6 papers
Inference-Cost-Aware Dynamic Tree Construction for Efficient Inference in Large Language Models
Yinrong Hong, Zhiquan Tan, Kai Hu
Large Language Models (LLMs) face significant inference latency challenges stemming from their autoregressive design and large size. To address this, speculative decoding emerges a…
Physical Prompt Injection Attacks on Large Vision-Language Models
Chen Ling, Kai Hu, Hangcheng Liu +3
Large Vision-Language Models (LVLMs) are increasingly deployed in real-world intelligent systems for perception and reasoning in open physical environments. While LVLMs are known t…
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
AGI Team, Yuxuan Cai, Lu Chen +62
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incent…
MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space
Yicheng Chen, Yining Li, Kai Hu +3
Data quality and diversity are key to the construction of effective instruction-tuning datasets. % With the increasing availability of open-source instruction-tuning datasets, it i…
M-LLM Based Video Frame Selection for Efficient Video Understanding
Kai Hu, Feng Gao, Xiaohan Nie +8
Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply n…
DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering
Haochen Wang, Kai Hu, Liangcai Gao
Remote work and online courses have become important methods of knowledge dissemination, leading to a large number of document-based instructional videos. Unlike traditional video…