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Xiang Wang

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66 papers

2 author rows

EAAI Journal 2026 Journal Article

A lightweight brain tumor segmentation network based on cross-modality feature fusion

Yawen Fan
Chenziyi Huang
Xiang Wang
Chaoyuan Wang
Quan Zhou
Jianxin Chen

In clinical diagnosis and treatment, the segmentation of brain tumors using multi-modality Magnetic Resonance Imaging (MRI) is crucial. Effectively leveraging information from different modalities is challenging, as they have varying sensitivities to tumor regions. To address this, we propose a four-branch encoder structure that incorporates distinct attention mechanisms for each modality, enabling the learning of discriminative features. Furthermore, we introduce a novel lightweight Cross-Modal Feature Fusion (CMFF) module to enhance feature representation. Additionally, we reduce the number of convolutional layers to prevent overfitting. Experimental results on the Brain Tumor Segmentation (BraTS) 2021 Challenge demonstrate that our framework achieves superior segmentation performance with a significantly reduced parameter count. Specifically, our method obtains average Dice scores of 93. 12% for whole tumor, 89. 50% for tumor core, and 85. 92% for enhancing tumor, using only 3. 66 M parameters. These results highlight the model’s strong performance and efficiency compared to existing baseline and state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Causal-HalBench: Uncovering LVLMs Object Hallucinations Through Causal Intervention

Zhe Xu
Zhicai Wang
Junkang Wu
Jinda Lu
Xiang Wang

Large Vision-Language Models (LVLMs) often suffer from object hallucination, making erroneous judgments about the presence of objects in images. We propose this primarily stems from spurious correlations arising when models strongly associate highly co-occurring objects during training, leading to hallucinated objects influenced by visual context. Current benchmarks mainly focus on hallucination detection but lack a formal characterization and quantitative evaluation of spurious correlations in LVLMs. To address this, we introduce causal analysis into the object recognition scenario of LVLMs, establishing a Structural Causal Model (SCM). Utilizing the language of causality, we formally define spurious correlations arising from co-occurrence bias. To quantify the influence induced by these spurious correlations, we develop Causal-HalBench, a benchmark specifically constructed with counterfactual samples and integrated with comprehensive causal metrics designed to assess model robustness against spurious correlations. Concurrently, we propose an extensible pipeline for the construction of these counterfactual samples, leveraging the capabilities of proprietary LVLMs and Text-to-Image (T2I) models for their generation. Our evaluations on mainstream LVLMs using Causal-HalBench demonstrate these models exhibit susceptibility to spurious correlations, albeit to varying extents.

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AAAI Conference 2026 Conference Paper

Delayed Feedback Modeling with Influence Functions

Chenlu Ding
Jiancan Wu
Yancheng Yuan
Cunchun Li
Xiang Wang
Dingxian Wang
Frank Yang
Andrew Rabinovich

In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial. A major challenge is delayed feedback, where conversions may occur long after user interactions, leading to incomplete recent data and biased model training. Existing solutions partially mitigate this issue but often rely on auxiliary models, making them computationally inefficient and less adaptive to user interest shifts. We propose IF-DFM, an Influence Function-empowered for Delayed Feedback Modeling which estimates the impact of newly arrived and delayed conversions on model parameters, enabling efficient updates without full retraining. By reformulating the inverse Hessian-vector-product as an optimization problem, IF-DFM achieves a favorable trade-off between scalability and effectiveness. Experiments on benchmark datasets show that IF-DFM outperforms prior methods in both accuracy and adaptability.

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AAAI Conference 2026 Conference Paper

Differentially Private Subspace Fine-Tuning for Large Language Models

Lele Zheng
Xiang Wang
Tao Zhang
Yang Cao
Ke Cheng
Yulong Shen

Fine-tuning large language models on downstream tasks is crucial for realizing their cross-domain potential but often relies on sensitive data, raising privacy concerns. Differential privacy (DP) offers rigorous privacy guarantees and has been widely adopted in fine-tuning; however, naively injecting noise across the high-dimensional parameter space creates perturbations with large norms, degrading performance and destabilizing training. To address this issue, we propose DP-SFT, a two-stage subspace fine-tuning method that substantially reduces noise magnitude while preserving formal DP guarantees. Our intuition is that, during fine-tuning, significant parameter updates lie within a low-dimensional, task-specific subspace, while other directions change minimally. Hence, we only inject DP noise into this subspace to protect privacy without perturbing irrelevant parameters. In phase one, we identify the subspace by analyzing principal gradient directions to capture task-specific update signals. In phase two, we project full gradients onto this subspace, add DP noise, and map the perturbed gradients back to the original parameter space for model updates, markedly lowering noise impact. Experiments on multiple datasets demonstrate that DP-SFT enhances accuracy and stability under rigorous DP constraints, accelerates convergence, and achieves substantial gains over DP fine-tuning baselines.

