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Hongyi Chen

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6 papers

2 author rows

JBHI Journal 2026 Journal Article

APSevLM: Acute Pancreatitis Severity Language Model

Leqi Zheng
Jiajun Fang
Hongyi Chen
Naiqing Li
Yunyuan Huang
Qiulin Ge
Yang Gu
Tao Yu

Approximately one-fifth of patients with acute pancreatitis (AP) develop severe forms, which are associated with high mortality rates, making early prediction of severity crucial for effective patient management. In this study, we present APSevLM (Acute Pancreatitis Severity Language Model), a large language model (LLM)-based approach that integrates admission-time clinical data, imaging reports, and expert knowledge to predict AP severity at an early stage. Through a comprehensive evaluation using data from over five hundred patients, APSevLM outperforms traditional scoring systems (BISAP and MCTSI), conventional machine learning algorithms, and state-of-the-art deep learning models, achieving an AUC of 0. 857. Attention visualizations of the model explain complex mechanisms that dynamically weigh different information modalities based on case severity. Furthermore, a systematic feature importance analysis identifies key predictive factors, particularly hematological parameters and cardiac markers, offering valuable insights for clinical practice. Our study positions APSevLM as an accurate predictive model and highlights potential biomarkers for the early diagnosis of severe AP.

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TMLR Journal 2026 Journal Article

S$^2$Transformer: Scalable Structured Transformers for Global Station Weather Forecasting

Hongyi Chen
Xiucheng Li
Xinyang Chen
Yun Cheng
Jing Li
Kehai Chen
Liqiang Nie

Global Station Weather Forecasting (GSWF) is a key meteorological research area, critical to energy, aviation, and agriculture. Existing time series forecasting methods often ignore or unidirectionally model spatial correlation when conducting large-scale global station forecasting. This contradicts the intrinsic nature underlying observations of the global weather system, limiting forecast performance. To address this, we propose a novel Spatial Structured Attention Block in this paper. It partitions the spatial graph into a set of subgraphs and instantiates Intra-subgraph Attention to learn local spatial correlation within each subgraph, and aggregates nodes into subgraph representations for message passing among the subgraphs via Inter-subgraph Attention---considering both spatial proximity and global correlation. Building on this block, we develop a multiscale spatiotemporal forecasting model S$^2$Transformer by progressively expanding subgraph scales. The resulting model is both scalable and able to produce structured spatial correlation, and meanwhile, it is easy to implement. The experimental results show that it can achieve performance improvements up to 16.8% over time series forecasting baselines at low running costs.

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NeurIPS Conference 2025 Conference Paper

PID-controlled Langevin Dynamics for Faster Sampling of Generative Models

Hongyi Chen
Jianhai Shu
Jingtao Ding
Yong Li
Xiao-Ping (Steven) Zhang

Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introduce PID-controlled Langevin Dynamics (PIDLD), a novel sampling acceleration algorithm that reinterprets the sampling process using control-theoretic principles. By treating energy gradients as feedback signals, PIDLD combines historical gradients (the integral term) and gradient trends (the derivative term) to efficiently traverse energy landscapes and adaptively stabilize, thereby significantly reducing the number of iterations required to produce high-quality samples. Our approach requires no additional training, datasets, or prior information, making it immediately integrable with any Langevin-based method. Extensive experiments across image generation and reasoning tasks demonstrate that PIDLD achieves higher quality with fewer steps, making Langevin-based generative models more practical for efficiency-critical applications. The implementation can be found at \href{https: //github. com/tsinghua-fib-lab/PIDLD}{https: //github. com/tsinghua-fib-lab/PIDLD}.

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AAAI Conference 2024 Conference Paper

Social Physics Informed Diffusion Model for Crowd Simulation

Hongyi Chen
Jingtao Ding
Yong Li
Yue Wang
Xiao-Ping Zhang

Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simulation but fail to model the heterogeneity and multi-modality of human movement comprehensively. In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap. SPDiff takes both the interactive and historical information of crowds in the current timeframe to reverse the diffusion process, thereby generating the distribution of pedestrian movement in the subsequent timeframe. Inspired by the well-known social physics model, i.e., Social Force, regarding crowd dynamics, we design a crowd interaction encoder to guide the denoising process and further enhance this module with the equivariant properties of crowd interactions. To mitigate error accumulation in long-term simulations, we propose a multi-frame rollout training algorithm for diffusion modeling. Experiments conducted on two real-world datasets demonstrate the superior performance of SPDiff in terms of both macroscopic and microscopic evaluation metrics. Code and appendix are available at https://github.com/tsinghua-fib-lab/SPDiff.

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IROS Conference 2023 Conference Paper

KGNv2: Separating Scale and Pose Prediction for Keypoint-Based 6-DoF Grasp Synthesis on RGB-D Input

Yiye Chen
Ruinian Xu
Yunzhi Lin
Hongyi Chen
Patricio A. Vela

We propose an improved keypoint approach for 6-DoF grasp pose synthesis from RGB-D input. Keypoint-based grasp detection from image input demonstrated promising results in a previous study, where the visual information provided by color imagery compensates for noisy or imprecise depth measurements. However, it relies heavily on accurate keypoint prediction in image space. We devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, the network estimates both the grasp pose and the camera-grasp length scale. Re-design of the keypoint output space mitigates the impact of keypoint prediction noise on Perspective-n-Point (PnP) algorithm solutions. Experiments show that the proposed method outperforms the baseline by a large margin, validating its design. Though trained only on simple synthetic objects, our method demonstrates sim-to-real capacity through competitive results in real-world robot experiments.

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ICLR Conference 2023 Conference Paper

Planning with Sequence Models through Iterative Energy Minimization

Hongyi Chen
Yilun Du
Yiye Chen
Joshua B. Tenenbaum
Patricio A. Vela

Recent works have shown that language modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing language models to planning, in which we wish to obtain a trajectory of actions to reach some goal, is less straightforward. The typical autoregressive generation procedures of language models preclude sequential refinement of earlier steps, which limits the effectiveness of a predicted plan. In this paper, we suggest an approach towards integrating planning with language models based on the idea of iterative energy minimization, and illustrate how such a procedure leads to improved RL performance across different tasks. We train a masked language model to capture an implicit energy function over trajectories of actions, and formulate planning as finding a trajectory of actions with minimum energy. We illustrate how this procedure enables improved performance over recent approaches across BabyAI and Atari environments. We further demonstrate unique benefits of our iterative optimization procedure, involving new task generalization, test-time constraints adaptation, and the ability to compose plans together. Project webpage: https://hychen-naza.github.io/projects/LEAP/index.html

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