Publications (19)
Trajectory Generation with Endpoint Regulation and Momentum-Aware Dynamics for Visually Impaired Scenarios
Yuting Zeng, Manping Fan, You Zhou +5
Trajectory generation for visually impaired scenarios requires smooth and temporally consistent state in structured, low-speed dynamic environments. However, traditional jerk-based…
Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts?
Qingchuan Li, Jiatong Li, Zirui Liu +4
Logical reasoning with large language models (LLMs) has received growing attention. One mainstream approach translates natural language into formal logic and then applies symbolic…
RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference
Ben Wan, Yan Feng, Zihan Tang +4
DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural inf…
Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, Jingya Wang +6
Safe and efficient assistive planning for visually impaired scenarios remains challenging, since existing methods struggle with multi-objective optimization, generalization, and in…
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…
Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models
Tongxuan Liu, Wenjiang Xu, Weizhe Huang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Althoug…
OOCO: Latency-disaggregated Architecture for Online-Offline Co-locate LLM Serving
Siyu Wu, Zihan Tang, Yuting Zeng +5
Large Language Models (LLMs) are increasingly deployed in both latency-sensitive online services and cost-sensitive offline workloads. Co-locating these workloads on shared serving…
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…
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…
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…
Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approach
Qingchuan Li, Jiatong Li, Tongxuan Liu +4
Large Language Models (LLMs) have exhibited remarkable potential across a wide array of reasoning tasks, including logical reasoning. Although massive efforts have been made to emp…
Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, You Zhou +5
This paper proposes a momentum-constrained hybrid heuristic trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios, integrating…
Area of minimal hypersurfaces
Qing-Ming Cheng, Guoxin Wei, Yuting Zeng
A well-known conjecture of Yau states that the area of one of Clifford minimal hypersurfaces $S^k\big{(}\sqrt{\frac{k}{n}}\, \big{)}\times S^{n-k}\big{(}\sqrt{\frac{n-k}{n}}\, \big…
From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation
Qingchuan Li, Mingyue Cheng, Zirui Liu +3
Logical reasoning is a core challenge in natural language understanding and a fundamental capability of artificial intelligence, underpinning scientific discovery, mathematical the…
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…
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…
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…
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…
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…