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Jingjing Gong

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12 papers

2 author rows

ICLR Conference 2025 Conference Paper

A Periodic Bayesian Flow for Material Generation

Hanlin Wu
Yuxuan Song
Jingjing Gong
Ziyao Cao
Yawen Ouyang
Jianbing Zhang
Hao Zhou 0012
Wei-Ying Ma

Generative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown early promise in modeling crystal distribution. More recently, Bayesian Flow Networks were introduced to aggregate noisy latent variables, resulting in a variance-reduced parameter space that has been shown to be advantageous for modeling Euclidean data distributions with structural constraints (Song, et al.,2023). Inspired by this, we seek to unlock its potential for modeling variables located in non-Euclidean manifolds e.g. those within crystal structures, by overcoming challenging theoretical issues. We introduce CrysBFN, a novel crystal generation method by proposing a periodic Bayesian flow, which essentially differs from the original Gaussian-based BFN by exhibiting non-monotonic entropy dynamics. To successfully realize the concept of periodic Bayesian flow, CrysBFN integrates a new entropy conditioning mechanism and empirically demonstrates its significance compared to time-conditioning. Extensive experiments over both crystal ab initio generation and crystal structure prediction tasks demonstrate the superiority of CrysBFN, which consistently achieves new state-of-the-art on all benchmarks. Surprisingly, we found that CrysBFN enjoys a significant improvement in sampling efficiency, e.g., 200x speedup (10 v.s. 2000 steps network forwards) compared with previous Diffusion-based methods on MP-20 dataset.

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NeurIPS Conference 2025 Conference Paper

Accelerating 3D Molecule Generative Models with Trajectory Diagnosis

Zhilong Zhang
Yuxuan Song
Yichun Wang
Jingjing Gong
Hanlin Wu
Dongzhan Zhou
Hao Zhou
Wei-Ying Ma

Geometric molecule generative models have found expanding applications across various scientific domains, but their generation inefficiency has become a critical bottleneck. Through a systematic investigation of the generative trajectory, we discover a unique challenge for molecule geometric graph generation: generative models require determining the permutation order of atoms in the molecule before refining its atomic feature values. Based on this insight, we decompose the generation process into permutation phase and adjustment phase, and propose a geometric-informed prior and consistency parameter objective to accelerate each phase. Extensive experiments demonstrate that our approach achieves competitive performance with approximately 10 sampling steps, 7. 5 × faster than previous state-of-the-art models and approximately 100 × faster than diffusion-based models, offering a significant step towards scalable molecular generation.

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NeurIPS Conference 2025 Conference Paper

ShortListing Model: A Streamlined Simplex Diffusion for Discrete Variable Generation

Yuxuan Song
Zhe Zhang
Yu Pei
Jingjing Gong
Qiying Yu
Zheng Zhang
Mingxuan Wang
Hao Zhou

Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting Model (SLM), a novel simplex-based diffusion model inspired by progressive candidate pruning. SLM operates on simplex centroids, reducing generation complexity and enhancing scalability. Additionally, SLM incorporates a flexible implementation of classifier-free guidance, enhancing unconditional generation performance. Extensive experiments on DNA promoter and enhancer design, protein design, character-level and large-vocabulary language modeling demonstrate the competitive performance and strong potential of SLM. Our code can be found at https: //github. com/GenSI-THUAIR/SLM.

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ICML Conference 2025 Conference Paper

Smooth Interpolation for Improved Discrete Graph Generative Models

Yuxuan Song
Juntong Shi
Jingjing Gong
Minkai Xu
Stefano Ermon
Hao Zhou 0012
Wei-Ying Ma

Though typically represented by the discrete node and edge attributes, the graph topological information can be sufficiently captured by the graph spectrum in a continuous space. It is believed that incorporating the continuity of graph topological information into the generative process design could establish a superior paradigm for graph generative modeling. Motivated by such prior and recent advancements in the generative paradigm, we propose Graph Bayesian Flow Networks (GraphBFN) in this paper, a principled generative framework that designs an alternative generative process emphasizing the dynamics of topological information. Unlike recent discrete-diffusion-based methods, GraphBFNemploys the continuous counts derived from sampling infinite times from a categorical distribution as latent to facilitate a smooth decomposition of topological information, demonstrating enhanced effectiveness. To effectively realize the concept, we further develop an advanced sampling strategy and new time-scheduling techniques to overcome practical barriers and boost performance. Through extensive experimental validation on both generic graph and molecular graph generation tasks, GraphBFN could consistently achieve superior or competitive performance with significantly higher training and sampling efficiency.

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ICLR Conference 2025 Conference Paper

Steering Protein Family Design through Profile Bayesian Flow

Jingjing Gong
Yu Pei
Siyu Long
Yuxuan Song
Zhe Zhang
Wenhao Huang 0001
Ziyao Cao
Shuyi Zhang

Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for specifically generative modeling of protein families. ProfileBFN extends the discrete Bayesian Flow Network from an MSA profile perspective, which can be trained on single protein sequences by regarding it as a degenerate profile, thereby achieving efficient protein family design by avoiding large-scale MSA data construction and training. Empirical results show that ProfileBFN has a profound understanding of proteins. When generating diverse and novel family proteins, it can accurately capture the structural characteristics of the family. The enzyme produced by this method is more likely than the previous approach to have the corresponding function, offering better odds of generating diverse proteins with the desired functionality.

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NeurIPS Conference 2025 Conference Paper

World-aware Planning Narratives Enhance Large Vision-Language Model Planner

Junhao Shi
Zhaoye Fei
Siyin Wang
Qipeng Guo
Jingjing Gong
Xipeng Qiu

Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current approaches rely on environment-agnostic imitation learning that disconnects instructions from environmental contexts, causing models to struggle with context-sensitive instructions and rely on supplementary cues rather than visual reasoning during long-horizon interactions. In this work, we propose World-Aware Planning Narrative Enhancement (WAP), a framework that infuses LVLMs with comprehensive environmental understanding through four cognitive capabilities (visual appearance modeling, spatial reasoning, functional abstraction, and syntactic grounding) while developing and evaluating models using only raw visual observations through curriculum learning. Evaluations on the EB-ALFRED benchmark demonstrate substantial improvements, with Qwen2. 5-VL achieving a 60. 7 absolute improvement in task success rates—particularly in commonsense reasoning (+60. 0) and long-horizon planning (+70. 0). Notably, our enhanced open-source models outperform proprietary systems like GPT-4o and Claude-3. 5-Sonnet by a large margin.

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ICML Conference 2024 Conference Paper

MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

Yanru Qu
Keyue Qiu
Yuxuan Song
Jingjing Gong
Jiawei Han 0001
Mingyue Zheng
Hao Zhou 0012
Wei-Ying Ma

Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6. 59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0. 84 kcal/mol). Code is available at https: //github. com/AlgoMole/MolCRAFT.

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ICLR Conference 2024 Conference Paper

Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks

Yuxuan Song
Jingjing Gong
Hao Zhou 0012
Mingyue Zheng
Jingjing Liu
Wei-Ying Ma

Advanced generative model (\textit{e.g.}, diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the \textit{multi-modality} and \textit{noise-sensitive} nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geometry by modeling diverse modalities in the differentiable parameter space of distributions. GeoBFN maintains the SE-(3) invariant density modeling property by incorporating equivariant inter-dependency modeling on parameters of distributions and unifying the probabilistic modeling of different modalities. Through optimized training and sampling techniques, we demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D molecule generation benchmarks in terms of generation quality (90.87\% molecule stability in QM9 and 85.6\% atom stability in GEOM-DRUG\footnote{The scores are reported at 1k sampling steps for fair comparison, and our scores could be further improved if sampling sufficiently longer steps.}). GeoBFN can also conduct sampling with any number of steps to reach an optimal trade-off between efficiency and quality (\textit{e.g.}, 20$\times$ speedup without sacrificing performance).

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ICML Conference 2023 Conference Paper

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D

Bo Qiang
Yuxuan Song
Minkai Xu
Jingjing Gong
Bowen Gao
Hao Zhou 0012
Wei-Ying Ma
Yanyan Lan

Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especially when generating large molecules. Fragment-based molecule generation is a promising strategy, however, it is nontrivial to be adapted for 3D non-autoregressive generations because of the combinational optimization problems. In this paper, we utilize a coarse-to-fine strategy to tackle this problem, in which a Hierarchical Diffusion-based model (i. e. HierDiff) is proposed to preserve the validity of local segments without relying on autoregressive modeling. Specifically, HierDiff first generates coarse-grained molecule geometries via an equivariant diffusion process, where each coarse-grained node reflects a fragment in a molecule. Then the coarse-grained nodes are decoded into fine-grained fragments by a message-passing process and a newly designed iterative refined sampling module. Lastly, the fine-grained fragments are then assembled to derive a complete atomic molecular structure. Extensive experiments demonstrate that HierDiff consistently improves the quality of molecule generation over existing methods.

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NeurIPS Conference 2023 Conference Paper

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Yuxuan Song
Jingjing Gong
Minkai Xu
Ziyao Cao
Yanyan Lan
Stefano Ermon
Hao Zhou
Wei-Ying Ma

The generation of 3D molecules requires simultaneously deciding the categorical features (atom types) and continuous features (atom coordinates). Deep generative models, especially Diffusion Models (DMs), have demonstrated effectiveness in generating feature-rich geometries. However, existing DMs typically suffer from unstable probability dynamics with inefficient sampling speed. In this paper, we introduce geometric flow matching, which enjoys the advantages of both equivariant modeling and stabilized probability dynamics. More specifically, we propose a hybrid probability path where the coordinates probability path is regularized by an equivariant optimal transport, and the information between different modalities is aligned. Experimentally, the proposed method could consistently achieve better performance on multiple molecule generation benchmarks with 4. 75$\times$ speed up of sampling on average.

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AAAI Conference 2019 Conference Paper

Long Short-Term Memory with Dynamic Skip Connections

Tao Gui
Qi Zhang
Lujun Zhao
Yaosong Lin
Minlong Peng
Jingjing Gong
Xuanjing Huang

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

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AAAI Conference 2019 Conference Paper

Switch-LSTMs for Multi-Criteria Chinese Word Segmentation

Jingjing Gong
Xinchi Chen
Tao Gui
Xipeng Qiu

Multi-criteria Chinese word segmentation is a promising but challenging task, which exploits several different segmentation criteria and mines their common underlying knowledge. In this paper, we propose a flexible multi-criteria learning for Chinese word segmentation. Usually, a segmentation criterion could be decomposed into multiple sub-criteria, which are shareable with other segmentation criteria. The process of word segmentation is a routing among these sub-criteria. From this perspective, we present Switch-LSTMs to segment words, which consist of several long short-term memory neural networks (LSTM), and a switcher to automatically switch the routing among these LSTMs. With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria. Experiments show that our model obtains significant improvements on eight corpora with heterogeneous segmentation criteria, compared to the previous method and single-criterion learning.

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