9 papers
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…
xLLM Technical Report
Tongxuan Liu, Tao Peng, Peijun Yang +50
We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimi…
IFDNS: An Iterative Feedback-Driven Neuro-Symbolic Method for Faithful Logical Reasoning
Xiaoheng Wang, Tongxuan Liu, Zi Gong +5
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of reasoning tasks, including logical and mathematical problem-solving. While prompt-base…
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…
HydraInfer: Hybrid Disaggregated Scheduling for Multimodal Large Language Model Serving
Xianzhe Dong, Tongxuan Liu, Yuting Zeng +7
Multimodal Large Language Models (MLLMs) have been rapidly advancing, enabling cross-modal understanding and generation, and propelling artificial intelligence towards artificial g…
Arrow: Adaptive Scheduling Mechanisms for Disaggregated LLM Inference Architecture
Yu Wu, Tongxuan Liu, Yuting Zeng +6
Existing large language model (LLM) serving systems typically employ Prefill-Decode disaggregated architecture to prevent computational interference between the prefill and decode…