1 citations · 1 across the 9 of their papers we have counts for
10 papers
Adaptive FastOPD: Progress-Aware Rollout Horizon Expansion for Efficient On-Policy Distillation
Qian Tan, Huaifei Liang, Xuanyu Zhu +2
On-policy distillation (OPD) provides dense teacher supervision along student-generated trajectories, but its online rollout process incurs substantial computational cost, particul…
MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation
Qian Tan, Xuanyu Zhu, Lei Jiang +3
Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecular representations. However…
Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents
Jiayi Wu, Ruobing Xie, Zeqian Huang +6
Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy Optimization (GRPO) being a widely used…
Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate
Qian Tan, Lei Jiang, Yuting Zeng +2
Large language models (LLMs) exhibit systematic Western-centric bias, yet whether prompting in non-Western languages (e.g., Chinese) can mitigate this remains understudied. Answeri…
Chem3DLLM: 3D Multimodal Large Language Models for Chemistry
Lei Jiang, Shuzhou Sun, Biqing Qi +5
In the real world, a molecule is a 3D geometric structure. Compared to 1D SMILES sequences and 2D molecular graphs, 3D molecules represent the most informative molecular modality.…
Cross-Modal Obfuscation for Jailbreak Attacks on Large Vision-Language Models
Lei Jiang, Zixun Zhang, Zizhou Wang +4
Large Vision-Language Models (LVLMs) demonstrate exceptional performance across multimodal tasks, yet remain vulnerable to jailbreak attacks that bypass built-in safety mechanisms…
TARAC: Mitigating Hallucination in LVLMs via Temporal Attention Real-time Accumulative Connection
Lei Jiang, Chunzhao Xie, Tongxuan Liu +6
Large Vision-Language Models have demonstrated remarkable capabilities, yet they suffer from hallucinations that limit practical deployment. While various mitigation strategies exi…
S-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency
Yuting Zeng, Weizhe Huang, Lei Jiang +5
Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing (NLP) scenarios, but they still face challenges when handling comp…
FoPru: Focal Pruning for Efficient Large Vision-Language Models
Lei Jiang, Weizhe Huang, Tongxuan Liu +4
Large Vision-Language Models (LVLMs) represent a significant advancement toward achieving superior multimodal capabilities by enabling powerful Large Language Models (LLMs) to unde…
GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion
Tongxuan Liu, Xingyu Wang, Weizhe Huang +5
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoni…