Guoqing Wang

AAAI Conference 2026 Conference Paper

Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning

• Yuqin Dai
• Shuo Yang
• Guoqing Wang
• Yong Deng
• Zhanwei Zhang
• Jun Yin
• Pengyu Zeng
• Zhenzhe Ying

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unreliable or misleading content that can degrade retrieval accuracy, and the underutilization of web tools, which, if effectively employed, could enhance query precision and help mitigate this noise, ultimately improving retrieval results in RAG systems. To address these issues, we propose WebFilter, a novel RAG framework that generates source-restricted queries and filters out unreliable content. This approach combines a retrieval filtering mechanism with a behavior- and outcome-driven reward strategy, optimizing both query formulation and retrieval outcomes. Extensive experiments demonstrate that WebFilter improves answer quality and retrieval precision, outperforming existing RAG methods on both in-domain and out-of-domain benchmarks.

AAAI Conference 2026 Conference Paper

MetaEval: Measuring the Discrimination of Benchmarks for Efficient LLM Evaluation

• Zhuo Wang
• Wen Wu
• Guoqing Wang
• Guangze Ye
• Zhenxiao Cheng

Benchmarks serve as standardized test systems to distinguish capabilities among large language models (LLMs). Discriminative items enable high-ability LLMs to favor correct answers, while causing low-ability models to assign lower plausibility to these answers and tend toward incorrect answers. Current methods for assessing benchmark quality primarily focus on coverage of difficulty levels and task diversity, yet lack direct quantification of discrimination—the core metric. Furthermore, large-scale benchmarks incur high evaluation costs. Although heuristic methods can reduce item counts to some extent, they cannot guarantee preservation of the benchmark’s original discriminative properties. To address these limitations, we propose MetaEval, a meta-evaluation framework designed to precisely quantify per-item discrimination and enable efficient assessment. Central to MetaEval is our novel Signal Detection and Item Response (SD-IR) model, which simulates LLMs’ detection of correct answers (signals) by representing each model’s perception through two latent ability states: “known” and “unknown”. For any item, discrimination is quantified as the difference in signal plausibility between these states. Leveraging these discrimination metrics, MetaEval introduces two strategies to replicate full-benchmark results using minimal subsets for efficient evaluation: (1) Distilling metaBench: a compact subset that retains discriminative power by removing redundant items; (2) Predicting performance on full-benchmark based on metaBench’s discrimination. Experiments across five benchmarks confirm that high-discrimination items capture greater performance variation among LLMs, align more closely with full-benchmark rankings, and exhibit superior predictive ability. Notably, in the best case, MetaEval achieves accurate full-benchmark estimation using only 2.5% of items, substantially reducing evaluation costs while preserving reliability.

AAAI Conference 2026 Conference Paper

Seeing Beyond Illusion: Generalized and Efficient Mirror Detection

• Mingfeng Zha
• Guoqing Wang
• Tianyu Li
• Wei Dong
• Peng Wang
• Yang Yang

Reflective imaging enables the mirror imagings and physical entities to possess identical attributes, e.g., color and shape. Current mirror detection (MD) methods primarily rely on designing functional components to establish the correlation and disparities between the imagings and entities, thereby identifying the mirror regions. However, the exploration of extended scenes with dynamic content changes is rarely investigated. Therefore, we propose the MirrorSAM designed for MD based on the Segment Anything Model (SAM). Specifically, due to the varying reflections produced by mirrors in different positions and the complex visual space that interferes with localization, we design the hierarchical mixture of direction experts (HMDE) in the low-rank space to reduce biases towards entities in SAM and dynamically adjust experts based on the input scene. We observe differences in depth between mirrors and adjacent areas, and propose the depth token calibration (DTC), which introduces a learnable depth token to generate the depth map and serve as an error correction factor. We further formulate the selective pixel-prototype contrastive (SPPC) loss, selecting partially confusable samples to promote the decoupling of mirror and non-mirror representations. Extensive experiments conducted on four mirror benchmarks and two settings demonstrate that our approach surpasses state-of-the-art methods with few trainable parameters and FLOPs. We further extend to four transparent surface benchmarks to validate generalization.

AAAI Conference 2025 Conference Paper

Decoupling Metacognition from Cognition: A Framework for Quantifying Metacognitive Ability in LLMs

• Guoqing Wang
• Wen Wu
• Guangze Ye
• Zhenxiao Cheng
• Xi Chen
• Hong Zheng

Large Language Models (LLMs) are known to hallucinate facts and make non-factual statements which can undermine trust in their output. The essence of hallucination lies in the absence of metacognition in LLMs, namely the understanding of their own cognitive processes. However, there has been limited research on quantitatively measuring metacognition within LLMs. Drawing inspiration from cognitive psychology theories, we first quantify the metacognitive ability of LLMs as their ability to evaluate the correctness of responses through confidence. Subsequently, we introduce a general framework called DMC designed to decouple metacognitive ability and cognitive ability. This framework tackles the challenge of noisy quantification caused by the coupling of metacognition and cognition in current research, such as calibration-based metrics. Specifically, the DMC framework comprises two key steps. Initially, the framework tasks the LLM with failure prediction, aiming to evaluate the model's performance in predicting failures, a performance jointly determined by both cognitive and metacognitive abilities of the LLM. Following this, the framework disentangles metacognitive ability and cognitive ability based on the failure prediction performance, providing a quantification of the LLM's metacognitive ability independent of cognitive influences. Experiments conducted on eight datasets across five domains reveal that (1) Our proposed DMC framework effectively separates the metacognition and cognition of LLMs; (2) Various confidence elicitation methods impact the quantification of metacognitve ability differently; (3) Stronger metacognitive ability are exhibited by LLMs with better overall performance; (4) Enhancing metacognition holds promise for alleviating hallucination issues.

NeurIPS Conference 2025 Conference Paper

Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling

• Dehao Zhang
• Malu Zhang
• Shuai Wang
• Jingya Wang
• Wenjie Wei
• Zeyu Ma
• Guoqing Wang
• Yang Yang

The explosive growth in sequence length has intensified the demand for effective and efficient long sequence modeling. Benefiting from intrinsic oscillatory membrane dynamics, Resonate-and-Fire (RF) neurons can efficiently extract frequency components from input signals and encode them into spatiotemporal spike trains, making them well-suited for long sequence modeling. However, RF neurons exhibit limited effective memory capacity and a trade-off between energy efficiency and training speed on complex temporal tasks. Inspired by the dendritic structure of biological neurons, we propose a Dendritic Resonate-and-Fire (D-RF) model, which explicitly incorporates a multi-dendritic and soma architecture. Each dendritic branch encodes specific frequency bands by utilizing the intrinsic oscillatory dynamics of RF neurons, thereby collectively achieving comprehensive frequency representation. Furthermore, we introduce an adaptive threshold mechanism into the soma structure. This mechanism adjusts the firing threshold according to historical spiking activity, thereby reducing redundant spikes while maintaining training efficiency in long-sequence tasks. Extensive experiments demonstrate that our method maintains competitive accuracy while substantially ensuring sparse spikes without compromising computational efficiency during training. These results underscore its potential as an effective and efficient solution for long sequence modeling on edge platforms.

AAAI Conference 2025 Conference Paper

Disentangled Modeling of Preferences and Social Influence for Group Recommendation

• Guangze Ye
• Wen Wu
• Guoqing Wang
• Xi Chen
• Hong Zheng
• Liang He

The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregating group preferences. Actually, social influence is also an important factor in modeling users' contributions to the final group decision. However, existing methods either neglect the social influence of individual members or bundle preferences and social influence together as a unified representation. As a result, these models emphasize the preferences of the majority within the group rather than the actual interaction items, which we refer to as the preference bias issue in GR. Moreover, the self-supervised learning (SSL) strategies they designed to address the issue of group data sparsity fail to account for users' contextual social weights when regulating group representations, leading to suboptimal results. To tackle these issues, we propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec). Concretely, we first design a user-level disentangling network to disentangle the preferences and social influence of group members with separate embedding propagation schemes based on (hyper)graph convolution networks. We then introduce a social-based contrastive learning strategy, selectively excluding user nodes based on their social importance to enhance group representations and alleviate the group-level data sparsity issue. The experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two real-world datasets.

NeurIPS Conference 2025 Conference Paper

Hybrid Boundary Physics-Informed Neural Networks for Solving Navier-Stokes Equations with Complex Boundary

• ChuYu Zhou
• Tianyu Li
• Chenxi Lan
• Rongyu Du
• Guoguo Xin
• Wei Li
• Guoqing Wang
• Xun Liu

Physics-informed neural networks (PINN) have achieved notable success in solving partial differential equations (PDE), yet solving the Navier-Stokes equations (NSE) with complex boundary conditions remains a challenging task. In this paper, we introduce a novel Hybrid Boundary PINN (HB-PINN) method that combines a pretrained network for efficient initialization with a boundary-constrained mechanism. The HB-PINN method features a primary network focused on inner domain points and a distance metric network that enhances predictions at the boundaries, ensuring accurate solutions for both boundary and interior regions. Comprehensive experiments have been conducted on the NSE under complex boundary conditions, including the 2D cylinder wake flow and the 2D blocked cavity flow with a segmented inlet. The proposed method achieves state-of-the-art (SOTA) performance on these benchmark scenarios, demonstrating significantly improved accuracy over existing PINN-based approaches.

AAAI Conference 2025 Conference Paper

Leveraging Asynchronous Spiking Neural Networks for Ultra Efficient Event-Based Visual Processing

• DingYi Zeng
• Yuchen Wang
• Honglin Cao
• Wanlong Liu
• Yichen Xiao
• ChengzhuoLu
• Wenyu Chen
• Malu Zhang

Event cameras encode visual information by generating asynchronous and sparse event streams, which hold great potential for low latency and low power consumption. Despite many successful implementations of event camera-based applications, most of them accumulate the events into frames and then utilize conventional frame-based computer vision algorithms. These frame-based methods, though typically effective, diminish the inherent advantages of the event camera's low latency and low power consumption. To solve the above problems, we propose ASGCN, which efficiently processes data on an event-by-event basis and dynamically evolves into a corresponding dynamic representation, enabling low latency and high sparsity of data representation. The sparsity computation is further improved by introducing brain-inspired spiking neural networks, resulting in low power consumption for ASGCN. Extensive and diverse experiments demonstrate the energy efficiency and low latency advantages of our processing pipeline. Especially on real-world event camera datasets, our pipeline consumes more than 10,000 times less energy and achieves similar performance compared to current frame-based methods.

NeurIPS Conference 2025 Conference Paper

S$^2$NN: Sub-bit Spiking Neural Networks

• Wenjie Wei
• Malu Zhang
• Jieyuan (Eric) Zhang
• Ammar Belatreche
• Shuai Wang
• Yimeng Shan
• Hanwen Liu
• Honglin Cao

Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence, but their continued scaling poses challenges for resource-limited deployment. Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks. To further explore the compression and acceleration potential of SNNs, we propose Sub-bit Spiking Neural Networks (S$^2$NNs) that represent weights with less than one bit. Specifically, we first establish an S$^2$NN baseline by leveraging the clustering patterns of kernels in well-trained binary SNNs. This baseline is highly efficient but suffers from \textit{outlier-induced codeword selection bias} during training. To mitigate this issue, we propose an \textit{outlier-aware sub-bit weight quantization} (OS-Quant) method, which optimizes codeword selection by identifying and adaptively scaling outliers. Furthermore, we propose a \textit{membrane potential-based feature distillation} (MPFD) method, improving the performance of highly compressed S$^2$NN via more precise guidance from a teacher model. Extensive results on vision reveal that S$^2$NN outperforms existing quantized SNNs in both performance and efficiency, making it promising for edge computing applications.

