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Weidi Xu

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning

Xuan Zhang
Zhijian Zhou
Weidi Xu
Yanting Miao
Chao Qu
Yuan Qi

Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network’s output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy: the first stage focuses on cultivating basic reasoning abilities, while the second emphasizes systematic learning of logical constraints. To impose hard constraints on neural outputs in the second stage, we formulate the diffusion reasoner as a Markov decision process and innovatively fine-tune it with an improved proximal policy optimization algorithm. We utilize a rule-based reward signal derived from the logical consistency of neural outputs and adopt a flexible strategy to optimize the diffusion reasoner's policy. We evaluate our methodology on some classical symbolic reasoning benchmarks, including Sudoku, Maze, pathfinding and preference learning. Experimental results demonstrate that our approach achieves outstanding accuracy and logical consistency among neural networks.

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ICLR Conference 2024 Conference Paper

LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints

Weidi Xu
Jingwei Wang
Lele Xie
Jianshan He
Hongting Zhou
Taifeng Wang
Xiaopei Wan
Jingdong Chen

Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints. This paper proposes a novel neural layer, LogicMP, which performs mean-field variational inference over a Markov Logic Network (MLN). It can be plugged into any off-the-shelf neural network to encode FOLCs while retaining modularity and efficiency. By exploiting the structure and symmetries in MLNs, we theoretically demonstrate that our well-designed, efficient mean-field iterations greatly mitigate the difficulty of MLN inference, reducing the inference from sequential calculation to a series of parallel tensor operations. Empirical results in three kinds of tasks over images, graphs, and text show that LogicMP outperforms advanced competitors in both performance and efficiency.

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AAAI Conference 2017 Conference Paper

Variational Autoencoder for Semi-Supervised Text Classification

Weidi Xu
Haoze Sun
Chao Deng
Ying Tan

Although semi-supervised variational autoencoder (SemiVAE) works in image classiﬁcation task, it fails in text classiﬁcation task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is veriﬁed that the decoder’s capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) is proposed, which increases the capability by feeding label into its decoder RNN at each time-step. Two speciﬁc decoder structures are investigated and both of them are veriﬁed to be effective. Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques. Experimental results on Large Movie Review Dataset (IMDB) and AG’s News corpus show that the proposed approach signiﬁcantly improves the classiﬁcation accuracy compared with pure-supervised classiﬁers, and achieves competitive performance against previous advanced methods. State-of-the-art results can be obtained by integrating other pretraining-based methods.

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