29 citations · 46 across the 25 of their papers we have counts for
11 papers · 1 filter
Pro-Router: Token-Aware Progressive Model Routing with Adaptive Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Xinyuan Gui, Shaowen Wang, Sheng Sun +3
The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant challenges to real-time deploymen…
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
Yang Xiang, Yixin Ji, Ruotao Xu +4
Large reasoning models (LRMs) have achieved remarkable performance in complex reasoning tasks, driven by their powerful inference-time scaling capability. However, LRMs often suffe…
DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systems
Qi Guo, Zheming Yang, Yunqing Hu +2
Multimodal large language models (MLLMs) have shown strong capability in semantic understanding and visual reasoning, yet their use on continuous video streams in bandwidth-constra…
R2E-VID: Two-Stage Robust Routing via Temporal Gating for Elastic Edge-Cloud Video Inference
Zheming Yang, Lulu Zuo, Shun Lu +4
With the rapid growth of large-scale video analytics applications, edge-cloud collaborative systems have become the dominant paradigm for real-time inference. However, existing app…
MSAO: Adaptive Modality Sparsity-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Zheming Yang, Qi Guo, Jun Wan +4
Multimodal large language models (MLLMs) enable powerful cross-modal reasoning capabilities but impose substantial computational and latency burdens, posing critical challenges for…
SearchAttack: Red-Teaming LLMs against Knowledge-to-Action Threats under Online Web Search
Yu Yan, Sheng Sun, Mingfeng Li +6
Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they hav…
Not All Negative Samples Are Equal: LLMs Learn Better from Plausible Reasoning
Zixiang Di, Jinyi Han, Shuo Zhang +8
Learning from negative samples holds great promise for improving Large Language Model (LLM) reasoning capability, yet existing methods treat all incorrect responses as equally info…
From Atoms to Chains: Divergence-Guided Reasoning Curriculum for Unlabeled LLM Domain Adaptation
Yongqi Wang, Xiaofeng Ji, Jie Wang +6
Adapting Large Language Models (LLMs) to specialized domains without human-annotated data is a crucial yet formidable challenge. Widely adopted knowledge distillation methods often…
AIVD: Adaptive Edge-Cloud Collaboration for Accurate and Efficient Industrial Visual Detection
Yunqing Hu, Zheming Yang, Chang Zhao +4
Multimodal large language models (MLLMs) demonstrate exceptional capabilities in semantic understanding and visual reasoning, yet they still face challenges in precise object local…
ThinkDrive: Chain-of-Thought Guided Progressive Reinforcement Learning Fine-Tuning for Autonomous Driving
Chang Zhao, Zheming Yang, Yunqing Hu +4
With the rapid advancement of large language models (LLMs) technologies, their application in the domain of autonomous driving has become increasingly widespread. However, existing…
Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding
Jianghao Yin, Qingbin Li, Kun Sun +10
While Multimodal Large Language Models (MLLMs) excel at single-image understanding, they exhibit significantly degraded performance in multi-image reasoning scenarios. Multi-image…