TCS Journal 2025 Journal Article

Target influence maximization problem under avoiding unexpected users interruption in online social network

• Xiaoping Zhu
• Jianming Zhu
• Priyanshi Garg
• Guoqing Wang

AAAI Conference 2025 Conference Paper

Towards Generalizable Multi-Camera 3D Object Detection via Perspective Rendering

• Hao Lu
• Yunpeng Zhang
• Guoqing Wang
• Qing Lian
• Dalong Du
• Ying-Cong Chen

Detecting and localizing objects in 3D space using multiple cameras, known as Multi-Camera 3D Object Detection (MC3D-Det), has gained prominence with the advent of bird's-eye view (BEV) approaches. However, these methods often struggle with the serious domain gaps caused by various viewpoints and environments between the training and testing domains. To address this challenge, we propose a novel framework that aligns 3D detection with 2D camera plane results by perspective rendering, thus achieving consistent and accurate results when facing serious domain shifts. Our approach consists of two main steps in both source and target domains: 1) rendering diverse view maps from BEV features by leveraging implicit foreground volumes and 2) rectifying the perspective bias of these maps. This design promotes the learning of perspective- and context-independent features, crucial for accurate object detection across varying viewpoints, camera parameters, and environmental conditions. Notably, our model-agnostic approach preserves the original network structure without incurring additional inference costs, facilitating seamless integration across various models and simplifying deployment. Worth noting is that our approach achieves satisfactory results in real data when trained only with virtual datasets, eliminating the need for real scene annotations. Experimental results on both Domain Generalization (DG) and Unsupervised Domain Adaptation (UDA) demonstrate its effectiveness.

ICRA Conference 2025 Conference Paper

VSS-SLAM: Voxelized Surfel Splatting for Geometally Accurate SLAM

• Xuanhua Chen
• Yunzhou Zhang
• Zhiyao Zhang
• Guoqing Wang
• Bin Zhao
• Xingshuo Wang

[1] Visual Simultaneous Localization and Mapping (SLAM) helps robots estimate their poses and perceive the environment in unknown settings. Recent work has demonstrated that implicit neural radiance fields and 3D Gaussian Splatting (3DGS) offer higher fidelity scene representation than traditional map representations. We propose VSS-SLAM, which utilizes voxelized surfels as the map representation for incremental mapping in unknown environments. This representation effectively addresses the issue of redundant and disordered primitives encountered in previous methods, thereby enhancing geometric accuracy during reconstruction. Specifically, our approach divides the scene using voxels and stores geometric and appearance information in feature vectors at the voxel vertices. Before rendering, these feature vectors are decoded to generate the corresponding surfels. Additionally, we align camera poses through image and depth rendering. Extensive experiments on the Replica and TUM-RGBD datasets demonstrate that VSS-SLAM delivers high-fidelity reconstruction and accurate pose estimation in both simulated and real-world environments. Source code will soon be available.

ICML Conference 2024 Conference Paper

Open-Vocabulary Calibration for Fine-tuned CLIP

• Shuoyuan Wang
• Jindong Wang 0001
• Guoqing Wang
• Bob Zhang 0001
• Kaiyang Zhou
• Hongxin Wei

Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving downstream performance of VLMs, particularly on parameter-efficient fine-tuning methods like prompt learning. However, a crucial aspect that has been largely overlooked is the confidence calibration problem in fine-tuned VLMs, which could greatly reduce reliability when deploying such models in the real world. This paper bridges the gap by systematically investigating the confidence calibration problem in the context of prompt learning and reveals that existing calibration methods are insufficient to address the problem, especially in the open-vocabulary setting. To solve the problem, we present a simple and effective approach called Distance-Aware Calibration (DAC), which is based on scaling the temperature using as guidance the distance between predicted text labels and base classes. The experiments with 7 distinct prompt learning methods applied across 11 diverse downstream datasets demonstrate the effectiveness of DAC, which achieves high efficacy without sacrificing the inference speed.

NeurIPS Conference 2024 Conference Paper

Physics-Constrained Comprehensive Optical Neural Networks

• Yanbing Liu
• Jianwei Qin
• Yan Liu
• Xi Yue
• Xun Liu
• Guoqing Wang
• Tianyu Li
• Fangwei Ye

With the advantages of low latency, low power consumption, and high parallelism, optical neural networks (ONN) offer a promising solution for time-sensitive and resource-limited artificial intelligence applications. However, the performance of the ONN model is often diminished by the gap between the ideal simulated system and the actual physical system. To bridge the gap, this work conducts extensive experiments to investigate systematic errors in the optical physical system within the context of image classification tasks. Through our investigation, two quantifiable errors—light source instability and exposure time mismatches—significantly impact the prediction performance of ONN. To address these systematic errors, a physics-constrained ONN learning framework is constructed, including a well designed loss function to mitigate the effect of light fluctuations, a CCD adjustment strategy to alleviate the effects of exposure time mismatches and a ’physics-prior based’ error compensation network to manage other systematic errors, ensuring consistent light intensity across experimental results and simulations. In our experiments, the proposed method achieved a test classification accuracy of 96. 5% on the MNIST dataset, a substantial improvement over the 61. 6% achieved with the original ONN. For the more challenging QuickDraw16 and Fashion MNIST datasets, experimental accuracy improved from 63. 0% to 85. 7% and from 56. 2% to 77. 5%, respectively. Moreover, the comparison results further demonstrate the effectiveness of the proposed physics-constrained ONN learning framework over state-of-the-art ONN approaches. This lays the groundwork for more robust and precise optical computing applications.

AAAI Conference 2024 Conference Paper

ScanERU: Interactive 3D Visual Grounding Based on Embodied Reference Understanding

• Ziyang Lu
• Yunqiang Pei
• Guoqing Wang
• Peiwei Li
• Yang Yang
• Yinjie Lei
• Heng Tao Shen

Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearance. To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding. Specifically, a new task termed Embodied Reference Understanding (ERU) is first designed for this concern. Then a new dataset called ScanERU is constructed to evaluate the effectiveness of this idea. Different from existing datasets, our ScanERU dataset is the first to cover semi-synthetic scene integration with textual, real-world visual, and synthetic gestural information. Additionally, this paper formulates a heuristic framework based on attention mechanisms and human body movements to enlighten the research of ERU. Experimental results demonstrate the superiority of the proposed method, especially in the recognition of multiple identical objects. Our codes and dataset are available in the ScanERU repository.

AAAI Conference 2024 Conference Paper

Weakly-Supervised Mirror Detection via Scribble Annotations

• Mingfeng Zha
• Yunqiang Pei
• Guoqing Wang
• Tianyu Li
• Yang Yang
• Wenbin Qian
• Heng Tao Shen

Mirror detection is of great significance for avoiding false recognition of reflected objects in computer vision tasks. Existing mirror detection frameworks usually follow a supervised setting, which relies heavily on high quality labels and suffers from poor generalization. To resolve this, we instead propose the first weakly-supervised mirror detection framework and also provide the first scribble-based mirror dataset. Specifically, we relabel 10,158 images, most of which have a labeled pixel ratio of less than 0.01 and take only about 8 seconds to label. Considering that the mirror regions usually show great scale variation, and also irregular and occluded, thus leading to issues of incomplete or over detection, we propose a local-global feature enhancement (LGFE) module to fully capture the context and details. Moreover, it is difficult to obtain basic mirror structure using scribble annotation, and the distinction between foreground (mirror) and background (non-mirror) features is not emphasized caused by mirror reflections. Therefore, we propose a foreground-aware mask attention (FAMA), integrating mirror edges and semantic features to complete mirror regions and suppressing the influence of backgrounds. Finally, to improve the robustness of the network, we propose a prototype contrast loss (PCL) to learn more general foreground features across images. Extensive experiments show that our network outperforms relevant state-of-the-art weakly supervised methods, and even some fully supervised methods. The dataset and codes are available at https://github.com/winter-flow/WSMD.

YNIMG Journal 2021 Journal Article

Moderated t-tests for group-level fMRI analysis

• Guoqing Wang
• John Muschelli
• Martin A. Lindquist