From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning

Ruilin Luo*, Chufan Shi*, Yizhen Zhang*, Cheng Yang, Songtao Jiang, Tongkun Guan, Ruizhe Chen, Ruihang Chu, Peng Wang, Mingkun Yang, Lei Wang, Yujiu Yang, Junyang Lin, Zhibo Yang

• We propose the VAS attention metric and find that the reasoning performance of Multimodal Large Reasoning Models (MLRM) is highly correlated with VAS.
• We introduce the AVAR training pipeline, which enhances the effectiveness of multimodal reasoning data training by scaling VAS through three stages: data construction, cold-start initialization, and reinforcement learning.
• Our model achieves state-of-the-art results on reasoning and perception benchmarks at the 7B scale.

Unlocking Multimodal Mathematical Reasoning via Process Reward Model

Ruilin Luo, Zhuofan Zheng, Yifan Wang, Xinzhe Ni, Zicheng Lin, Songtao Jiang, Yiyao Yu, Chufan Shi, Ruihang Chu, Lei Wang, Jin zeng, Yujiu Yang

[Paper] | [Code] | [HuggingFace]

• We are the first to propose leveraging a process reward model (PRM) to provide process-level optimization in multimodal mathematical reasoning.
• We introduce a training framework addressing three key stages: reasoning data scarcity, reward data scaling, and online PRM-integrated reinforcement learning (RL).
• Our data is used to train Seed1.5-VL, and we open-source URSA-8B-PS-GRPO, a model whose reasoning capability matches that of the InternVL3 series.

PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL

Ruilin Luo, Liyuan Wang, Binghuai Lin, Zicheng Lin, Yujiu Yang

[Paper] | [Code]

• We propose a code agent framework for the text-to-SQL task that leverages structured grammars for question-type classification, followed by question-type-specific automated question bank construction and a two-layer few-shot retrieval mechanism.
• We observe that stronger models achieve greater improvements on harder question types when provided with better-matched few-shot chain-of-thought (CoT) prompts, reflecting a learning pattern aligned with human cognition.
• Our method achieves state-of-the-art (SOTA) performance on benchmarks such as Spider and BIRD.

Fair Text-Attributed Graph Representation Learning

Ruilin Luo, Tianle Gu, Lin Wang, Yunfeng Zhou, Lei Wang, Yujiu Yang

• We are the first to identify the bias amplification effect between language model embeddings and GNNs in TAG representation learning.
• We propose solutions from the perspectives of fine-tuning and offline reinforcement learning.
• We provide a theoretical foundation for both the amplification of unfairness and our proposed mitigation approaches.

Prior Bilinear Based Models for Knowledge Graph Completion

Ruilin Luo*, Jiayi Li*, Jiaqi Sun, Jing Xiao, Yujiu Yang

[Paper]

• We propose the principle of identity in knowledge graph completion (KGC).
• We introduce a bilinear KGC method that explicitly models the law of identity and outperforms classical approaches such as RESCAL and ComplEx.
• We provide a theoretical derivation for modeling the law of identity.

Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion

Ruilin Luo, Jiayi Li, Jianghangfan Zhang, Jing Xiao, Yujiu Yang

[Paper] | [Code]