Author name cluster

Yue Qiu

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

VP-Bench: A Comprehensive Benchmark for Visual Prompting in Multimodal Large Language Models

Mingjie Xu
Jinpeng Chen
Yuzhi Zhao
Jason Chun Lok Li
Yue Qiu
Zekang Du
Mengyang Wu
Pingping Zhang

Multimodal Large Language Models (MLLM) have enabled a wide range of advanced vision-language applications, including fine-grained object recognition and contextual understanding. When querying specific regions or objects in an image, human users naturally use "Visual Prompts" (VP) like bounding boxes to provide reference. However, no existing benchmark systematically evaluates the ability of MLLMs to interpret such VPs. This gap raises uncertainty about whether current MLLMs can effectively recognize VPs, an intuitive prompting method for humans, and utilize them to solve problems. To address this limitation, we introduce VP-Bench, aiming to assess MLLMs’ capability in VP perception and utilization. VP-Bench employs a two-stage evaluation framework: Stage 1 examines models’ ability to perceive VPs in natural scenes, utilizing 100K visualized prompts spanning 8 shapes and 355 attribute combinations. Stage 2 investigates the impact of VPs on downstream tasks, measuring their effectiveness in real-world problem-solving scenarios. Using VP-Bench, we evaluate 21 MLLMs, including proprietary systems (e.g., GPT-4o) and open-source models (e.g., InternVL-2.5 and Qwen2.5-VL). In addition, we conduct a comprehensive analysis of the factors influencing VP understanding, such as attribute variations and model scale. VP-Bench establishes a new reference framework for studying MLLMs’ ability to comprehend and resolve grounded referring questions.

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YNIMG Journal 2025 Journal Article

Exploring the impact of APOE ɛ4 on functional connectivity in Alzheimer’s disease across cognitive impairment levels

Kangli Dong
Wei Liang
Ting Hou
Zhijie Lu
Yixuan Hao
Chenrui Li
Yue Qiu
Nan Kong

The apolipoprotein E (APOE) ɛ4 allele is a recognized genetic risk factor for Alzheimer's Disease (AD). Studies have shown that APOE ɛ4 mediates the modulation of intrinsic functional brain networks in cognitively normal individuals and significantly disrupts the whole-brain topological structure in AD patients. However, how APOE ɛ4 regulates brain functional connectivity (FC) and consequently affects the levels of cognitive impairment in AD patients remains unknown. In this study, we systematically analyzed functional magnetic resonance imaging (fMRI) data from two distinct cohorts: an In-house dataset includes 59 AD patients (73.37 ± 6.42 years), and the ADNI dataset includes 117 AD patients (74.91 ± 7.91 years). Experimental comparisons were conducted by grouping AD patients based on both APOE ɛ4 status and cognitive impairment levels of AD. Network-Based Statistic (NBS) method and the Graph Neural Network Explainer (GNN-Explainer) were combined to identify significant FC changes across different comparisons. Importantly, the GNN-Explainer method was introduced as an enhancement over the NBS method to better model complex high-order nonlinear characteristics for discovering FC features that significantly contribute to classification tasks. The results showed that APOE ɛ4 primarily influenced temporal lobe FCs, while it influenced different cognitive impairment levels of AD by adjusting prefrontal-parietal FCs. These findings were validated by p-values < 0.05 from NBS method, and 5-fold cross-validation along with ablation studies from the GNN-Explainer method. In conclusion, our findings provide new insights into the role of APOE ɛ4 in altering FC dynamics during the progression of AD, highlighting potential targets for early intervention.

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ECAI Conference 2025 Conference Paper

On Perturbed Natural Adaptive Gradient Descent and Its Application in Portfolio Optimization

Yi Cai
Huili Liang
Yue Qiu
Xiao Wang
Tian Xie
Zixuan Zhao

In this paper, we introduce the Perturbed Natural Adaptive Gradient Descent (PN-AdaGrad) method, a novel optimization algorithm that combines the principles of Natural gradient descent and adaptive gradient descent on Riemannian manifold. We provide a rigorous theoretical analysis of the PN-AdaGrad method, proving its convergence to critical point of the objective function under mild assumptions. To validate the practical effectiveness of the PN-AdaGrad method, we verify our algorithm on real-world datasets in the context of portfolio optimization. Portfolio optimization involves selecting the optimal allocation of assets to maximize returns while minimizing risk. Our experiments show that the PN-AdaGrad method outperforms traditional gradient descent and other state-of-the-art optimization algorithms.

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ICML Conference 2024 Conference Paper

Conformal Prediction for Deep Classifier via Label Ranking

Jianguo Huang
Huajun Xi
Linjun Zhang
Huaxiu Yao
Yue Qiu
Hongxin Wei

Conformal prediction is a statistical framework that generates prediction sets containing ground-truth labels with a desired coverage guarantee. The predicted probabilities produced by machine learning models are generally miscalibrated, leading to large prediction sets in conformal prediction. To address this issue, we propose a novel algorithm named $\textit{Sorted Adaptive Prediction Sets}$ (SAPS), which discards all the probability values except for the maximum softmax probability. The key idea behind SAPS is to minimize the dependence of the non-conformity score on the probability values while retaining the uncertainty information. In this manner, SAPS can produce compact prediction sets and communicate instance-wise uncertainty. Extensive experiments validate that SAPS not only lessens the prediction sets but also broadly enhances the conditional coverage rate of prediction sets.

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AAAI Conference 2024 Conference Paper

Decoding Global Preferences: Temporal and Cooperative Dependency Modeling in Multi-Agent Preference-Based Reinforcement Learning

Tianchen Zhu
Yue Qiu
Haoyi Zhou
Jianxin Li

Designing accurate reward functions for reinforcement learning (RL) has long been challenging. Preference-based RL (PbRL) offers a promising approach by using human preferences to train agents, eliminating the need for manual reward design. While successful in single-agent tasks, extending PbRL to complex multi-agent scenarios is nontrivial. Existing PbRL methods lack the capacity to comprehensively capture both temporal and cooperative aspects, leading to inadequate reward functions. This work introduces an advanced multi-agent preference learning framework that effectively addresses these limitations. Based on a cascading Transformer architecture, our approach captures both temporal and cooperative dependencies, alleviating issues related to reward uniformity and intricate interactions among agents. Experimental results demonstrate substantial performance improvements in multi-agent cooperative tasks, and the reconstructed reward function closely resembles expert-defined reward functions. The source code is available at https://github.com/catezi/MAPT.

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NeurIPS Conference 2024 Conference Paper

From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning

Pusen Dong
Tianchen Zhu
Yue Qiu
Haoyi Zhou
Jianxin Li

Safe reinforcement learning (RL) requires the agent to finish a given task while obeying specific constraints. Giving constraints in natural language form has great potential for practical scenarios due to its flexible transfer capability and accessibility. Previous safe RL methods with natural language constraints typically need to design cost functions manually for each constraint, which requires domain expertise and lacks flexibility. In this paper, we harness the dual role of text in this task, using it not only to provide constraint but also as a training signal. We introduce the Trajectory-level Textual Constraints Translator (TTCT) to replace the manually designed cost function. Our empirical results demonstrate that TTCT effectively comprehends textual constraint and trajectory, and the policies trained by TTCT can achieve a lower violation rate than the standard cost function. Extra studies are conducted to demonstrate that the TTCT has zero-shot transfer capability to adapt to constraint-shift environments.

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IJCAI Conference 2023 Conference Paper

Towards Long-delayed Sparsity: Learning a Better Transformer through Reward Redistribution

Tianchen Zhu
Yue Qiu
Haoyi Zhou
Jianxin Li

Recently, Decision Transformer (DT) pioneered the offline RL into a contextual conditional sequence modeling paradigm, which leverages self-attended autoregression to learn from global target rewards, states, and actions. However, many applications have a severe delay of the above signals, such as the agent can only obtain a reward signal at the end of each trajectory. This delay causes an unwanted bias cumulating in autoregressive learning global signals. In this paper, we focused its virtual example on episodic reinforcement learning with trajectory feedback. We propose a new reward redistribution algorithm for learning parameterized reward functions, and it decomposes the long-delayed reward onto each timestep. To improve the redistributing's adaptation ability, we formulate the previous decomposition as a bi-level optimization problem for global optimal. We extensively evaluate the proposed method on various benchmarks and demonstrate an overwhelming performance improvement under long-delayed settings.

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