7 papers
Seeing Symbols, Missing Cultures: Probing Vision-Language Models' Reasoning on Fire Imagery and Cultural Meaning
Haorui Yu, Yang Zhao, Yijia Chu +1
Vision-Language Models (VLMs) often appear culturally competent but rely on superficial pattern matching rather than genuine cultural understanding. We introduce a diagnostic frame…
MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Yanghao Li, Rui Qian, Bowen Pan +24
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from…
Apple Intelligence Foundation Language Models: Tech Report 2025
Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…
HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search
Jiajie Jin, Xiaoxi Li, Guanting Dong +5
Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG…
Efficient Jailbreaking of Large Models by Freeze Training: Lower Layers Exhibit Greater Sensitivity to Harmful Content
Hongyuan Shen, Min Zheng, Jincheng Wang +1
With the widespread application of Large Language Models across various domains, their security issues have increasingly garnered significant attention from both academic and indus…
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
Quan Wei, Chung-Yiu Yau, Hoi-To Wai +4
Supervised fine-tuning is a standard method for adapting pre-trained large language models (LLMs) to downstream tasks. Quantization has been recently studied as a post-training tec…