The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Tsinghua University（清华大学）；Reallm Labs（Reallm实验室）；Wuhan University（武汉大学）；The University of Hong Kong（香港大学）
Zhen Li, Yupeng Su, Runming Yang, Congkai Xie, Zheng Wang, Zhongwei Xie, Ngai Wong, Hongxia Yang
Large language models have achieved significant advancements in complex mathematical reasoning benchmarks, such as MATH. However, their substantial computational requirements present challenges for practical deployment. Model quantization has emerged as an effective strategy to reduce memory usage and computational costs by employing lower precision and bit-width representations. In this study, we systematically evaluate the impact of quantization on mathematical reasoning tasks. Our results demonstrate that aggressive quantization methods like AWQ and GPTQ introduce up to 32.39% accuracy degradation (average 11.31%) on Llama-3 models, particularly in numerical computation and reasoning planning. To address this, we introduce a multidimensional evaluation framework combining qualitative capability analysis and quantitative error assessment. We further develop targeted recovery strategies, showing that fine-tuning quantized models on only 545 task-specific examples for 3 minutes on 4 GPUs effectively restores reasoning capabilities to near full-precision levels. Additionally, our error assessment pipeline achieves 98.9% accuracy in diagnosing and localizing errors across 3,366 failure cases, providing actionable insights for mitigating quantization-induced degradation.
When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Electrical Engineering, Southeast University（电气工程学院，东南大学）；School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）
How Does Sequence Modeling Architecture Influence Base Capabilities of Pre-trained Language Models? Exploring Key Architecture Design Principles to Avoid Base Capabilities Degradation
Research Center for Social Computing and Interactive Robotics（社会计算与交互机器人研究中心）；Harbin Institute of Technology（哈尔滨工业大学）；iFLYTEK Co., Ltd（iFLYTEK公司）
Quantization Meets Reasoning: Exploring and Mitigating Degradation of Low-Bit LLMs in Mathematical Reasoning
The Hong Kong Polytechnic University（香港理工大学）；University of California, Santa Barbara（加州大学圣芭芭拉分校）；Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；The University of Hong Kong（香港大学）
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Universität Hamburg（汉堡大学）；Nazarbayev University（纳扎尔巴耶夫大学）；ISP RAS（俄罗斯科学院信息安全问题研究所）；Artificial Intelligence Research Institute（人工智能研究院）
Exploring Language Model Generalization in Low-Resource Extractive QA
Pennsylvania State University（宾夕法尼亚州立大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Indian Institute of Technology (IIT) Bhubaneswar（印度理工学院布巴内斯瓦尔分校）
BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions
University of Surrey（萨里大学）；ETH Zurich（苏黎世联邦理工学院）；University of Lausanne（洛桑大学）；University of Zurich（苏黎世大学）；University of Konstanz（康斯坦茨大学）；University of St. Gallen（圣加仑大学）
