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Xuewen Yang

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Robust Response Generation in the Wild

Jiatai Wang
Zhiwei Xu
Di Jin
Xuewen Yang
Tao Li

The proliferation of large language models (LLMs) has significantly advanced intelligent systems. Unfortunately, LLMs often face knowledge conflicts between internal memory and retrieved external information, arising from misinformation, biases, or outdated knowledge. These conflicts undermine response reliability and introduce uncertainty in decision-making. In this work, we analyze how LLMs navigate knowledge conflicts from an information-theoretic perspective and reveal that when conflicting and supplementary information exhibit significant differences, LLMs confidently resolve their preferences and alleviate the uncertainty during their response generation. When this difference is ambiguous, LLMs experience considerable uncertainty about their generation. Based on this insight, we propose Swin-VIB, a novel framework that integrates a pipeline of variational information bottleneck models to adapt the retrieved information difference, facilitating robust response generation of LLMs even in conflicting contexts. Extensive experiments confirm our theoretical analysis and demonstrate the performance of Swin-VIB. Notably, Swin-VIB outperforms all competitive baselines in terms of the accuracy of the multiple-choice task, while improving the EM values in the open-ended QA task by at least 11.14%.

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ICRA Conference 2025 Conference Paper

Exploring the Domain-Invariant Flow Representation in Vision-Based Tactile Sensors for Omni-Hardness Perception

Xuewen Yang
Nan Wang 0013
Jiayang Gu
Yugang Zhang
Guoyu Wang
Aiguo Song

Vision-based tactile sensors have recently gained prominence due to their superior resolution and ability to capture multi-dimensional contact information. However, even when sensors share the same sensing principle, variations in production factors can lead to differences in the color patterns of tactile signals. Unlike common vision tasks, vision-based tactile perception depends on tracking light variation in colorful signals, making it more susceptible to lighting conditions and thus more prone to domain gaps. In this paper, we propose an Omni-hardness perception framework that enables adaptation across various vision-based tactile sensors. Firstly, in-depth analyses of the factors influencing the generalization of hardness perception are presented. Furthermore, the light balance module and the force scale module are coupled to regulate network learning of generalized representations. Experimental results across multiple sensors demonstrate the transferability of learned representations. Additionally, downstream tasks in natural object perception, tumor detection, and grasping stability prediction, are proposed to evaluate the potential applications. The framework's performance shows promise for advancing general tactile sensing and embodied tactile perception.

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AAAI Conference 2020 Conference Paper

Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning

Yingru Liu
Xuewen Yang
Dongliang Xie
Xin Wang
Li Shen
Haozhi Huang
Niranjan Balasubramanian

Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still a challenging problem to search the ﬂexible and accurate architecture that can be shared among multiple tasks. In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL. The main principle of TAAN is to derive ﬂexible activation functions for different tasks from the data with other parameters of the network fully shared. We further propose two functional regularization methods that improve the MTL performance of TAAN. The improved performance of both TAAN and the regularization methods is demonstrated by comprehensive experiments.

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