Author name cluster

Yang Qiu

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

MIRAGE: Towards AI-Generated Image Detection in the Wild

OuCheng Huang
Manxi Lin
Jiexiang Tan
Xiaoxiong Du
Yang Qiu
Junjun Zheng
Xiangheng Kong
Yuning Jiang

The spreading of AI-generated images (AIGI), driven by advances in generative AI, poses a significant threat to in- formation security and public trust. Existing AIGI detectors, while effective against images in clean laboratory settings, fail to generalize to in-the-wild scenarios. These real-world images are noisy, varying from “obviously fake” images to realistic ones derived from multiple generative models and further edited for quality control. We address in-the-wild AIGI detection in this paper. We introduce MIRAGE, a challenging benchmark designed to emulate the complexity of in-the-wild AIGI. MIRAGE is constructed from two sources: (1) a large corpus of Internet-sourced AIGI verified by human experts, and (2) a synthesized dataset created through the collaboration between multiple expert generators, closely simulating the realistic AIGI in the wild. Building on this benchmark, we propose MIRAGE-R1, a vision- language model with heuristic-to-analytic reasoning, a reflective reasoning mechanism for AIGI detection. MIRAGE-R1 is trained in two stages: a supervised-fine-tuning cold start, followed by a reinforcement learning stage. By further adopting a inference-time adaptive thinking strategy, MIRAGE-R1 is able to provide either a quick judgment or a more robust and accurate conclusion, effectively balancing inference speed and performance. Extensive experiments show that our model leads state-of-the-art detectors by 5% and 10% on MIRAGE and public benchmark, respectively.

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NeurIPS Conference 2025 Conference Paper

Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization

Yang Qiu
Yixiong Zou
Jun Wang
Wei Liu
Xiangyu Fu
Ruixuan Li

Out-of-distribution generalization under distributional shifts remains a critical challenge for graph neural networks. Existing methods generally adopt the Invariant Risk Minimization (IRM) framework, requiring costly environment annotations or heuristically generated synthetic splits. To circumvent these limitations, in this work, we aim to develop an IRM-free method for capturing causal subgraphs. We first identify that causal subgraphs exhibit substantially smaller distributional variations than non-causal components across diverse environments, which we formalize as the Invariant Distribution Criterion and theoretically prove in this paper. Building on this criterion, we systematically uncover the quantitative relationship between distributional shift and representation norm for identifying the causal subgraph, and investigate its underlying mechanisms in depth. Finally, we propose an IRM-free method by introducing a norm-guided invariant distribution objective for causal subgraph discovery and prediction. Extensive experiments on two widely used benchmarks demonstrate that our method consistently outperforms state-of-the-art methods in graph generalization. Code is available at https: //github. com/anders1123/IDG.

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NeurIPS Conference 2025 Conference Paper

WHAT MAKES MATH PROBLEMS HARD FOR REINFORCEMENT LEARNING: A CASE STUDY

Ali Shehper
Anibal Medina-Mardones
Lucas Fagan
Bartłomiej Lewandowski
Angus Gruen
Yang Qiu
Piotr Kucharski
Zhenghan Wang

Using a long-standing conjecture from combinatorial group theory, we explore, from multiple perspectives, the challenges of finding rare instances carrying disproportionately high rewards. Based on lessons learned in the context defined by the Andrews--Curtis conjecture, we analyze how reinforcement learning agents handle problems of varying hardness. We also address many mathematical questions as a part of our study. Notably, we demonstrate the length reducibility of all but two presentations in the Akbulut--Kirby series (1981), and resolve various potential counterexamples in the Miller--Schupp series (1991), including three infinite subfamilies.

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AAMAS Conference 2024 Conference Paper

Engaging the Elderly in Exercise with Agents: A Gamified Stationary Bike System for Sarcopenia Management

Yang Qiu
Ping Chen
Huiguo Zhang
Bo Huang
Di Wang
Zhiqi Shen

This paper introduces a portable, gamified exercise system with an embedded agent, specifically designed to aid the elderly in lowerbody workouts using stationary bikes. The system integrates a custom-made Internet of Things (IoT) sensing unit, a gamified application, and an agent-embedded backend platform. By leveraging real-time feedback along with historical user data, the agent actively contributes to exercise safety and adherence by customizing the intensity of workouts and managing break periods. This novel approach aims to make cycling exercise for sarcopenia prevention and intervention more engaging and effective, promoting regular participation and potentially improving health outcomes.

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ECAI Conference 2024 Conference Paper

PAGE: Parametric Generative Explainer for Graph Neural Network

Yang Qiu
Wei Liu 0144
Jun Wang 0018
Ruixuan Li 0001

This article introduces PAGE, a parameterized generative interpretive framework. PAGE is capable of providing faithful explanations for any graph neural network without necessitating prior knowledge or internal details. Specifically, we train the autoencoder to generate explanatory substructures by designing appropriate training strategy. Due to the dimensionality reduction of features in the latent space of the autoencoder, it becomes easier to extract causal features leading to the model’s output, which can be easily employed to generate explanations. To accomplish this, we introduce an additional discriminator to capture the causality between latent causal features and the model’s output. By designing appropriate optimization objectives, the well-trained discriminator can be employed to constrain the encoder in generating enhanced causal features. Finally, these features are mapped to substructures of the input graph through the decoder to serve as explanations. Compared to existing methods, PAGE operates at the sample scale rather than nodes or edges, eliminating the need for perturbation or encoding processes as seen in previous methods. Experimental results on both artificially synthesized and real-world datasets demonstrate that our approach not only exhibits the highest faithfulness and accuracy but also significantly outperforms baseline models in terms of efficiency.

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NeurIPS Conference 2023 Conference Paper

D-Separation for Causal Self-Explanation

Wei Liu
Jun Wang
Haozhao Wang
Ruixuan Li
Zhiying Deng
YuanKai Zhang
Yang Qiu

Rationalization aims to strengthen the interpretability of NLP models by extracting a subset of human-intelligible pieces of their inputting texts. Conventional works generally employ the maximum mutual information (MMI) criterion to find the rationale that is most indicative of the target label. However, this criterion can be influenced by spurious features that correlate with the causal rationale or the target label. Instead of attempting to rectify the issues of the MMI criterion, we propose a novel criterion to uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, which is grounded on our finding that the non-causal features and the target label are \emph{d-separated} by the causal rationale. By minimizing the dependence between the non-selected parts of the input and the target label conditioned on the selected rationale candidate, all the causes of the label are compelled to be selected. In this study, we employ a simple and practical measure for dependence, specifically the KL-divergence, to validate our proposed MCD criterion. Empirically, we demonstrate that MCD improves the F1 score by up to 13. 7% compared to previous state-of-the-art MMI-based methods. Our code is in an anonymous repository: https: //anonymous. 4open. science/r/MCD-CE88.

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