Yu Yan, Chunhong Zhang, Haiyu Zhao, Ziyang Zeng, Zihao Liu, Yongkang Wu, Jianzhou Diao, YiJie Chen, Shujie Wang, Zheng Hu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Ranking relevance is a fundamental task in search engines, aiming to identify the items most relevant to a given user query. Traditional relevance models typically produce scalar scores or directly predict relevance labels, limiting both interpretability and the modeling of complex relevance signals. Inspired by recent advances in Chain-of-Thought (CoT) reasoning for complex tasks, we investigate whether explicit reasoning can enhance both interpretability and performance in relevance modeling. However, existing reasoning-based Generative Relevance Models (GRMs) primarily rely on supervised fine-tuning on large amounts of human-annotated or synthetic CoT data, which often leads to limited generalization. Moreover, domain-agnostic, free-form reasoning tends to be overly generic and insufficiently grounded, limiting its potential to handle the diverse and ambiguous cases prevalent in open-domain search. In this work, we formulate relevance modeling in Xiaohongshu search as a reasoning task and introduce a Reinforcement Learning (RL)-based training framework to enhance the grounded reasoning capabilities of GRMs. Specifically, we incorporate practical business-specific relevance criteria into the multi-step reasoning prompt design and propose Stepwise Advantage Masking (SAM), a lightweight process-supervision strategy which facilitates effective learning of these criteria through improved credit assignment. To enable industrial deployment, we further distill the large-scale RL-tuned model to a lightweight version suitable for real-world search systems. Extensive offline evaluations and online A/B tests demonstrate that our approach consistently delivers significant improvements across key relevance and business metrics, validating its effectiveness, robustness, and practicality for large-scale industrial search systems.

Abstract Angiography simulation is a critical step in virtual interventional surgery. However, the blood vessels are always complex and irregular, which poses a significant challenge to the accuracy and efficiency of simulation. In this paper, we present a novel method to simulate contrast media propagation, which can efficiently and accurately deal with various complex vascular structures and the coupling between blood and contrast media. Our method represents the vascular structures by signed distance functions and to impose boundary conditions, and then we compute the boundary volume by Gauss–Kronrod quadrature, which yields more accurate results even in complex vessels. Furthermore, we improve the simulation's initialization efficiency and real‐time force computational efficiency by precomputing the boundary volume values throughout the vascular structure and saving them to a volume map, which can be queried very efficiently during runtime. Moreover, we add term to the multiple‐fluid model to handle the vascular viscosity, which achieves a more realistic effect. Experiments show that our method obtains more smooth and accurate particle states, and significantly improves initialization efficiency. Finally, we invited 12 clinicians to evaluate the algorithm and verify the clinical value of our algorithm.