9 papers
VecGlypher: Unified Vector Glyph Generation with Language Models
Xiaoke Huang, Bhavul Gauri, Kam Woh Ng +12
Vector glyphs are the atomic units of digital typography, yet most learning-based pipelines still depend on carefully curated exemplar sheets and raster-to-vector postprocessing, w…
When Token Pruning is Worse than Random: Understanding Visual Token Information in VLLMs
Yahong Wang, Juncheng Wu, Zhangkai Ni +8
Vision Large Language Models (VLLMs) incur high computational costs due to their reliance on hundreds of visual tokens to represent images. While token pruning offers a promising s…
Where on Earth? A Vision-Language Benchmark for Probing Model Geolocation Skills Across Scales
Zhaofang Qian, Hardy Chen, Zeyu Wang +9
Vision-language models (VLMs) have advanced rapidly, yet their capacity for image-grounded geolocation in open-world conditions, a task that is challenging and of demand in real li…
Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs
Xiaoke Huang, Ningsen Wang, Hui Liu +2
Large Multimodal Models (LMMs) are increasingly capable of answering medical questions that require joint reasoning over images and text, yet training general medical VQA systems i…
MedVLThinker: Simple Baselines for Multimodal Medical Reasoning
Xiaoke Huang, Juncheng Wu, Hui Liu +2
Large Reasoning Models (LRMs) have introduced a new paradigm in AI by enabling models to ``think before responding" via chain-of-thought reasoning. However, the absence of open and…
A Survey on Latent Reasoning
Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng +30
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate s…
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains
Juncheng Wu, Sheng Liu, Haoqin Tu +5
Recent advances in reasoning-enhanced Large Language Models such as OpenAI-o1/3 and DeepSeek-R1 have significantly improved performance on complex tasks. However, the quality and t…
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Hardy Chen, Haoqin Tu, Fali Wang +5
This work revisits the dominant supervised fine-tuning (SFT) then reinforcement learning (RL) paradigm for training Large Vision-Language Models (LVLMs), and reveals a key finding:…
m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models
Xiaoke Huang, Juncheng Wu, Hui Liu +2
Test-time scaling has emerged as a powerful technique for enhancing the reasoning capabilities of large language models. However, its effectiveness in medical reasoning remains unc…