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