1 citations · 3 across the 11 of their papers we have counts for
12 papers
ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification
Jieyun Huang, Yi Shen, Kaikai Zhao +7
Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious classifiers in production systems. In Chinese SMS, attackers can exploi…
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed…
Token-Operations-Oriented Inference Optimization Techniques for Large Models
Shiguo Lian, Kai Wang, Zhaoxiang Liu +23
Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large model services. Centered on token-oriented…
NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?
Yuru Wang, Lejun Cheng, Yuxin Zuo +14
We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move b…
Evaluating LLM Coding Agents on SZ-Family Lossy Compression Across Architectures
Changqing Li, Shouwei Gao, Kai Zhao +2
Large language model (LLM) coding agents are increasingly applied to code translation and optimization, yet their effectiveness in performance-critical high-performance computing (…
KAConvNet: Kolmogorov-Arnold Convolutional Networks for Vision Recognition
Zhaoxiang Liu, Zhicheng Ma, Kaikai Zhao +2
The Convolutional Neural Networks (CNNs) have been the dominant and effective approach for general computer vision tasks. Recently, Kolmogorov-Arnold neural networks (KANs), based…
TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration
Guoli Jia, Yisheng Zhang, Haote Hu +11
Vision-language agents that orchestrate specialized tools for image restoration (IR) have emerged as a promising method, yet most existing frameworks operate in a training-free man…
MITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillance
Kaikai Zhao, Zhaoxiang Liu, Peng Wang +7
General-domain large multimodal models (LMMs) have achieved significant advances in various image-text tasks. However, their performance in the Intelligent Traffic Surveillance (IT…
Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis
Kaikai Zhao, Zhaoxiang Liu, Xuejiao Lei +12
DeepSeek-R1, known for its low training cost and exceptional reasoning capabilities, has achieved state-of-the-art performance on various benchmarks. However, detailed evaluations…
What is the best model? Application-driven Evaluation for Large Language Models
Shiguo Lian, Kaikai Zhao, Xinhui Liu +5
General large language models enhanced with supervised fine-tuning and reinforcement learning from human feedback are increasingly popular in academia and industry as they generali…
CHiSafetyBench: A Chinese Hierarchical Safety Benchmark for Large Language Models
Wenjing Zhang, Xuejiao Lei, Zhaoxiang Liu +5
With the profound development of large language models(LLMs), their safety concerns have garnered increasing attention. However, there is a scarcity of Chinese safety benchmarks fo…
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks
Jianfeng Huang, Chenyang Li, Yimin Lin +1
It is hard to collect enough flaw images for training deep learning network in industrial production. Therefore, existing industrial anomaly detection methods prefer to use CNN-bas…