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AAAI Conference 2026 Conference Paper

Interpretable Reward Model via Sparse Autoencoder

Shuyi Zhang
Wei Shi
Sihang Li
Jiayi Liao
Tao Liang
Hengxing Cai
Xiang Wang

Large language models (LLMs) have been widely deployed across numerous fields. Reinforcement Learning from Human Feedback (RLHF) leverages reward models (RMs) as proxies for human preferences to align LLM behaviors with human values, making the accuracy, reliability, and interpretability of RMs critical for effective alignment. However, traditional RMs lack interpretability, offer limited insight into the reasoning behind reward assignments, and are inflexible toward user preference shifts. While recent multidimensional RMs aim for improved interpretability, they often fail to provide feature-level attribution and require costly annotations. To overcome these limitations, we introduce the Sparse Autoencoder-Enhanced Reward Model (SARM), a novel architecture that integrates a pretrained Sparse Autoencoder (SAE) into a reward model. SARM maps the hidden activations of LLM-based RM into an interpretable, sparse, and monosemantic feature space, from which a scalar head aggregates feature activations to produce transparent and conceptually meaningful reward scores. Empirical evaluations demonstrate that SARM facilitates direct feature-level attribution of reward assignments, allows dynamic adjustment to preference shifts, and achieves superior alignment performance compared to conventional reward models.

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AAAI Conference 2026 Conference Paper

Learning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment

Wenti Yin
Huaxin Zhang
Xiang Wang
Yuqing Lu
Yicheng Zhang
Bingquan Gong
Jialong Zuo
Li Yu

Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories. However, their objectives may tend to detect the most salient response segments, while neglecting to mine diverse normal patterns separated from anomalies, and are prone to category confusion due to similar appearance, leading to unsatisfactory fine-grained classification results. Therefore, we propose a novel Disentangled Semantic Alignment Network (DSANet) to explicitly separate abnormal and normal features from coarse-grained and fine-grained aspects, enhancing the distinguishability. Specifically, at the coarse-grained level, we introduce a self-guided normality modeling branch that reconstructs input video features under the guidance of learned normal prototypes, encouraging the model to exploit normality cues inherent in the video, thereby improving the temporal separation of normal patterns and anomalous events. At the fine-grained level, we present a decoupled contrastive semantic alignment mechanism, which first temporally decomposes each video into event-centric and background-centric components using frame-level anomaly scores and then applies visual-language contrastive learning to enhance class-discriminative representations. Comprehensive experiments on two standard benchmarks, namely XD-Violence and UCF-Crime, demonstrate that DSANet outperforms existing state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input

Chenxu Li
Zhicai Wang
Yuan Sheng
Xingyu Zhu
Yanbin Hao
Xiang Wang

Multimodal Large Language Models (MLLMs) increasingly support dynamic image resolutions. However, current evaluation paradigms primarily assess semantic performance, overlooking the critical question of resolution robustness - whether performance remains stable across varying input resolutions. To address this gap, we introduce Res-Bench, a comprehensive benchmark comprising 14,400 samples across 12 resolution levels and six core capability dimensions. We designed a novel evaluation framework that goes beyond traditional accuracy metrics to capture performance stability. This framework introduces multiple robustness metrics: Spearman's correlation for assessing resolution-performance trends, and Absolute/Relative Continuous Error (ACE/RCE) for measuring performance volatility. Using these metrics, we conducted a large-scale evaluation of leading MLLMs. Our analysis encompasses: (1) model-centric and task-centric robustness examination, (2) investigation of preprocessing strategies including padding and super-resolution, and (3) exploration of fine-tuning for stability enhancement.

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NeurIPS Conference 2025 Conference Paper

3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding

Chang Wu
Zhiyuan Liu
Wen Shu
Liang Wang
Yanchen Luo
Wenqiang Lei
Yatao Bian
Junfeng Fang

Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL). However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial, primarily due to two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for reconstructing re-masked atoms. To address these challenges, we propose 3D-GSRD: a 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation of 3D-GSRD lies in its Selective Re-mask Decoding (SRD), which re-masks only 3D-relevant information from encoder representations while preserving the 2D graph structures. This SRD is synergistically integrated with a 3D Relational-Transformer (3D-ReTrans) encoder alongside a structure-independent decoder. We analyze that SRD, combined with the structure-independent decoder, enhances the encoder's role in MRL. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-the-art on 7 out of 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at https: //github. com/WuChang0124/3D-GSRD.