1 citations · 1 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…