Yige Wang

AAAI Conference 2026 Conference Paper

From Chaos to Clarity: A Knowledge Graph-Driven Audit Dataset Generation Framework for LLM Unlearning

• Weipeng Jiang
• Juan Zhai
• Shiqing Ma
• Ziyan Lei
• Xiaofei Xie
• Yige Wang
• Chao Shen

Recently LLMs have faced increasing demands to selectively remove specific information through Machine Unlearning. While evaluating unlearning effectiveness is crucial, existing benchmarks suffer from fundamental limitations in audit dataset generation from unstructured corpora. We identify two critical challenges: ensuring audit adequacy and handling knowledge redundancy between forget and retain datasets. Current approaches rely on ad-hoc question generation from unstructured text, leading to unpredictable coverage gaps and evaluation blind spots. Knowledge redundancy between forget and retain corpora further obscures evaluation, making it difficult to distinguish genuine unlearning failures from legitimately retained knowledge. To bring clarity to this challenge, we propose LUCID, an automated framework that leverages knowledge graphs to achieve comprehensive audit dataset generation with fine-grained coverage and systematic redundancy elimination. By converting unstructured corpora into structured knowledge representations, it transforms the ad-hoc audit dataset generation process into a transparent and automated generation pipeline that ensures both adequacy and non-redundancy. Applying LUCID to the MUSE benchmark, we generated over 69,000 and 111,000 audit cases for News and Books datasets respectively, identifying thousands of previously undetected knowledge memorization instances. Our analysis reveals that knowledge redundancy significantly skews metrics, artificially inflating ROUGE from 19.7% to 26.1% and Entailment Scores from 32.4% to 35.2%, highlighting the necessity of deduplication for accurate assessment.

AAAI Conference 2026 Conference Paper

MoMoREC: A Multi-agent Motivation Generation Framework for Residual Semantic ID-Aware Recommendation

• Yige Wang
• Mingming Li
• Li Wang
• Kaichen Zhao
• Wangming Li
• Weipeng Jiang
• Xueying Li

Recent advances in the field of sequential recommendation have highlighted the potential of Large Language Models (LLMs) in enhancing item embeddings and improving user understanding. However, existing approaches face three major limitations: 1) insufficient understanding of the reasons behind users' purchase decisions, 2) the high-dimensional embeddings directly produced by LLMs are not well compatible with traditional low-dimensional ID embeddings and 3) reliance on additional fine-tuning and high inference overhead to adapt LLMs to the recommendation task. In this paper, we propose MoMoREC, a simple yet effective user-understanding-based recommendation strategy. This method leverages the intrinsic comprehension capabilities of LLMs combined with residual semantic IDs to better understand users. Specifically, starting from common user purchasing behaviors and incorporating item characteristics, we employ a multi-agent framework to utilize LLMs in analyzing user shopping motivations and extracting high-dimensional dense embeddings. These embeddings are then transformed into low-dimensional IDs using a residual semantic ID approach via clustering and residual dimensionality reduction, which can be fed into the recommendation model. MoMoREC effectively integrates the understanding power of LLMs with the strengths of recommendation systems, preserving rich semantic language embeddings while reducing or eliminating the need for auxiliary trainable modules. As a result, it seamlessly adapts to any sequential recommendation framework. Experiments on three benchmark datasets show that MoMoRec significantly improves traditional recommendation models, demonstrating its effectiveness and flexibility.

EAAI Journal 2024 Journal Article

Interval-valued spherical fuzzy quality function deployment methodology: Metaverse collaborative system design application

• Qianwen Fu
• Qinghua Liu
• Xun Lu
• Tao Hu
• Yige Wang
• Junchen Pan

IROS Conference 2022 Conference Paper

CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning

• Mahdi Saleh
• Yige Wang
• Nassir Navab
• Benjamin Busam
• Federico Tombari

Processing 3D data efficiently has always been a challenge. Spatial operations on large-scale point clouds, stored as sparse data, require extra cost. Attracted by the success of transformers, researchers are using multi-head attention for vision tasks. However, attention calculations in transformers come with quadratic complexity in the number of inputs and miss spatial intuition on sets like point clouds. We redesign set transformers in this work and incorporate them into a hierarchical framework for shape classification and part and scene segmentation. We propose our local attention unit, which captures features in a spatial neighborhood. We also compute efficient and dynamic global cross attentions by leveraging sampling and grouping at each iteration. Finally, to mitigate the non-heterogeneity of point clouds, we propose an efficient Multi-Scale Tokenization (MST), which extracts scale-invariant tokens for attention operations. The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods while requiring significantly fewer computations. Our proposed architecture predicts segmentation labels with around half the latency and parameter count of the previous most effi-cient method with comparable performance. The code is available at https://github.com/YigeWang-WHU/CloudAttention.