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Jilong Wang

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4 papers

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AAAI Conference 2026 Conference Paper

CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolution

Baoliang Tian
Yuxuan Si
Jilong Wang
LingYao Li
Zhongyuan Bao
Zineng Zhou
Tao Wang
Sixu Li

Multimodal Large Language Models are primarily trained and evaluated on aligned image-text pairs, which leaves their ability to detect and resolve real-world inconsistencies largely unexplored. In open-domain applications visual and textual cues often conflict, requiring models to perform structured reasoning beyond surface-level alignment. We introduce CrossCheck-Bench, a diagnostic benchmark for evaluating contradiction detection in multimodal inputs. The benchmark adopts a hierarchical task framework covering three levels of reasoning complexity and defines seven atomic capabilities essential for resolving cross-modal inconsistencies. CrossCheck-Bench includes 15k question-answer pairs sourced from real-world artifacts with synthetically injected contradictions. The dataset is constructed through a multi-stage annotation pipeline involving more than 450 expert hours to ensure semantic validity and calibrated difficulty across perception, integration, and reasoning. We evaluate 13 state-of-the-art vision-language models and observe a consistent performance drop as tasks shift from perceptual matching to logical contradiction detection. Most models perform well on isolated entity recognition but fail when multiple clues must be synthesized for conflict reasoning. Capability-level analysis further reveals uneven skill acquisition, especially in tasks requiring multi-step inference or rule-based validation. Additional probing shows that conventional prompting strategies such as Chain-of-Thought and Set-of-Mark yield only marginal gains. By contrast, methods that interleave symbolic reasoning with grounded visual processing achieve more stable improvements. These results highlight a persistent bottleneck in multimodal reasoning and suggest new directions for building models capable of robust cross-modal verification.

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AAAI Conference 2026 Conference Paper

Gait Transformer: End-to-End Transformer Backbone for Gait Recognition

Saihui Hou
Wenpeng Lang
Jilong Wang
Yan Huang
Liang Wang
Yongzhen Huang

Gait recognition has emerged as a promising biometric technique for long-distance and non-intrusive human identification. While Transformers have revolutionized vision tasks, their adaptation to gait recognition remains underexplored due to domain-specific challenges such as sparse silhouette modality, spatial-temporal dynamics, fine-grained motion cues, and limited training data. In this paper, we propose Gait Transformer (GaT), an end-to-end Transformer backbone specifically tailored for silhouette-based gait recognition. GaT introduces three key components: (1) a hybrid patch embedding module that combines convolutional stems with group-batch normalization to enhance structural preservation; (2) a decomposed token mixer that explicitly models both short-range and long-range dependencies across spatial-temporal dimensions; and (3) a hybrid positional encoding strategy that integrates absolute, relative, and rotary embeddings to support efficient training under data scarcity. Without relying on any pretraining, GaT achieves state-of-the-art performance on Gait3D, GREW, and CCGR-MINI.

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AAMAS Conference 2023 Conference Paper

Cedric: A Collaborative DDoS Defense System Using Credit

Jiawei Li
Hui Wang
Jilong Wang

Distributed denial of service (DDoS) is one of the most common and damaging cyber attacks, and its impact grows rapidly with the massive use of Internet. Collaborative DDoS defense across countries enables faster and more efficient DDoS attack mitigation. Collaboration requires countries that are not target victims to help detect and block the malicious flow, but selfish countries may refuse to do so because lacking individual gain compared with individual cost. In this paper, we model a stochastic game where selfish countries interact repeatedly and form coalitions to defend DDoS attacks. We design a multi-agent system, Cedric, to simulate and solve this complex stochastic game. Each agent adopts Q-learning to find their long-term optimal strategies, and credits are used to encourage efficient collaboration. The Shapley Value based reward assignment of Cedric satisfies several desired properties about fairness and stability. Simulations with trace data of over 7 years’ global DDoS attacks support the superiority of Cedric empirically.

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NeurIPS Conference 2020 Conference Paper

Graph Stochastic Neural Networks for Semi-supervised Learning

Haibo Wang
Chuan Zhou
Xin Chen
Jia Wu
Shirui Pan
Jilong Wang

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure. To improve the rigidness and inflexibility of deterministic classification functions, this paper proposes a novel framework named Graph Stochastic Neural Networks (GSNN), which aims to model the uncertainty of the classification function by simultaneously learning a family of functions, i. e. , a stochastic function. Specifically, we introduce a learnable graph neural network coupled with a high-dimensional latent variable to model the distribution of the classification function, and further adopt the amortised variational inference to approximate the intractable joint posterior for missing labels and the latent variable. By maximizing the lower-bound of the likelihood for observed node labels, the instantiated models can be trained in an end-to-end manner effectively. Extensive experiments on three real-world datasets show that GSNN achieves substantial performance gain in different scenarios compared with stat-of-the-art baselines.

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