6 papers
From Heuristics to Transformers: A Comprehensive Survey of Type Inference from Stripped Binaries
Hua Zheng, Yuhang Guo, Kuanishbay Sadatdiynov +5
The recovery of high-level type information from stripped binaries-executables devoid of symbol tables and debugging information-is a cornerstone of software reverse engineering, v…
Large Language Models for Multilingual Code Intelligence: A Survey
Chao Jiang, Dugang Liu, Cheng Wen +6
Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance…
Enhancing guidance for missing data in diffusion-based sequential recommendation
Qilong Yan, Yifei Xing, Dugang Liu +2
Contemporary sequential recommendation methods are becoming more complex, shifting from classification to a diffusion-guided generative paradigm. However, the quality of guidance i…
See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
Pengteng Li, Pinhao Song, Wuyang Li +5
We introduce SEE&TREK, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMS) under vision-only const…
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
Dugang Liu, Shenxian Xian, Xiaolin Lin +5
The training paradigm integrating large language models (LLM) is gradually reshaping sequential recommender systems (SRS) and has shown promising results. However, most existing LL…
Benchmarking for Deep Uplift Modeling in Online Marketing
Dugang Liu, Xing Tang, Yang Qiao +4
Online marketing is critical for many industrial platforms and business applications, aiming to increase user engagement and platform revenue by identifying corresponding delivery-…