arXiv subjects

Hongji Chen

Publications and source records attributed to Hongji Chen.

3 recordsLinked to original sources

arXiv2026

Thermal Breaking of the I-Love Universality for Hot White Dwarfs

The universal I-Love-Q relations for compact stars have significant applications in gravitational-wave astronomy, but thermal effects can break these relations in low-mass white dwarfs. In this work, we employ the stellar evolution code MESA to construct realistic models of $0.15 \, M_{\odot}$ helium-core and $0.6 \, M_{\odot}$ carbon-oxygen core white dwarfs at various temperatures. By utilizing the Clairaut-Radau equation, we quantitatively extract the radial variation of the eccentricity of internal isodensity surfaces. Our numerical results demonstrate that higher central temperatures amplify the eccentricity variation, causing the I-Love relations to deviate from the zero-temperature Chandrasekhar model, whereas subsequent cooling restores them. This confirms that the temperature-induced violation of the universal relations is fundamentally driven by the loss of self-similarity in isodensity surfaces, providing key insights into the applicability conditions of I-Love-Q relations in compact objects.

astro-ph.HE↗

arXiv2026

AI Can Learn Scientific Taste

Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with the potential for long-term scientific impact. Scientific taste is largely concentrated among highly experienced researchers, whose expertise is usually limited to a few specialised fields. If AI could learn scientific taste, it could reduce reliance on human experts and accelerate scientific discovery. Whether AI can learn this ability remains an open question. We introduce Reinforcement Learning from Community Feedback (RLCF) to learn judgement and ideation. Scientific Judge learns from community feedback, such as citations. Scientific Thinker learns to propose research ideas with high potential impact. Experiments show that Scientific Judge outperforms strong LLM baselines and that learned judgement generalises to future-year papers, other community metrics, and unseen fields. Furthermore, Scientific Thinker proposes research ideas with higher potential impact than those proposed by baselines. These results suggest that AI can learn scientific taste, marking an important step towards AI systems that could help accelerate scientific discovery.

cs.CL↗

arXiv2025

Unified Enhancement of the Generalization and Robustness of Language Models via Bi-Stage Optimization

Neural network language models (LMs) are confronted with significant challenges in generalization and robustness. Currently, many studies focus on improving either generalization or robustness in isolation, without methods addressing both aspects simultaneously, which presents a significant challenge in developing LMs that are both robust and generalized. In this paper, we propose a bi-stage optimization framework to uniformly enhance both the generalization and robustness of LMs, termed UEGR. Specifically, during the forward propagation stage, we enrich the output probability distributions of adversarial samples by adaptive dropout to generate diverse sub models, and incorporate JS divergence and adversarial losses of these output distributions to reinforce output stability. During backward propagation stage, we compute parameter saliency scores and selectively update only the most critical parameters to minimize unnecessary deviations and consolidate the model's resilience. Theoretical analysis shows that our framework includes gradient regularization to limit the model's sensitivity to input perturbations and selective parameter updates to flatten the loss landscape, thus improving both generalization and robustness. The experimental results show that our method significantly improves the generalization and robustness of LMs compared to other existing methods across 13 publicly available language datasets, achieving state-of-the-art (SOTA) performance.

cs.CL↗