Wang +1
Neural density estimation has seen widespread applications in the gravitational-wave (GW) data analysis, which enables real-time parameter estimation for compact binary coalescence…
Constraining Fermionic Dark Matter with Galactic Neutron Stars
Jianyuan Luo, Dicong Liang, Lijing Shao
Dark matter (DM) remains one of the most significant open questions in modern physics, with its nature and interactions largely unexplored. In this study, we investigate the behavi…
Ringdown mode amplitudes of charged binary black holes
Zexin Hu, Daniela D. Doneva, Ziming Wang +4
The ringdown phase of the binary black hole (BBH) merger provides a clean and direct probe of strong-field gravity and tests of the nature of black holes. The quasinormal mode (QNM…