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Tongya Zheng

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19 papers

2 author rows

AAAI Conference 2026 Conference Paper

Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning

Qinghong Guo
Yu Wang
Ji Cao
Tongya Zheng
Junshu Dai
Bingde Hu
Shunyu Liu
Canghong Jin

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised paradigm. However, spatial heterogeneity and temporal dynamics of road networks raise severe challenges to the neighborhood smoothing mechanism of self-supervised GNNs. To address these issues, we propose a Dual-branch Spatial-Temporal self-supervised representation framework for enhanced road representations, termed as DST. On one hand, DST designs a mix-hop transition matrix for graph convolution to incorporate dynamic relations of roads from trajectories. Besides, DST contrasts road representations of the vanilla road network against that of hypergraphs in a spatial self-supervised way. The hypergraph is newly built based on three types of hyperedges to capture long-range relations. On the other hand, DST performs next token prediction as the temporal self-supervised task on the sequences of traffic dynamics based on a causal Transformer, which is further regularized by differentiating traffic modes of weekdays from those of weekends. Extensive experiments against state-of-the-art methods verify the superiority of our proposed framework. Moreover, the comprehensive spatiotemporal modeling facilitates DST to excel in zero-shot learning scenarios.

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AAAI Conference 2026 Conference Paper

Neural Graph Navigation for Intelligent Subgraph Matching

Yuchen Ying
Yiyang Dai
Wenda Li
Wenjie Huang
Rui Wang
Tongya Zheng
Yu Wang
Hanyang Yuan

Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges due to the dramatically growing search space. Existing methods address this problem within a filtering-ordering-enumeration framework, in which the enumeration stage recursively matches the query graph against the candidate subgraphs of the data graph. However, the lack of awareness of subgraph structural patterns leads to a costly brute-force enumeration, thereby critically motivating the need for intelligent navigation in subgraph matching. To address this challenge, we propose Neural Graph Navigation (NeuGN), a neuro-heuristic framework that transforms brute-force enumeration into neural-guided search by integrating neural navigation mechanisms into the core enumeration process. By preserving heuristic-based completeness guarantees while incorporating neural intelligence, NeuGN significantly reduces the First Match Steps by up to 98.2% compared to state-of-the-art methods across six real-world datasets.

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AAMAS Conference 2026 Conference Paper

Parallelized Planning-Acting for Multi-Agent LLM Systems in Minecraft

Yaoru Li
Shunyu Liu
Tongya Zheng
Li Sun
Mingli Song

Recent advancements in Large Language Model (LLM)-based Multi- Agent Systems (MAS) have demonstrated remarkable potential for tackling complex decision-making tasks. However, existing frameworks inevitably rely on serialized execution paradigms, where agentsmustcompletesequentialLLMplanningbeforetakingaction. This fundamental constraint severely limits real-time responsivenessandadaptation, whichiscrucialindynamicenvironmentswith ever-changing scenarios like Minecraft. In this paper, we propose a novel parallelized planning-acting framework for LLM-based MAS, featuring a dual-thread architecture with interruptible execution to enable concurrent planning and acting. Specifically, our framework comprisestwocorethreads: (1)aplanningthread drivenbyacentralized memory system, maintaining synchronization of environmental states and agent communication to support dynamic decisionmaking; and (2) an acting thread equipped with a comprehensive skill library, enabling automated task execution through recursive decomposition. Extensive experiments on Minecraft demonstrate the effectiveness of the proposed framework.

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AAAI Conference 2025 Conference Paper

Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning

Yaoquan Wei
Shunyu Liu
Jie Song
Tongya Zheng
Kaixuan Chen
Mingli Song

Action advising endeavors to leverage supplementary guidance from expert teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement Learning (DRL). Previous agent-specific action advising methods are hindered by imperfections in the agent itself, while agent-agnostic approaches exhibit limited adaptability to the learning agent. In this study, we propose a novel framework called Agent-Aware trAining yet Agent-Agnostic Action Advising (A7) to strike a balance between the two. The underlying concept of A7 revolves around utilizing the similarity of state features as an indicator for soliciting advice. However, unlike prior methodologies, the measurement of state feature similarity is performed by neither the error-prone learning agent nor the agent-agnostic advisor. Instead, we employ a proxy model to extract state features that are both discriminative (adaptive to the agent) and generally applicable (robust to agent noise). Furthermore, we utilize behavior cloning to train a model for reusing advice and introduce an intrinsic reward for the advised samples to incentivize the utilization of expert guidance. Experiments are conducted on the GridWorld, LunarLander, and six prominent scenarios from Atari games. The results demonstrate that A7 significantly accelerates the learning process and surpasses existing methods (both agent- specific and agent-agnostic) by a substantial margin. Our code will be made publicly available.

