1 citations · 1 across the 6 of their papers we have counts for
7 papers
MinCU: A Fine-Grained Benchmark for Grounded Minimal-Change Understanding in Image Pairs
Chaoqian Mu, Wenhao Wu, Zichen Liang +4
Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large language models (MLLMs). Existing b…
MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
Shangheng Du, Xiangchao Yan, Jinxin Shi +13
Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution…
Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition
Zichen Liang, Jingjing Fei, Jie Wang +6
Recent advances in multimodal large language models (MLLMs) have been primarily evaluated on general-purpose benchmarks, while their applications in domain-specific scenarios, such…
KAC: Kolmogorov-Arnold Classifier for Continual Learning
Yusong Hu, Zichen Liang, Fei Yang +3
Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a…
Identifying Sensitive Weights via Post-quantization Integral
Yuezhou Hu, Weiyu Huang, Zichen Liang +4
Serving Large Language Models (LLMs) is costly. However, post-training weight quantization can address this problem by both compressing their sizes for limited memory and saving ba…
SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors
Bohan Lyu, Siqiao Huang, Zichen Liang
Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, su…
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs
Kang Zhao, Tao Yuan, Han Bao +6
To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups…