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Mingzhong Wang

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14 papers

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AAAI Conference 2026 Conference Paper

From Tokens to Latent States: Leveraging Pre-trained Language Models for Improving Partially Observable Reinforcement Learning

Meiju Li
Ruixiang Sun
Xin Li
Mingzhong Wang

Partially observable Markov decision processes (POMDPs) present significant challenges for reinforcement learning, as agents must learn optimal policies while maintaining belief states over unobserved environment states based on partial observations. We observe a compelling analogy: large language models (LLMs) autoregressively generate token probability distributions based on preceding context, mirroring how belief states are maintained and updated in POMDPs. This insight motivates leveraging the rich prior knowledge embedded in pre-trained LLMs for latent states estimation from observation-action histories. However, two critical challenges emerge: on the one hand, modality misalignment prevents LLMs from directly encoding visual observations and discrete actions; on the other hand, semantic misalignment exists between observation-action sequences and token sequences. To address these challenges, we introduce a novel framework ELSLLM that employs a Johnson-Lindenstrauss projection (JLP) module to transform input dimensions while preserving state similarity with theoretical guarantees, and utilizes modern Hopfield networks (MHN) to store all word embeddings from pre-trained LLMs as a knowledge repository. Through retrieval and querying mechanisms, ELSLLM achieves token-level knowledge alignment without requiring fine-tuning of the pre-trained LLMs. Extensive experiments on partially observable environments demonstrate that ELSLLM achieves state-of-the-art performance, significantly outperforming baseline methods with and without LSTM memory mechanisms. Our work opens new avenues for integrating pre-trained LLMs with reinforcement learning in partially observable settings.

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AAAI Conference 2026 Conference Paper

Generative Branching for Mixed-Integer Linear Programming

Ruobing Wang
Xin Li
Yangchuan Wang
Zijian Zhang
Mingzhong Wang

Branch-and-bound (B&B) is a fundamental algorithmic framework for solving Mixed-Integer Linear Programming (MILP) problems, where branching decisions critically affect solver efficiency. Recent learning-based methods apply imitation learning to select branching variables, but their deterministic predictions limit exploration and generalization. In this paper, we propose a novel framework that formulates branching variable selection as a conditional generative process, exploring deep-level decision features. Our approach leverages diffusion models to enable diverse and exploratory branching score generation, while consistency modeling distills this process into efficient one-step inference conditioned on the B&B state. This mode allows our method to achieve both high-quality and fast branching decisions, significantly improving the overall performance of branch-and-bound solvers. Extensive experiments on challenging cross-scale and cross-category benchmarks demonstrate that our framework consistently outperforms state-of-the-art imitation learning baselines, delivering substantial improvements in solution quality, computational efficiency, and inference speed.

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AAAI Conference 2026 Conference Paper

Offline Meta-Reinforcement Learning with Flow-Based Task Inference and Adaptive Correction of Feature Overgeneralization

Min Wang
Xin Li
Mingzhong Wang
Hasnaa Bennis

Offline meta-reinforcement learning (OMRL) combines the strengths of learning from diverse datasets in offline RL with the adaptability to new tasks of meta-RL, promising safe and efficient knowledge acquisition by RL agents. However, OMRL still suffers extrapolation errors due to out-of-distribution (OOD) actions, compromised by broad task distributions and Markov Decision Process (MDP) ambiguity in meta-RL setups. Existing research indicates that the generalization of the Q network affects the extrapolation error in offline RL. This paper investigates this relationship by decomposing the Q value into feature and weight components, observing that while decomposition enhances adaptability and convergence in the case of high-quality data, it often leads to policy degeneration or collapse in complex tasks. We observe that decomposed Q values introduce a large estimation bias when the feature encounters OOD samples, a phenomenon we term "feature overgeneralization''. To address this issue, we propose FLORA, which identifies OOD samples by modeling feature distributions and estimating their uncertainties. FLORA integrates a return feedback mechanism to adaptively adjust feature components. Furthermore, to learn precise task representations, FLORA explicitly models the complex task distribution using a chain of invertible transformations. We theoretically and empirically demonstrate that FLORA achieves rapid adaptation and meta-policy improvement compared to baselines across various environments.

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AAAI Conference 2024 Conference Paper

Improving GNN Calibration with Discriminative Ability: Insights and Strategies

Yujie Fang
Xin Li
Qianyu Chen
Mingzhong Wang

The widespread adoption of Graph Neural Networks (GNNs) has led to an increasing focus on their reliability. To address the issue of underconfidence in GNNs, various calibration methods have been developed to gain notable reductions in calibration error. However, we observe that existing approaches generally fail to enhance consistently, and in some cases even deteriorate, GNNs' ability to discriminate between correct and incorrect predictions. In this study, we advocate the significance of discriminative ability and the inclusion of relevant evaluation metrics. Our rationale is twofold: 1) Overlooking discriminative ability can inadvertently compromise the overall quality of the model; 2) Leveraging discriminative ability can significantly inform and improve calibration outcomes. Therefore, we thoroughly explore the reasons why existing calibration methods have ineffectiveness and even degradation regarding the discriminative ability of GNNs. Building upon these insights, we conduct GNN calibration experiments across multiple datasets using a straightforward example model, denoted as DC(GNN). Its excellent performance confirms the potential of integrating discriminative ability as a key consideration in the calibration of GNNs, thereby establishing a pathway toward more effective and reliable network calibration.

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AAAI Conference 2024 Conference Paper

MetaCARD: Meta-Reinforcement Learning with Task Uncertainty Feedback via Decoupled Context-Aware Reward and Dynamics Components

Min Wang
Xin Li
Leiji Zhang
Mingzhong Wang

Meta-Reinforcement Learning (Meta-RL) aims to reveal shared characteristics in dynamics and reward functions across diverse training tasks. This objective is achieved by meta-learning a policy that is conditioned on task representations with encoded trajectory data or context, thus allowing rapid adaptation to new tasks from a known task distribution. However, since the trajectory data generated by the policy may be biased, the task inference module tends to form spurious correlations between trajectory data and specific tasks, thereby leading to poor adaptation to new tasks. To address this issue, we propose the Meta-RL with task unCertAinty feedback through decoupled context-aware Reward and Dynamics components (MetaCARD). MetaCARD distinctly decouples the dynamics and rewards when inferring tasks and integrates task uncertainty feedback from policy evaluation into the task inference module. This design effectively reduces uncertainty in tasks with changes in dynamics or/and reward functions, thereby enabling accurate task identification and adaptation. The experiment results on both Meta-World and classical MuJoCo benchmarks show that MetaCARD significantly outperforms prevailing Meta-RL baselines, demonstrating its remarkable adaptation ability in sophisticated environments that involve changes in both reward functions and dynamics.

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AAAI Conference 2023 Conference Paper

Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding

Qianyu Chen
Xin Li
Kunnan Geng
Mingzhong Wang

Molecular structures and Drug-Drug Interactions (DDI) are recognized as important knowledge to guide medication recommendation (MR) tasks, and medical concept embedding has been applied to boost their performance. Though promising performance has been achieved by leveraging Graph Neural Network (GNN) models to encode the molecular structures of medications or/and DDI, we observe that existing models are still defective: 1) to differentiate medications with similar molecules but different functionality; or/and 2) to properly capture the unintended reactions between drugs in the embedding space. To alleviate this limitation, we propose Carmen, a cautiously designed graph embedding-based MR framework. Carmen consists of four components, including patient representation learning, context information extraction, a context-aware GNN, and DDI encoding. Carmen incorporates the visit history into the representation learning of molecular graphs to distinguish molecules with similar topology but dissimilar activity. Its DDI encoding module is specially devised for the non-transitive interaction DDI graphs. The experiments on real-world datasets demonstrate that Carmen achieves remarkable performance improvement over state-of-the-art models and can improve the safety of recommended drugs with a proper DDI graph encoding.

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TIST Journal 2023 Journal Article

Relation-aware Graph Convolutional Networks for Multi-relational Network Alignment

Yujie Fang
Xin Li
Rui Ye
Xiaoyan Tan
Peiyao Zhao
Mingzhong Wang

The alignment of multiple multi-relational networks, such as knowledge graphs, is vital for many AI applications. In comparison with existing GCNs which cannot fully utilize relational information of multiple types, we propose a relation-aware graph convolutional network (ERGCN), which is equipped with both entity convolution and relation convolution to learn the entity embeddings and relation embeddings simultaneously. The role discrimination and translation property of knowledge graphs are adopted in the entity convolutional process to incorporate the relation information. To facilitate the relation convolution, we construct quadruples to model the connection between a pair of relations thus to determine their neighborhood, which also enables the relation convolution to be conducted in an efficient way. Thereafter, AERGCN, the alignment framework based on ERGCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function, which aims at minimizing the distances between anchors and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the competitive baselines in terms of link prediction, entity alignment, and relation alignment.

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AAAI Conference 2023 Conference Paper

WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series

Fuhao Yang
Xin Li
Min Wang
Hongyu Zang
Wei Pang
Mingzhong Wang

Multivariate time series (MTS) analysis and forecasting are crucial in many real-world applications, such as smart traffic management and weather forecasting. However, most existing work either focuses on short sequence forecasting or makes predictions predominantly with time domain features, which is not effective at removing noises with irregular frequencies in MTS. Therefore, we propose WaveForM, an end-to-end graph enhanced Wavelet learning framework for long sequence FORecasting of MTS. WaveForM first utilizes Discrete Wavelet Transform (DWT) to represent MTS in the wavelet domain, which captures both frequency and time domain features with a sound theoretical basis. To enable the effective learning in the wavelet domain, we further propose a graph constructor, which learns a global graph to represent the relationships between MTS variables, and graph-enhanced prediction modules, which utilize dilated convolution and graph convolution to capture the correlations between time series and predict the wavelet coefficients at different levels. Extensive experiments on five real-world forecasting datasets show that our model can achieve considerable performance improvement over different prediction lengths against the most competitive baseline of each dataset.

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AAAI Conference 2022 Conference Paper

SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning

Hongyu Zang
Xin Li
Mingzhong Wang

This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods. Addressing the computational complexity, stringent assumptions and representation collapse challenges in existing work of bisimulation metric, we devise Simple State Representation (SimSR) operator. SimSR enables us to design a stochastic approximation method that can practically learn the mapping functions (encoders) from observations to latent representation space. In addition to the theoretical analysis and comparison with the existing work, we experimented and compared our work with recent state-of-the-art solutions in visual MuJoCo tasks. The results shows that our model generally achieves better performance and has better robustness and good generalization.

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AAAI Conference 2020 Conference Paper

Universal Value Iteration Networks: When Spatially-Invariant Is Not Universal

Li Zhang
Xin Li
Sen Chen
Hongyu Zang
Jie Huang
Mingzhong Wang

In this paper, we ﬁrst formally deﬁne the problem set of spatially invariant Markov Decision Processes (MDPs), and show that Value Iteration Networks (VIN) and its extensions are computationally bounded to it due to the use of the convolution kernel. To generalize VIN to spatially variant MDPs, we propose Universal Value Iteration Networks (UVIN). In comparison with VIN, UVIN automatically learns a ﬂexible but compact network structure to encode the transition dynamics of the problems and support the differentiable planning module. We evaluate UVIN with both spatially invariant and spatially variant tasks, including navigation in regular maze, chessboard maze, and Mars, and Minecraft item syntheses. Results show that UVIN can achieve similar performance as VIN and its extensions on spatially invariant tasks, and signiﬁcantly outperforms other models on more general problems.

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IJCAI Conference 2019 Conference Paper

A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment

Rui Ye
Xin Li
Yujie Fang
Hongyu Zang
Mingzhong Wang

Alignment of multiple multi-relational networks, such as knowledge graphs, is vital for AI applications. Different from the conventional alignment models, we apply the graph convolutional network (GCN) to achieve more robust network embedding for the alignment task. In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function which aims at minimizing the distances between anchors, and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the state-of-the-art methods in terms of network embedding, entity alignment, and relation alignment.

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IJCAI Conference 2018 Conference Paper

Non-translational Alignment for Multi-relational Networks

Shengnan Li
Xin Li
Rui Ye
Mingzhong Wang
Haiping Su
Yingzi Ou

Most existing solutions for the alignment of multi-relational networks, such as multi-lingual knowledge bases, are ``translation''-based which facilitate the network embedding via the trans-family, such as TransE. However, they cannot address triangular or other structural properties effectively. Thus, we propose a non-translational approach, which aims to utilize a probabilistic model to offer more robust solutions to the alignment task, by exploring the structural properties as well as leveraging on anchors to project each network onto the same vector space during the process of learning the representation of individual networks. The extensive experiments on four multi-lingual knowledge graphs demonstrate the effectiveness and robustness of the proposed method over a set of state-of-the-art alignment methods.

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AAMAS Conference 2009 Conference Paper

Reasoning Intra-Dependency in Commitments for Robust Scheduling

Mingzhong Wang
Kotagiri Ramamohanarao
Jinjun Chen

Commitment-modeled protocols enable ﬂexible and robust interactions among agents. However, existing work has focused on features and capabilities of protocols without considering the active role of agents in them. Therefore, in this paper we propose to augment agents with the ability of reasoning about and manipulating their commitments to maximize the system utility. We adopt a bottom-up approach by ﬁrst investigating the intra-dependency between each commitment’s preconditions and result which leads to a novel classiﬁcation of commitments as well as a formalism to express various types of complex commitment. Within this framework, we provide a set of inference rules to beneﬁt an agent by means of commitment refactoring which enables composition and/or decomposition of its commitments to optimize runtime performance. We also discuss the pros and cons of an agent scheduling and executing its commitments in parallel. We propose a reasoning strategy and an algorithm to minimize possible loss when the commitment is broken and maximize the overall system robustness and performance. Experiments show that concurrent schedules based on the features of commitments can boost the system performance signiﬁcantly.

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AAMAS Conference 2007 Conference Paper

ARTS: Agent-oriented Robust Transactional System*

Mingzhong Wang
Amy Unruh
Kotagiri Ramamohanarao

This paper presents the ARTS (Agent-oriented Robust Transactional System) model, which applies transaction concepts to provide agent developers with high-level support for agent system robustness and reliability. ARTS abstractly considers agents as executors of encapsulated task entities which comply with a set of execution constraints on both normative execution and compensation (repair) semantics. ARTS then defines the task interface in terms of predictable terminating states to support a contract-like interaction among agents. In conjunction with this encapsulation of task semantics, ARTS defines a model for specifying scoped compensation and exception-handling plans for a given task, and for systematically selecting and executing these plans — triggered by subtask events — so that the enclosing task semantics are enforced. These capabilities together define a model that reduces design complexity while increasing system robustness, by allowing an agent developer to compose recursively-defined, atomically-handled tasks.

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