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AAMAS Conference 2025 Conference Paper

CADP: Towards Better Centralized Learning for Decentralized Execution in MARL

Yihe Zhou
Shunyu Liu
Yunpeng Qing
Tongya Zheng
Kaixuan Chen
Jie Song
Mingli Song

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IJCAI Conference 2025 Conference Paper

CADP: Towards Better Centralized Learning for Decentralized Execution in MARL

Yihe Zhou
Shunyu Liu
Yunpeng Qing
Tongya Zheng
Kaixuan Chen
Jie Song
Mingli Song

Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use additional global state information to guide training in a centralized way and make their own decisions only based on decentralized local policies. Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents from adopting global cooperative information from each other during centralized training. Therefore, we argue that the existing CTDE framework cannot fully utilize global information for training, leading to an inefficient joint exploration and perception, which can degrade the final performance. In this paper, we introduce a novel Centralized Advising and Decentralized Pruning (CADP) framework for MARL, that not only enables an efficacious message exchange among agents during training but also guarantees the independent policies for decentralized execution. Firstly, CADP endows agents the explicit communication channel to seek and take advice from different agents for more centralized training. To further ensure the decentralized execution, we propose a smooth model pruning mechanism to progressively constrain the agent communication into a closed one without degradation in agent cooperation capability. Empirical evaluations on different benchmarks and across various MARL backbones demonstrate that the proposed framework achieves superior performance compared with the state-of-the-art counterparts. Our code is available at https: //github. com/zyh1999/CADP

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AAAI Conference 2025 Conference Paper

Cooperative Policy Agreement: Learning Diverse Policy for Offline MARL

Yihe Zhou
Yuxuan Zheng
Yue Hu
Kaixuan Chen
Tongya Zheng
Jie Song
Mingli Song
Shunyu Liu

Offline Multi-Agent Reinforcement Learning (MARL) aims to learn optimal joint policies from pre-collected datasets without further interaction with the environment. Despite the encouraging results achieved so far, we identify the policy mismatch problem that arises from employing diverse offline MARL datasets, a highly important ingredient for cooperative generalization yet largely overlooked by existing literature. Specifically, in the case that offline datasets exhibit various optimal joint policies, policy mismatch often occurs when individual actions from different optimal joint actions are combined in a way that results in a suboptimal joint action. In this paper, we introduce a novel Cooperative Policy Agreement (CPA) method, that not only mitigates the policy mismatch problem but also learns to generate diverse joint policies. CPA firstly introduces an autoregressive decision-making mechanism among agents during offline training. This mechanism enables agents to access the actions previously taken by other agents, thereby facilitating effective joint policy matching. Moreover, diverse joint policies can be directly obtained through sequential action sampling from the autoregressive model. Then we further incorporate a policy agreement mechanism to convert these autoregressive joint policies into decentralized policies with a non-autoregressive form, while still ensuring the diversity of the generated policies. This mechanism guarantees that the proposed CPA adheres to the Centralized Training with Decentralized Execution (CTDE) constraint. Experiments conducted on various benchmarks demonstrate that CPA yields superior performance to state-of-the-art competitors.

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AAAI Conference 2025 Conference Paper

Disentangled Table-Graph Representation for Interpretable Transmission Line Fault Location

Na Yu
Yutong Deng
Shunyu Liu
Kaixuan Chen
Tongya Zheng
Mingli Song

The fault location task in power grids is crucial for maintaining social order and ensuring public safety. However, existing methods that rely on tabular state records often neglect the intrinsic topological influences of transmission lines, resulting in a segmented approach to fault location that consists of multiple stages. In this paper, we propose an Disentangled Table-Graph representation framework, termed DTG, which integrates fault location tasks at coarse-grained line levels and fine-grained point levels within an end-to-end learning paradigm. Our innovative disentanglement strategy produces interpretable attribution coefficients that connect tabular records and transmission line topology, thereby facilitating fault location at both line- and point-levels. The joint prediction tasks designed around our disentangled tabular graph representation promote mutual information exchange between features and topology of transmission lines in an interpretable manner. Experimental results on the 7-bus system, 36-bus system and a realistic 325-bus system in China demonstrate that the proposed method adapt to different topological structures and handle different types of faults. Compared to traditional methods, DTG4Power achieves high accuracy in both fault lines and fault points.

