1 citations · 2 across the 7 of their papers we have counts for
7 papers
UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards
Jun Wang, Shuo Tan, Zelong Sun +5
Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems typically rely on generic ret…
-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
Siting Wang, Xiaofeng Wang, Zheng Zhu +7
Flow-based vision-language-action (VLA) models excel in embodied control but suffer from intractable likelihoods during multi-step sampling, hindering online reinforcement learning…
GTA: Grouped-head latenT Attention
Luoyang Sun, Cheng Deng, Jiwen Jiang +5
Attention mechanisms underpin the success of large language models (LLMs), yet their substantial computational and memory overhead poses challenges for optimizing efficiency and pe…
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Haoyang Li, Zhanchao Xu, Yiming Li +9
Multi-turn dialogues are essential in many real-world applications of large language models, such as chatbots and virtual assistants. As conversation histories become longer, exist…
SpatialViz-Bench: A Cognitively-Grounded Benchmark for Diagnosing Spatial Visualization in MLLMs
Siting Wang, Minnan Pei, Luoyang Sun +6
Humans can imagine and manipulate visual images mentally, a capability known as spatial visualization. While many multi-modal benchmarks assess reasoning on visible visual informat…
PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing
Cheng Deng, Luoyang Sun, Jiwen Jiang +10
While scaling laws have been continuously validated in large language models (LLMs) with increasing model parameters, the inherent tension between the inference demands of LLMs and…
Activation-aware Probe-Query: Effective Key-Value Retrieval for Long-Context LLMs Inference
Qingfa Xiao, Jiachuan Wang, Haoyang Li +6
Recent advances in large language models (LLMs) have showcased exceptional performance in long-context tasks, while facing significant inference efficiency challenges with limited…