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IJCAI Conference 2025 Conference Paper

Efficient Dynamic Graphs Learning with Refined Batch Parallel Training

Zhengzhao Feng
Rui Wang
Longjiao Zhang
Tongya Zheng
Ziqi Huang
Mingli Song

Memory-based temporal graph neural networks (MTGNN) use node memory to store historical information, enabling efficient processing of large dynamic graphs through batch parallel training, with larger batch sizes leading to increased training efficiency. However, this approach overlooks the interdependency among edges within the same batch, leading to outdated memory states and reduced training accuracy. Previous studies have attempted to mitigate this issue through methods such as measuring memory loss, overlap training, and additional compensation modules. Despite these efforts, challenges persist, including imprecise coarse-grained memory loss measurement and ineffective compensation modules. To address these challenges, we propose the Refined Batch parallel Training (RBT) framework, which accurately evaluates intra-batch information loss and optimizes batch partitioning to minimize loss, enhancing the training process's effectiveness and efficiency. RBT also includes a precise and efficient memory compensation algorithm. Experimental results demonstrate RBT's superior performance compared to existing MTGNN frameworks like TGL, ETC, and PRES in terms of training efficiency and accuracy across various dynamic graph datasets. Our code is made publicly available at https: //github. com/fengwudi/RBT.

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ICLR Conference 2025 Conference Paper

From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks

Jie Yang
Yuwen Wang
Kaixuan Chen 0004
Tongya Zheng
Yihe Zhou
Zhenbang Xiao
Ji Cao 0001
Mingli Song

Interpretable Graph Neural Networks (GNNs) aim to reveal the underlying reasoning behind model predictions, attributing their decisions to specific subgraphs that are informative. However, existing subgraph-based interpretable methods suffer from an overemphasis on local structure, potentially overlooking long-range dependencies within the entire graphs. Although recent efforts that rely on graph coarsening have proven beneficial for global interpretability, they inevitably reduce the graphs to a fixed granularity. Such an inflexible way can only capture graph connectivity at a specific level, whereas real-world graph tasks often exhibit relationships at varying granularities (e.g., relevant interactions in proteins span from functional groups, to amino acids, and up to protein domains). In this paper, we introduce a novel Tree-like Interpretable Framework (TIF) for graph classification, where plain GNNs are transformed into hierarchical trees, with each level featuring coarsened graphs of different granularity as tree nodes. Specifically, TIF iteratively adopts a graph coarsening module to compress original graphs (i.e., root nodes of trees) into increasingly coarser ones (i.e., child nodes of trees), while preserving diversity among tree nodes within different branches through a dedicated graph perturbation module. Finally, we propose an adaptive routing module to identify the most informative root-to-leaf paths, providing not only the final prediction but also the multi-granular interpretability for the decision-making process. Extensive experiments on the graph classification benchmarks with both synthetic and real-world datasets demonstrate the superiority of TIF in interpretability, while also delivering a competitive prediction performance akin to the state-of-the-art counterparts.

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AAAI Conference 2025 Conference Paper

Global Attribute-Association Pattern Aggregation for Graph Fraud Detection

Mingjiang Duan
Da He
Tongya Zheng
Lingxiang Jia
Mingli Song
Xinyu Wang
Zunlei Feng

Fraud is increasingly prevalent, and its patterns are frequently changing, posing challenges for fraud detection methods such as random forests and Graph Neural Networks (GNNs), which rely on bin-based and mixture features separately. The former may lose crucial graph-associated features, while the latter face incorrect feature fusion. To overcome these limitations, we propose an approach based on attribute-association pattern that leverages the distinct attribute and association patterns differentiating fraudulent from benign behaviors, to enhance fraud detection capabilities. Attribute features are adaptively split into separate bins to eliminate incorrect attribute fusion and combine association patterns through graph neighbor message passing, thereby deriving attribute-association pattern features. Using the learned attribute-association patterns, the fraud patterns between a single pattern and the patterns across the entire graph are globally aggregated. Extensive experiments comparing our approach with 24 methods on 7 datasets demonstrate that the proposed method achieves SOTA performance.

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AAAI Conference 2025 Conference Paper

Holistic Semantic Representation for Navigational Trajectory Generation

Ji Cao
Tongya Zheng
Qinghong Guo
Yu Wang
Junshu Dai
Shunyu Liu
Jie Yang
Jie Song

Trajectory generation has garnered significant attention from researchers in the field of spatio-temporal analysis, as it can generate substantial synthesized human mobility trajectories that enhance user privacy and alleviate data scarcity. However, existing trajectory generation methods often focus on improving trajectory generation quality from a singular perspective, lacking a comprehensive semantic understanding across various scales. Consequently, we are inspired to develop a HOlistic SEmantic Representation (HOSER) framework for navigational trajectory generation. Given an origin-and-destination (OD) pair and the starting time point of a latent trajectory, we first propose a Road Network Encoder to expand the receptive field of road- and zone-level semantics. Second, we design a Multi-Granularity Trajectory Encoder to integrate the spatio-temporal semantics of the generated trajectory at both the point and trajectory levels. Finally, we employ a Destination-Oriented Navigator to seamlessly integrate destination-oriented guidance. Extensive experiments on three real-world datasets demonstrate that HOSER outperforms state-of-the-art baselines by a significant margin. Moreover, the model's performance in few-shot learning and zero-shot learning scenarios further verifies the effectiveness of our holistic semantic representation.

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IJCAI Conference 2025 Conference Paper

Odyssey: Empowering Minecraft Agents with Open-World Skills

Shunyu Liu
Yaoru Li
Kongcheng Zhang
Zhenyu Cui
Wenkai Fang
Yuxuan Zheng
Tongya Zheng
Mingli Song

Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving basic programmatic tasks, e. g. , material collection and tool-crafting following the Minecraft tech-tree, treating the ObtainDiamond task as the ultimate goal. This limitation stems from the narrowly defined set of actions available to agents, requiring them to learn effective long-horizon strategies from scratch. Consequently, discovering diverse gameplay opportunities in the open world becomes challenging. In this work, we introduce Odyssey, a new framework that empowers Large Language Model (LLM)-based agents with open-world skills to explore the vast Minecraft world. Odyssey comprises three key parts: (1) An interactive agent with an open-world skill library that consists of 40 primitive skills and 183 compositional skills. (2) A fine-tuned LLaMA-3 model trained on a large question-answering dataset with 390k+ instruction entries derived from the Minecraft Wiki. (3) A new agent capability benchmark includes the long-term planning task, the dynamic-immediate planning task, and the autonomous exploration task. Extensive experiments demonstrate that the proposed Odyssey framework can effectively evaluate different capabilities of LLM-based agents. All datasets, model weights, and code are publicly available to motivate future research on more advanced autonomous agent solutions.

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NeurIPS Conference 2025 Conference Paper

SALoM: Structure Aware Temporal Graph Networks with Long-Short Memory Updater

Hanwen Liu
Longjiao Zhang
Rui Wang
Tongya Zheng
Sai Wu
Chang Yao
Mingli Song

Dynamic graph learning is crucial for accurately modeling complex systems by integrating topological structure and temporal information within graphs. While memory-based methods are commonly used and excel at capturing short-range temporal correlations, they struggle with modeling long-range dependencies, harmonizing long-range and short-range correlations, and integrating structural information effectively. To address these challenges, we present SALoM: Structure Aware Temporal Graph Networks with Long-Short Memory Updater. SALoM features a memory module that addresses gradient vanishing and information forgetting, enabling the capture of long-term dependencies across various time scales. Additionally, SALoM utilizes a long-short memory updater (LSMU) to dynamically balance long-range and short-range temporal correlations, preventing over-generalization. By integrating co-occurrence encoding and LSMU through information bottleneck-based fusion, SALoM effectively captures both the structural and temporal information within graphs. Experimental results across various graph datasets demonstrate SALoM's superior performance, achieving state-of-the-art results in dynamic graph link prediction. Our code is openly accessible at https: //github. com/wave5418/SALoM.

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NeurIPS Conference 2025 Conference Paper

SeRL: Self-play Reinforcement Learning for Large Language Models with Limited Data

Wenkai Fang
Shunyu Liu
Yang Zhou
Kongcheng Zhang
Tongya Zheng
Kaixuan Chen
Mingli Song
Dacheng Tao

Recent advances have demonstrated the effectiveness of Reinforcement Learning (RL) in improving the reasoning capabilities of Large Language Models (LLMs). However, existing works inevitably rely on high-quality instructions and verifiable rewards for effective training, both of which are often difficult to obtain in specialized domains. In this paper, we propose Self-play Reinforcement Learning (SeRL) to bootstrap LLM training with limited initial data. Specifically, SeRL comprises two complementary modules: self-instruction and self-rewarding. The former module generates additional instructions based on the available data at each training step, employing comprehensive online filtering strategies to ensure instruction quality, diversity, and difficulty. The latter module introduces a simple yet effective majority-voting mechanism to estimate response rewards for additional instructions, eliminating the need for external annotations. Finally, SeRL performs conventional RL based on the generated data, facilitating iterative self-play learning. Extensive experiments on various reasoning benchmarks and across different LLM backbones demonstrate that the proposed SeRL yields results superior to its counterparts and achieves performance on par with those obtained by high-quality data with verifiable rewards. Our code is available at https: //github. com/wantbook-book/SeRL.

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NeurIPS Conference 2025 Conference Paper

Tree of Preferences for Diversified Recommendation

Hanyang Yuan
Ning Tang
Tongya Zheng
Jiarong Xu
Xintong Hu
Renhong Huang
Shunyu Liu
Jiacong Hu

Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items. Existing approaches predominantly aim to infer the diversity of user preferences from observed user feedback. Nonetheless, due to inherent data biases, the observed data may not fully reflect user interests, where underexplored preferences can be overwhelmed or remain unmanifested. Failing to capture these preferences can lead to suboptimal diversity in recommendations. To fill this gap, this work aims to study diversified recommendation from a data-bias perspective. Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach that utilizes LLMs' expertise to uncover underexplored user preferences from observed behavior, ultimately providing diverse and relevant recommendations. To achieve this, we first introduce Tree of Preferences (ToP), an innovative structure constructed to model user preferences from coarse to fine. ToP enables LLMs to systematically reason over the user's rationale behind their behavior, thereby uncovering their underexplored preferences. To guide diversified recommendations using uncovered preferences, we adopt a data-centric approach, identifying candidate items that match user preferences and generating synthetic interactions that reflect underexplored preferences. These interactions are integrated to train a general recommender for diversification. Moreover, we scale up overall efficiency by dynamically selecting influential users during optimization. Extensive evaluations of both diversity and relevance show that our approach outperforms existing methods in most cases and achieves near-optimal performance in others, with reasonable inference latency.

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AAAI Conference 2024 Conference Paper

DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection

Mingjiang Duan
Tongya Zheng
Yang Gao
Gang Wang
Zunlei Feng
Xinyu Wang

Fraud detection has increasingly become a prominent research field due to the dramatically increased incidents of fraud. The complex connections involving thousands, or even millions of nodes, present challenges for fraud detection tasks. Many researchers have developed various graph-based methods to detect fraud from these intricate graphs. However, those methods neglect two distinct characteristics of the fraud graph: the non-additivity of certain attributes and the distinguishability of grouped messages from neighbor nodes. This paper introduces the Dynamic Grouping Aggregation Graph Neural Network (DGA-GNN) for fraud detection, which addresses these two characteristics by dynamically grouping attribute value ranges and neighbor nodes. In DGA-GNN, we initially propose the decision tree binning encoding to transform non-additive node attributes into bin vectors. This approach aligns well with the GNN’s aggregation operation and avoids nonsensical feature generation. Furthermore, we devise a feedback dynamic grouping strategy to classify graph nodes into two distinct groups and then employ a hierarchical aggregation. This method extracts more discriminative features for fraud detection tasks. Extensive experiments on five datasets suggest that our proposed method achieves a 3% ~ 16% improvement over existing SOTA methods. Code is available at https://github.com/AtwoodDuan/DGA-GNN.

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ECAI Conference 2024 Conference Paper

Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation

Wenda Li 0003
Kaixuan Chen 0004
Shunyu Liu 0001
Tongya Zheng
Wenjie Huang
Mingli Song

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https: //github. com/l-wd/LGMformer.

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AAAI Conference 2023 Conference Paper

Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

Shunyu Liu
Yihe Zhou
Jie Song
Tongya Zheng
Kaixuan Chen
Tongtian Zhu
Zunlei Feng
Mingli Song

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to promote diverse behaviors among agents, while existing methods directly encourage the diversity of learned agent networks with various strategies. However, we argue that these dedicated designs for agent networks are still limited by the indistinguishable VD network, leading to homogeneous agent behaviors and thus downgrading the cooperation capability. In this paper, we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly boosting the credit-level distinguishability of the VD network to break the bottleneck of multi-agent diversity. Specifically, our approach leverages contrastive learning to maximize the mutual information between the temporal credits and identity representations of different agents, encouraging the full expressiveness of credit assignment and further the emergence of individualities. The algorithm implementation of the proposed CIA module is simple yet effective that can be readily incorporated into various VD architectures. Experiments on the SMAC benchmarks and across different VD backbones demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/CIA.

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